id	sid	tid	token	lemma	pos
fcis-28118	1	1	frontiers	frontier	NOUN
fcis-28118	1	2	in	in	ADP
fcis-28118	1	3	computing	computing	NOUN
fcis-28118	1	4	and	and	CCONJ
fcis-28118	1	5	intelligent	intelligent	ADJ
fcis-28118	1	6	systems	system	NOUN
fcis-28118	1	7	issn	issn	VERB
fcis-28118	1	8	:	:	PUNCT
fcis-28118	1	9	2832	2832	NUM
fcis-28118	1	10	-	-	SYM
fcis-28118	1	11	6024	6024	NUM
fcis-28118	1	12	|	|	NOUN
fcis-28118	1	13	vol	vol	NOUN
fcis-28118	1	14	.	.	PROPN
fcis-28118	2	1	10	10	NUM
fcis-28118	2	2	,	,	PUNCT
fcis-28118	2	3	no	no	INTJ
fcis-28118	2	4	.	.	NOUN
fcis-28118	2	5	3	3	NUM
fcis-28118	2	6	,	,	PUNCT
fcis-28118	2	7	2024	2024	NUM
fcis-28118	2	8	23	23	NUM
fcis-28118	2	9	prediction	prediction	NOUN
fcis-28118	2	10	of	of	ADP
fcis-28118	2	11	protein	protein	NOUN
fcis-28118	2	12	secondary	secondary	ADJ
fcis-28118	2	13	structure	structure	NOUN
fcis-28118	2	14	using	use	VERB
fcis-28118	2	15	a	a	DET
fcis-28118	2	16	hybrid	hybrid	ADJ
fcis-28118	2	17	convolutional	convolutional	ADJ
fcis-28118	2	18	blocks	block	NOUN
fcis-28118	2	19	with	with	ADP
fcis-28118	2	20	gru	gru	NOUN
fcis-28118	2	21	units	unit	NOUN
fcis-28118	2	22	shiwei	shiwei	PROPN
fcis-28118	2	23	yang	yang	PROPN
fcis-28118	2	24	,	,	PUNCT
fcis-28118	3	1	xiaozhou	xiaozhou	PROPN
fcis-28118	3	2	chen	chen	PROPN
fcis-28118	4	1	*	*	PUNCT
fcis-28118	4	2	school	school	PROPN
fcis-28118	4	3	of	of	ADP
fcis-28118	4	4	yunnan	yunnan	PROPN
fcis-28118	4	5	minzu	minzu	PROPN
fcis-28118	4	6	university	university	PROPN
fcis-28118	4	7	,	,	PUNCT
fcis-28118	4	8	mathematics	mathematic	NOUN
fcis-28118	4	9	and	and	CCONJ
fcis-28118	4	10	computer	computer	NOUN
fcis-28118	4	11	science	science	NOUN
fcis-28118	4	12	,	,	PUNCT
fcis-28118	4	13	kunming	kunming	PROPN
fcis-28118	4	14	,	,	PUNCT
fcis-28118	4	15	yunnan	yunnan	PROPN
fcis-28118	4	16	,	,	PUNCT
fcis-28118	4	17	china	china	PROPN
fcis-28118	4	18	*	*	PUNCT
fcis-28118	4	19	corresponding	correspond	VERB
fcis-28118	4	20	author	author	NOUN
fcis-28118	4	21	:	:	PUNCT
fcis-28118	5	1	xiaozhou	xiaozhou	PROPN
fcis-28118	5	2	chen	chen	PROPN
fcis-28118	5	3	(	(	PUNCT
fcis-28118	5	4	email	email	NOUN
fcis-28118	5	5	:	:	PUNCT
fcis-28118	5	6	chxiaozhou@163.com	chxiaozhou@163.com	ADJ
fcis-28118	5	7	)	)	PUNCT
fcis-28118	5	8	abstract	abstract	NOUN
fcis-28118	5	9	:	:	PUNCT
fcis-28118	5	10	in	in	ADP
fcis-28118	5	11	order	order	NOUN
fcis-28118	5	12	to	to	PART
fcis-28118	5	13	better	well	ADV
fcis-28118	5	14	understand	understand	VERB
fcis-28118	5	15	the	the	DET
fcis-28118	5	16	function	function	NOUN
fcis-28118	5	17	of	of	ADP
fcis-28118	5	18	proteins	protein	NOUN
fcis-28118	5	19	,	,	PUNCT
fcis-28118	5	20	protein	protein	NOUN
fcis-28118	5	21	research	research	NOUN
fcis-28118	5	22	in	in	ADP
fcis-28118	5	23	the	the	DET
fcis-28118	5	24	field	field	NOUN
fcis-28118	5	25	of	of	ADP
fcis-28118	5	26	bioinformatics	bioinformatics	NOUN
fcis-28118	5	27	has	have	AUX
fcis-28118	5	28	always	always	ADV
fcis-28118	5	29	been	be	AUX
fcis-28118	5	30	an	an	DET
fcis-28118	5	31	important	important	ADJ
fcis-28118	5	32	issue	issue	NOUN
fcis-28118	5	33	,	,	PUNCT
fcis-28118	5	34	and	and	CCONJ
fcis-28118	5	35	protein	protein	NOUN
fcis-28118	5	36	structure	structure	NOUN
fcis-28118	5	37	prediction	prediction	NOUN
fcis-28118	5	38	is	be	AUX
fcis-28118	5	39	also	also	ADV
fcis-28118	5	40	one	one	NUM
fcis-28118	5	41	of	of	ADP
fcis-28118	5	42	the	the	DET
fcis-28118	5	43	research	research	NOUN
fcis-28118	5	44	topics	topic	NOUN
fcis-28118	5	45	.	.	PUNCT
fcis-28118	6	1	starting	start	VERB
fcis-28118	6	2	from	from	ADP
fcis-28118	6	3	the	the	DET
fcis-28118	6	4	one	one	NUM
fcis-28118	6	5	-	-	PUNCT
fcis-28118	6	6	dimensional	dimensional	ADJ
fcis-28118	6	7	structure	structure	NOUN
fcis-28118	6	8	(	(	PUNCT
fcis-28118	6	9	amino	amino	NOUN
fcis-28118	6	10	acid	acid	NOUN
fcis-28118	6	11	sequence	sequence	NOUN
fcis-28118	6	12	)	)	PUNCT
fcis-28118	6	13	,	,	PUNCT
fcis-28118	6	14	it	it	PRON
fcis-28118	6	15	is	be	AUX
fcis-28118	6	16	a	a	DET
fcis-28118	6	17	good	good	ADJ
fcis-28118	6	18	method	method	NOUN
fcis-28118	6	19	to	to	PART
fcis-28118	6	20	predict	predict	VERB
fcis-28118	6	21	and	and	CCONJ
fcis-28118	6	22	classify	classify	VERB
fcis-28118	6	23	the	the	DET
fcis-28118	6	24	secondary	secondary	ADJ
fcis-28118	6	25	structure	structure	NOUN
fcis-28118	6	26	and	and	CCONJ
fcis-28118	6	27	adopt	adopt	VERB
fcis-28118	6	28	deep	deep	ADJ
fcis-28118	6	29	neural	neural	ADJ
fcis-28118	6	30	networks	network	NOUN
fcis-28118	6	31	to	to	PART
fcis-28118	6	32	solve	solve	VERB
fcis-28118	6	33	sequence	sequence	NOUN
fcis-28118	6	34	problems	problem	NOUN
fcis-28118	6	35	.	.	PUNCT
fcis-28118	7	1	we	we	PRON
fcis-28118	7	2	used	use	VERB
fcis-28118	7	3	convolutional	convolutional	ADJ
fcis-28118	7	4	blocks	block	NOUN
fcis-28118	7	5	combined	combine	VERB
fcis-28118	7	6	with	with	ADP
fcis-28118	7	7	gated	gate	VERB
fcis-28118	7	8	recurrent	recurrent	ADJ
fcis-28118	7	9	units	unit	NOUN
fcis-28118	7	10	(	(	PUNCT
fcis-28118	7	11	grus	grus	NOUN
fcis-28118	7	12	)	)	PUNCT
fcis-28118	7	13	to	to	PART
fcis-28118	7	14	predict	predict	VERB
fcis-28118	7	15	protein	protein	NOUN
fcis-28118	7	16	secondary	secondary	ADJ
fcis-28118	7	17	structure	structure	NOUN
fcis-28118	7	18	.	.	PUNCT
fcis-28118	8	1	different	different	ADJ
fcis-28118	8	2	from	from	ADP
fcis-28118	8	3	previous	previous	ADJ
fcis-28118	8	4	convolutional	convolutional	ADJ
fcis-28118	8	5	neural	neural	ADJ
fcis-28118	8	6	networks	network	NOUN
fcis-28118	8	7	architectures	architecture	NOUN
fcis-28118	8	8	,	,	PUNCT
fcis-28118	8	9	in	in	ADP
fcis-28118	8	10	this	this	DET
fcis-28118	8	11	study	study	NOUN
fcis-28118	8	12	,	,	PUNCT
fcis-28118	8	13	we	we	PRON
fcis-28118	8	14	used	use	VERB
fcis-28118	8	15	a	a	DET
fcis-28118	8	16	mixture	mixture	NOUN
fcis-28118	8	17	of	of	ADP
fcis-28118	8	18	two	two	NUM
fcis-28118	8	19	convolutional	convolutional	ADJ
fcis-28118	8	20	blocks	block	NOUN
fcis-28118	8	21	of	of	ADP
fcis-28118	8	22	different	different	ADJ
fcis-28118	8	23	scales	scale	NOUN
fcis-28118	8	24	combined	combine	VERB
fcis-28118	8	25	with	with	ADP
fcis-28118	8	26	grus	grus	NOUN
fcis-28118	8	27	for	for	ADP
fcis-28118	8	28	sequence	sequence	NOUN
fcis-28118	8	29	labeling	labeling	NOUN
fcis-28118	8	30	for	for	ADP
fcis-28118	8	31	protein	protein	NOUN
fcis-28118	8	32	secondary	secondary	ADJ
fcis-28118	8	33	structure	structure	NOUN
fcis-28118	8	34	prediction	prediction	NOUN
fcis-28118	8	35	.	.	PUNCT
fcis-28118	9	1	additionally	additionally	ADV
fcis-28118	9	2	,	,	PUNCT
fcis-28118	9	3	considering	consider	VERB
fcis-28118	9	4	that	that	SCONJ
fcis-28118	9	5	the	the	DET
fcis-28118	9	6	coding	code	VERB
fcis-28118	9	7	format	format	NOUN
fcis-28118	9	8	in	in	ADP
fcis-28118	9	9	previous	previous	ADJ
fcis-28118	9	10	studies	study	NOUN
fcis-28118	9	11	was	be	AUX
fcis-28118	9	12	too	too	ADV
fcis-28118	9	13	simple	simple	ADJ
fcis-28118	9	14	,	,	PUNCT
fcis-28118	9	15	this	this	DET
fcis-28118	9	16	experiment	experiment	NOUN
fcis-28118	9	17	also	also	ADV
fcis-28118	9	18	added	add	VERB
fcis-28118	9	19	the	the	DET
fcis-28118	9	20	physical	physical	ADJ
fcis-28118	9	21	and	and	CCONJ
fcis-28118	9	22	chemical	chemical	NOUN
fcis-28118	9	23	properties	property	NOUN
fcis-28118	9	24	and	and	CCONJ
fcis-28118	9	25	the	the	DET
fcis-28118	9	26	logarithmic	logarithmic	ADJ
fcis-28118	9	27	relative	relative	ADJ
fcis-28118	9	28	probability	probability	NOUN
fcis-28118	9	29	of	of	ADP
fcis-28118	9	30	8	8	NUM
fcis-28118	9	31	types	type	NOUN
fcis-28118	9	32	of	of	ADP
fcis-28118	9	33	secondary	secondary	ADJ
fcis-28118	9	34	structure	structure	NOUN
fcis-28118	9	35	amino	amino	NOUN
fcis-28118	9	36	acids	acid	NOUN
fcis-28118	9	37	as	as	ADP
fcis-28118	9	38	the	the	DET
fcis-28118	9	39	feature	feature	NOUN
fcis-28118	9	40	input	input	NOUN
fcis-28118	9	41	,	,	PUNCT
fcis-28118	9	42	which	which	PRON
fcis-28118	9	43	improved	improve	VERB
fcis-28118	9	44	the	the	DET
fcis-28118	9	45	accuracy	accuracy	NOUN
fcis-28118	9	46	after	after	ADP
fcis-28118	9	47	the	the	DET
fcis-28118	9	48	addition	addition	NOUN
fcis-28118	9	49	of	of	ADP
fcis-28118	9	50	features	feature	NOUN
fcis-28118	9	51	.	.	PUNCT
fcis-28118	10	1	experiments	experiment	NOUN
fcis-28118	10	2	show	show	VERB
fcis-28118	10	3	that	that	SCONJ
fcis-28118	10	4	our	our	PRON
fcis-28118	10	5	model	model	NOUN
fcis-28118	10	6	has	have	VERB
fcis-28118	10	7	good	good	ADJ
fcis-28118	10	8	performance	performance	NOUN
fcis-28118	10	9	in	in	ADP
fcis-28118	10	10	q8	q8	PROPN
fcis-28118	10	11	accuracy	accuracy	NOUN
fcis-28118	10	12	.	.	PUNCT
fcis-28118	11	1	keywords	keyword	NOUN
fcis-28118	11	2	:	:	PUNCT
fcis-28118	11	3	convolutional	convolutional	ADJ
fcis-28118	11	4	block	block	NOUN
fcis-28118	11	5	;	;	PUNCT
fcis-28118	11	6	grus	grus	NOUN
fcis-28118	11	7	;	;	PUNCT
fcis-28118	11	8	secondary	secondary	ADJ
fcis-28118	11	9	structure	structure	NOUN
fcis-28118	11	10	.	.	PUNCT
fcis-28118	12	1	1	1	X
fcis-28118	12	2	.	.	X
fcis-28118	12	3	introduction	introduction	NOUN
fcis-28118	12	4	proteins	protein	NOUN
fcis-28118	12	5	are	be	AUX
fcis-28118	12	6	the	the	DET
fcis-28118	12	7	material	material	ADJ
fcis-28118	12	8	basis	basis	NOUN
fcis-28118	12	9	of	of	ADP
fcis-28118	12	10	essential	essential	ADJ
fcis-28118	12	11	life	life	NOUN
fcis-28118	12	12	activities	activity	NOUN
fcis-28118	12	13	and	and	CCONJ
fcis-28118	12	14	the	the	DET
fcis-28118	12	15	main	main	ADJ
fcis-28118	12	16	bearers	bearer	NOUN
fcis-28118	12	17	of	of	ADP
fcis-28118	12	18	life	life	NOUN
fcis-28118	12	19	activities	activity	NOUN
fcis-28118	12	20	.	.	PUNCT
fcis-28118	13	1	they	they	PRON
fcis-28118	13	2	can	can	AUX
fcis-28118	13	3	regulate	regulate	VERB
fcis-28118	13	4	antibodies	antibody	NOUN
fcis-28118	13	5	,	,	PUNCT
fcis-28118	13	6	cell	cell	NOUN
fcis-28118	13	7	structural	structural	ADJ
fcis-28118	13	8	elements	element	NOUN
fcis-28118	13	9	,	,	PUNCT
fcis-28118	13	10	motor	motor	NOUN
fcis-28118	13	11	elements	element	NOUN
fcis-28118	13	12	,	,	PUNCT
fcis-28118	13	13	promote	promote	VERB
fcis-28118	13	14	chemical	chemical	ADJ
fcis-28118	13	15	reactions	reaction	NOUN
fcis-28118	13	16	,	,	PUNCT
fcis-28118	13	17	and	and	CCONJ
fcis-28118	13	18	regulate	regulate	VERB
fcis-28118	13	19	cell	cell	NOUN
fcis-28118	13	20	activity	activity	NOUN
fcis-28118	13	21	.	.	PUNCT
fcis-28118	14	1	as	as	SCONJ
fcis-28118	14	2	we	we	PRON
fcis-28118	14	3	all	all	PRON
fcis-28118	14	4	know	know	VERB
fcis-28118	14	5	,	,	PUNCT
fcis-28118	14	6	there	there	PRON
fcis-28118	14	7	is	be	VERB
fcis-28118	14	8	a	a	DET
fcis-28118	14	9	complex	complex	ADJ
fcis-28118	14	10	relationship	relationship	NOUN
fcis-28118	14	11	between	between	ADP
fcis-28118	14	12	protein	protein	NOUN
fcis-28118	14	13	function	function	NOUN
fcis-28118	14	14	and	and	CCONJ
fcis-28118	14	15	its	its	PRON
fcis-28118	14	16	structure	structure	NOUN
fcis-28118	14	17	;	;	PUNCT
fcis-28118	14	18	it	it	PRON
fcis-28118	14	19	can	can	AUX
fcis-28118	14	20	be	be	AUX
fcis-28118	14	21	said	say	VERB
fcis-28118	14	22	that	that	SCONJ
fcis-28118	14	23	the	the	DET
fcis-28118	14	24	structure	structure	NOUN
fcis-28118	14	25	a	a	DET
fcis-28118	14	26	protein	protein	NOUN
fcis-28118	14	27	presents	present	VERB
fcis-28118	14	28	determines	determine	VERB
fcis-28118	14	29	the	the	DET
fcis-28118	14	30	function	function	NOUN
fcis-28118	14	31	it	it	PRON
fcis-28118	14	32	achieve[1	achieve[1	ADP
fcis-28118	14	33	]	]	PUNCT
fcis-28118	14	34	.	.	PUNCT
fcis-28118	15	1	therefore	therefore	ADV
fcis-28118	15	2	,	,	PUNCT
fcis-28118	15	3	in	in	ADP
fcis-28118	15	4	proteomics	proteomic	NOUN
fcis-28118	15	5	,	,	PUNCT
fcis-28118	15	6	how	how	SCONJ
fcis-28118	15	7	to	to	PART
fcis-28118	15	8	make	make	VERB
fcis-28118	15	9	the	the	DET
fcis-28118	15	10	prediction	prediction	NOUN
fcis-28118	15	11	of	of	ADP
fcis-28118	15	12	protein	protein	NOUN
fcis-28118	15	13	secondary	secondary	ADJ
fcis-28118	15	14	structure	structure	NOUN
fcis-28118	15	15	more	more	ADV
fcis-28118	15	16	efficient	efficient	ADJ
fcis-28118	15	17	and	and	CCONJ
fcis-28118	15	18	highly	highly	ADV
fcis-28118	15	19	precise	precise	ADJ
fcis-28118	15	20	has	have	AUX
fcis-28118	15	21	become	become	VERB
fcis-28118	15	22	a	a	DET
fcis-28118	15	23	hot	hot	ADJ
fcis-28118	15	24	topic[2	topic[2	NOUN
fcis-28118	15	25	]	]	PUNCT
fcis-28118	15	26	.	.	PUNCT
fcis-28118	16	1	in	in	ADP
fcis-28118	16	2	the	the	DET
fcis-28118	16	3	past	past	NOUN
fcis-28118	16	4	,	,	PUNCT
fcis-28118	16	5	researchers	researcher	NOUN
fcis-28118	16	6	usually	usually	ADV
fcis-28118	16	7	used	use	VERB
fcis-28118	16	8	x	x	ADJ
fcis-28118	16	9	-	-	NOUN
fcis-28118	16	10	ray	ray	NOUN
fcis-28118	16	11	crystallography	crystallography	NOUN
fcis-28118	16	12	,	,	PUNCT
fcis-28118	16	13	nuclear	nuclear	ADJ
fcis-28118	16	14	magnetic	magnetic	ADJ
fcis-28118	16	15	resonance	resonance	NOUN
fcis-28118	16	16	spectroscopy	spectroscopy	NOUN
fcis-28118	16	17	,	,	PUNCT
fcis-28118	16	18	and	and	CCONJ
fcis-28118	16	19	frozen	frozen	ADJ
fcis-28118	16	20	electron	electron	NOUN
fcis-28118	16	21	microscopy	microscopy	NOUN
fcis-28118	16	22	experiments	experiment	NOUN
fcis-28118	16	23	to	to	PART
fcis-28118	16	24	predict	predict	VERB
fcis-28118	16	25	the	the	DET
fcis-28118	16	26	secondary	secondary	ADJ
fcis-28118	16	27	structure	structure	NOUN
fcis-28118	16	28	of	of	ADP
fcis-28118	16	29	proteins	protein	NOUN
fcis-28118	16	30	.	.	PUNCT
fcis-28118	17	1	however	however	ADV
fcis-28118	17	2	,	,	PUNCT
fcis-28118	17	3	these	these	DET
fcis-28118	17	4	experiments	experiment	NOUN
fcis-28118	17	5	are	be	AUX
fcis-28118	17	6	rigorous	rigorous	ADJ
fcis-28118	17	7	and	and	CCONJ
fcis-28118	17	8	expensive	expensive	ADJ
fcis-28118	17	9	,	,	PUNCT
fcis-28118	17	10	and	and	CCONJ
fcis-28118	17	11	can	can	AUX
fcis-28118	17	12	be	be	AUX
fcis-28118	17	13	interfered	interfere	VERB
fcis-28118	17	14	with	with	ADP
fcis-28118	17	15	by	by	ADP
fcis-28118	17	16	various	various	ADJ
fcis-28118	17	17	factors	factor	NOUN
fcis-28118	17	18	,	,	PUNCT
fcis-28118	17	19	especially	especially	ADV
fcis-28118	17	20	when	when	SCONJ
fcis-28118	17	21	the	the	DET
fcis-28118	17	22	amino	amino	NOUN
fcis-28118	17	23	acid	acid	NOUN
fcis-28118	17	24	fragments	fragment	NOUN
fcis-28118	17	25	of	of	ADP
fcis-28118	17	26	some	some	DET
fcis-28118	17	27	proteins	protein	NOUN
fcis-28118	17	28	can	can	AUX
fcis-28118	17	29	not	not	PART
fcis-28118	17	30	pass	pass	VERB
fcis-28118	17	31	certain	certain	ADJ
fcis-28118	17	32	parts	part	NOUN
fcis-28118	17	33	of	of	ADP
fcis-28118	17	34	the	the	DET
fcis-28118	17	35	experiment	experiment	NOUN
fcis-28118	17	36	,	,	PUNCT
fcis-28118	17	37	usually	usually	ADV
fcis-28118	17	38	resulting	result	VERB
fcis-28118	17	39	in	in	ADP
fcis-28118	17	40	less	less	ADJ
fcis-28118	17	41	-	-	PUNCT
fcis-28118	17	42	than	than	ADP
fcis-28118	17	43	-	-	PUNCT
fcis-28118	17	44	ideal	ideal	NOUN
fcis-28118	17	45	result[3	result[3	NOUN
fcis-28118	17	46	]	]	X
fcis-28118	17	47	.	.	PUNCT
fcis-28118	18	1	however	however	ADV
fcis-28118	18	2	,	,	PUNCT
fcis-28118	18	3	with	with	ADP
fcis-28118	18	4	the	the	DET
fcis-28118	18	5	continuous	continuous	ADJ
fcis-28118	18	6	development	development	NOUN
fcis-28118	18	7	and	and	CCONJ
fcis-28118	18	8	advancement	advancement	NOUN
fcis-28118	18	9	of	of	ADP
fcis-28118	18	10	deep	deep	ADJ
fcis-28118	18	11	learning	learning	NOUN
fcis-28118	18	12	and	and	CCONJ
fcis-28118	18	13	artificial	artificial	ADJ
fcis-28118	18	14	intelligence	intelligence	NOUN
fcis-28118	18	15	,	,	PUNCT
fcis-28118	18	16	computer	computer	NOUN
fcis-28118	18	17	science	science	NOUN
fcis-28118	18	18	has	have	AUX
fcis-28118	18	19	intermingled	intermingle	VERB
fcis-28118	18	20	,	,	PUNCT
fcis-28118	18	21	attracting	attract	VERB
fcis-28118	18	22	a	a	DET
fcis-28118	18	23	large	large	ADJ
fcis-28118	18	24	number	number	NOUN
fcis-28118	18	25	of	of	ADP
fcis-28118	18	26	researchers	researcher	NOUN
fcis-28118	18	27	to	to	PART
fcis-28118	18	28	use	use	VERB
fcis-28118	18	29	deep	deep	ADJ
fcis-28118	18	30	learning	learning	NOUN
fcis-28118	18	31	methods	method	NOUN
fcis-28118	18	32	to	to	PART
fcis-28118	18	33	study	study	VERB
fcis-28118	18	34	the	the	DET
fcis-28118	18	35	relationship	relationship	NOUN
fcis-28118	18	36	between	between	ADP
fcis-28118	18	37	protein	protein	NOUN
fcis-28118	18	38	sequences	sequence	NOUN
fcis-28118	18	39	and	and	CCONJ
fcis-28118	18	40	their	their	PRON
fcis-28118	18	41	structures[4	structures[4	NOUN
fcis-28118	18	42	]	]	PUNCT
fcis-28118	18	43	.	.	PUNCT
fcis-28118	19	1	during	during	ADP
fcis-28118	19	2	the	the	DET
fcis-28118	19	3	process	process	NOUN
fcis-28118	19	4	of	of	ADP
fcis-28118	19	5	predicting	predict	VERB
fcis-28118	19	6	protein	protein	NOUN
fcis-28118	19	7	structures	structure	NOUN
fcis-28118	19	8	,	,	PUNCT
fcis-28118	19	9	it	it	PRON
fcis-28118	19	10	is	be	AUX
fcis-28118	19	11	possible	possible	ADJ
fcis-28118	19	12	to	to	PART
fcis-28118	19	13	predict	predict	VERB
fcis-28118	19	14	the	the	DET
fcis-28118	19	15	secondary	secondary	ADJ
fcis-28118	19	16	structure	structure	NOUN
fcis-28118	19	17	sequence	sequence	NOUN
fcis-28118	19	18	of	of	ADP
fcis-28118	19	19	each	each	DET
fcis-28118	19	20	position	position	NOUN
fcis-28118	19	21	in	in	ADP
fcis-28118	19	22	the	the	DET
fcis-28118	19	23	protein	protein	NOUN
fcis-28118	19	24	's	's	PART
fcis-28118	19	25	primary	primary	ADJ
fcis-28118	19	26	structure	structure	NOUN
fcis-28118	19	27	.	.	PUNCT
fcis-28118	20	1	in	in	ADP
fcis-28118	20	2	protein	protein	NOUN
fcis-28118	20	3	secondary	secondary	ADJ
fcis-28118	20	4	structure	structure	NOUN
fcis-28118	20	5	prediction	prediction	NOUN
fcis-28118	20	6	tasks	task	NOUN
fcis-28118	20	7	,	,	PUNCT
fcis-28118	20	8	there	there	PRON
fcis-28118	20	9	are	be	VERB
fcis-28118	20	10	typically	typically	ADV
fcis-28118	20	11	two	two	NUM
fcis-28118	20	12	types	type	NOUN
fcis-28118	20	13	of	of	ADP
fcis-28118	20	14	predictions	prediction	NOUN
fcis-28118	20	15	:	:	PUNCT
fcis-28118	20	16	eight	eight	NUM
fcis-28118	20	17	-	-	PUNCT
fcis-28118	20	18	class	class	NOUN
fcis-28118	20	19	prediction	prediction	NOUN
fcis-28118	20	20	(	(	PUNCT
fcis-28118	20	21	q8	q8	PROPN
fcis-28118	20	22	)	)	PUNCT
fcis-28118	20	23	and	and	CCONJ
fcis-28118	20	24	threeclass	threeclass	NOUN
fcis-28118	20	25	prediction	prediction	NOUN
fcis-28118	20	26	(	(	PUNCT
fcis-28118	20	27	q3	q3	PROPN
fcis-28118	20	28	)	)	PUNCT
fcis-28118	20	29	.	.	PUNCT
fcis-28118	21	1	compared	compare	VERB
fcis-28118	21	2	to	to	ADP
fcis-28118	21	3	q3	q3	PROPN
fcis-28118	21	4	,	,	PUNCT
fcis-28118	21	5	q8	q8	PROPN
fcis-28118	21	6	can	can	AUX
fcis-28118	21	7	reveal	reveal	VERB
fcis-28118	21	8	more	more	ADV
fcis-28118	21	9	detailed	detailed	ADJ
fcis-28118	21	10	information	information	NOUN
fcis-28118	21	11	about	about	ADP
fcis-28118	21	12	protein	protein	NOUN
fcis-28118	21	13	structure	structure	NOUN
fcis-28118	21	14	.	.	PUNCT
fcis-28118	22	1	therefore	therefore	ADV
fcis-28118	22	2	,	,	PUNCT
fcis-28118	22	3	this	this	DET
fcis-28118	22	4	article	article	NOUN
fcis-28118	22	5	focuses	focus	VERB
fcis-28118	22	6	on	on	ADP
fcis-28118	22	7	the	the	DET
fcis-28118	22	8	q8	q8	PROPN
fcis-28118	22	9	prediction	prediction	NOUN
fcis-28118	22	10	of	of	ADP
fcis-28118	22	11	protein	protein	NOUN
fcis-28118	22	12	secondary	secondary	ADJ
fcis-28118	22	13	structure[5	structure[5	PROPN
fcis-28118	22	14	]	]	X
fcis-28118	22	15	.	.	PUNCT
fcis-28118	23	1	in	in	ADP
fcis-28118	23	2	the	the	DET
fcis-28118	23	3	study	study	NOUN
fcis-28118	23	4	of	of	ADP
fcis-28118	23	5	protein	protein	NOUN
fcis-28118	23	6	secondary	secondary	ADJ
fcis-28118	23	7	structure	structure	NOUN
fcis-28118	23	8	prediction	prediction	NOUN
fcis-28118	23	9	,	,	PUNCT
fcis-28118	23	10	a	a	DET
fcis-28118	23	11	large	large	ADJ
fcis-28118	23	12	number	number	NOUN
fcis-28118	23	13	of	of	ADP
fcis-28118	23	14	researchers	researcher	NOUN
fcis-28118	23	15	use	use	VERB
fcis-28118	23	16	computers	computer	NOUN
fcis-28118	23	17	to	to	PART
fcis-28118	23	18	build	build	VERB
fcis-28118	23	19	deep	deep	ADJ
fcis-28118	23	20	learning	learning	NOUN
fcis-28118	23	21	models	model	NOUN
fcis-28118	23	22	.	.	PUNCT
fcis-28118	24	1	in	in	ADP
fcis-28118	24	2	the	the	DET
fcis-28118	24	3	research	research	NOUN
fcis-28118	24	4	process	process	NOUN
fcis-28118	24	5	,	,	PUNCT
fcis-28118	24	6	the	the	DET
fcis-28118	24	7	task	task	NOUN
fcis-28118	24	8	of	of	ADP
fcis-28118	24	9	predicting	predict	VERB
fcis-28118	24	10	positions	position	NOUN
fcis-28118	24	11	from	from	ADP
fcis-28118	24	12	the	the	DET
fcis-28118	24	13	primary	primary	ADJ
fcis-28118	24	14	structure	structure	NOUN
fcis-28118	24	15	sequence	sequence	NOUN
fcis-28118	24	16	and	and	CCONJ
fcis-28118	24	17	generating	generate	VERB
fcis-28118	24	18	new	new	ADJ
fcis-28118	24	19	sequences	sequence	NOUN
fcis-28118	24	20	is	be	AUX
fcis-28118	24	21	referred	refer	VERB
fcis-28118	24	22	to	to	ADP
fcis-28118	24	23	as	as	ADP
fcis-28118	24	24	sequence	sequence	NOUN
fcis-28118	24	25	labeling	labeling	NOUN
fcis-28118	24	26	.	.	PUNCT
fcis-28118	25	1	with	with	ADP
fcis-28118	25	2	the	the	DET
fcis-28118	25	3	continuous	continuous	ADJ
fcis-28118	25	4	development	development	NOUN
fcis-28118	25	5	of	of	ADP
fcis-28118	25	6	research	research	NOUN
fcis-28118	25	7	,	,	PUNCT
fcis-28118	25	8	various	various	ADJ
fcis-28118	25	9	machine	machine	NOUN
fcis-28118	25	10	learning	learn	VERB
fcis-28118	25	11	model	model	NOUN
fcis-28118	25	12	methods	method	NOUN
fcis-28118	25	13	such	such	ADJ
fcis-28118	25	14	as	as	ADP
fcis-28118	25	15	support	support	NOUN
fcis-28118	25	16	vector	vector	NOUN
fcis-28118	25	17	machine	machine	NOUN
fcis-28118	25	18	[	[	X
fcis-28118	25	19	6	6	NUM
fcis-28118	25	20	]	]	PUNCT
fcis-28118	25	21	,	,	PUNCT
fcis-28118	25	22	recurrent	recurrent	ADJ
fcis-28118	25	23	neural	neural	ADJ
fcis-28118	25	24	networks[7	networks[7	NOUN
fcis-28118	25	25	]	]	PUNCT
fcis-28118	25	26	,	,	PUNCT
fcis-28118	25	27	and	and	CCONJ
fcis-28118	25	28	convolutional	convolutional	ADJ
fcis-28118	25	29	neural	neural	ADJ
fcis-28118	25	30	networks	network	NOUN
fcis-28118	25	31	(	(	PUNCT
fcis-28118	25	32	cnns)[8	cnns)[8	X
fcis-28118	25	33	]	]	PUNCT
fcis-28118	25	34	have	have	AUX
fcis-28118	25	35	been	be	AUX
fcis-28118	25	36	widely	widely	ADV
fcis-28118	25	37	used	use	VERB
fcis-28118	25	38	in	in	ADP
fcis-28118	25	39	various	various	ADJ
fcis-28118	25	40	prediction	prediction	NOUN
fcis-28118	25	41	experiments	experiment	NOUN
fcis-28118	25	42	of	of	ADP
fcis-28118	25	43	protein	protein	NOUN
fcis-28118	25	44	structure	structure	NOUN
fcis-28118	25	45	.	.	PUNCT
fcis-28118	26	1	among	among	ADP
fcis-28118	26	2	these	these	PRON
fcis-28118	26	3	,	,	PUNCT
fcis-28118	26	4	convolutional	convolutional	ADJ
fcis-28118	26	5	neural	neural	ADJ
fcis-28118	26	6	networks	network	NOUN
fcis-28118	26	7	and	and	CCONJ
fcis-28118	26	8	recurrent	recurrent	ADJ
fcis-28118	26	9	neural	neural	ADJ
fcis-28118	26	10	networks	network	NOUN
fcis-28118	26	11	are	be	AUX
fcis-28118	26	12	favored	favor	VERB
fcis-28118	26	13	for	for	ADP
fcis-28118	26	14	their	their	PRON
fcis-28118	26	15	excellent	excellent	ADJ
fcis-28118	26	16	performance	performance	NOUN
fcis-28118	26	17	on	on	ADP
fcis-28118	26	18	sequence	sequence	NOUN
fcis-28118	26	19	data	datum	NOUN
fcis-28118	26	20	.	.	PUNCT
fcis-28118	27	1	as	as	ADP
fcis-28118	27	2	a	a	DET
fcis-28118	27	3	special	special	ADJ
fcis-28118	27	4	kind	kind	NOUN
fcis-28118	27	5	of	of	ADP
fcis-28118	27	6	sequence	sequence	NOUN
fcis-28118	27	7	data	datum	NOUN
fcis-28118	27	8	,	,	PUNCT
fcis-28118	27	9	protein	protein	NOUN
fcis-28118	27	10	amino	amino	NOUN
fcis-28118	27	11	acid	acid	NOUN
fcis-28118	27	12	sequences	sequence	NOUN
fcis-28118	27	13	can	can	AUX
fcis-28118	27	14	be	be	AUX
fcis-28118	27	15	utilized	utilize	VERB
fcis-28118	27	16	to	to	PART
fcis-28118	27	17	extract	extract	VERB
fcis-28118	27	18	local	local	ADJ
fcis-28118	27	19	context	context	NOUN
fcis-28118	27	20	features	feature	NOUN
fcis-28118	27	21	by	by	ADP
fcis-28118	27	22	convolutional	convolutional	ADJ
fcis-28118	27	23	neural	neural	ADJ
fcis-28118	27	24	networks	network	NOUN
fcis-28118	27	25	.	.	PUNCT
fcis-28118	28	1	studies	study	NOUN
fcis-28118	28	2	in	in	ADP
fcis-28118	28	3	recent	recent	ADJ
fcis-28118	28	4	years	year	NOUN
fcis-28118	28	5	have	have	AUX
fcis-28118	28	6	shown	show	VERB
fcis-28118	28	7	that	that	SCONJ
fcis-28118	28	8	this	this	DET
fcis-28118	28	9	approach	approach	NOUN
fcis-28118	28	10	has	have	AUX
fcis-28118	28	11	achieved	achieve	VERB
fcis-28118	28	12	good	good	ADJ
fcis-28118	28	13	results	result	NOUN
fcis-28118	28	14	in	in	ADP
fcis-28118	28	15	protein	protein	NOUN
fcis-28118	28	16	secondary	secondary	ADJ
fcis-28118	28	17	structure	structure	NOUN
fcis-28118	28	18	prediction	prediction	NOUN
fcis-28118	28	19	.	.	PUNCT
fcis-28118	29	1	however	however	ADV
fcis-28118	29	2	,	,	PUNCT
fcis-28118	29	3	convolutional	convolutional	ADJ
fcis-28118	29	4	neural	neural	ADJ
fcis-28118	29	5	networks	network	NOUN
fcis-28118	29	6	mainly	mainly	ADV
fcis-28118	29	7	focus	focus	VERB
fcis-28118	29	8	on	on	ADP
fcis-28118	29	9	local	local	ADJ
fcis-28118	29	10	spatial	spatial	ADJ
fcis-28118	29	11	structures	structure	NOUN
fcis-28118	29	12	while	while	SCONJ
fcis-28118	29	13	ignoring	ignore	VERB
fcis-28118	29	14	the	the	DET
fcis-28118	29	15	temporal	temporal	ADJ
fcis-28118	29	16	order	order	NOUN
fcis-28118	29	17	dimension	dimension	NOUN
fcis-28118	29	18	.	.	PUNCT
fcis-28118	30	1	therefore	therefore	ADV
fcis-28118	30	2	,	,	PUNCT
fcis-28118	30	3	recurrent	recurrent	ADJ
fcis-28118	30	4	neural	neural	ADJ
fcis-28118	30	5	networks	network	NOUN
fcis-28118	30	6	are	be	AUX
fcis-28118	30	7	introduced	introduce	VERB
fcis-28118	30	8	in	in	ADP
fcis-28118	30	9	this	this	DET
fcis-28118	30	10	paper	paper	NOUN
fcis-28118	30	11	.	.	PUNCT
fcis-28118	31	1	by	by	ADP
fcis-28118	31	2	incorporating	incorporate	VERB
fcis-28118	31	3	cyclic	cyclic	ADJ
fcis-28118	31	4	connections	connection	NOUN
fcis-28118	31	5	,	,	PUNCT
fcis-28118	31	6	sequential	sequential	ADJ
fcis-28118	31	7	information	information	NOUN
fcis-28118	31	8	can	can	AUX
fcis-28118	31	9	be	be	AUX
fcis-28118	31	10	captured	capture	VERB
fcis-28118	31	11	and	and	CCONJ
fcis-28118	31	12	processed	process	VERB
fcis-28118	31	13	,	,	PUNCT
fcis-28118	31	14	enhancing	enhance	VERB
fcis-28118	31	15	the	the	DET
fcis-28118	31	16	network	network	NOUN
fcis-28118	31	17	's	's	PART
fcis-28118	31	18	ability	ability	NOUN
fcis-28118	31	19	to	to	PART
fcis-28118	31	20	handle	handle	VERB
fcis-28118	31	21	sequence	sequence	NOUN
fcis-28118	31	22	data	datum	NOUN
fcis-28118	31	23	.	.	PUNCT
fcis-28118	32	1	in	in	ADP
fcis-28118	32	2	this	this	DET
fcis-28118	32	3	study	study	NOUN
fcis-28118	32	4	,	,	PUNCT
fcis-28118	32	5	both	both	CCONJ
fcis-28118	32	6	convolutional	convolutional	ADJ
fcis-28118	32	7	blocks	block	NOUN
fcis-28118	32	8	from	from	ADP
fcis-28118	32	9	convolutional	convolutional	ADJ
fcis-28118	32	10	neural	neural	ADJ
fcis-28118	32	11	networks	network	NOUN
fcis-28118	32	12	and	and	CCONJ
fcis-28118	32	13	grus[9	grus[9	PROPN
fcis-28118	32	14	]	]	X
fcis-28118	32	15	,	,	PUNCT
fcis-28118	32	16	a	a	DET
fcis-28118	32	17	variant	variant	NOUN
fcis-28118	32	18	of	of	ADP
fcis-28118	32	19	recurrent	recurrent	ADJ
fcis-28118	32	20	neural	neural	ADJ
fcis-28118	32	21	networks	network	NOUN
fcis-28118	32	22	,	,	PUNCT
fcis-28118	32	23	are	be	AUX
fcis-28118	32	24	employed	employ	VERB
fcis-28118	32	25	to	to	PART
fcis-28118	32	26	predict	predict	VERB
fcis-28118	32	27	the	the	DET
fcis-28118	32	28	secondary	secondary	ADJ
fcis-28118	32	29	structure	structure	NOUN
fcis-28118	32	30	of	of	ADP
fcis-28118	32	31	proteins	protein	NOUN
fcis-28118	32	32	.	.	PUNCT
fcis-28118	33	1	2	2	X
fcis-28118	33	2	.	.	X
fcis-28118	33	3	secondary	secondary	ADJ
fcis-28118	33	4	protein	protein	NOUN
fcis-28118	33	5	structure	structure	NOUN
fcis-28118	33	6	the	the	DET
fcis-28118	33	7	prediction	prediction	NOUN
fcis-28118	33	8	of	of	ADP
fcis-28118	33	9	protein	protein	NOUN
fcis-28118	33	10	secondary	secondary	ADJ
fcis-28118	33	11	structure	structure	NOUN
fcis-28118	33	12	usually	usually	ADV
fcis-28118	33	13	begins	begin	VERB
fcis-28118	33	14	with	with	ADP
fcis-28118	33	15	the	the	DET
fcis-28118	33	16	primary	primary	ADJ
fcis-28118	33	17	form	form	NOUN
fcis-28118	33	18	of	of	ADP
fcis-28118	33	19	the	the	DET
fcis-28118	33	20	protein	protein	NOUN
fcis-28118	33	21	,	,	PUNCT
fcis-28118	33	22	known	know	VERB
fcis-28118	33	23	as	as	ADP
fcis-28118	33	24	the	the	DET
fcis-28118	33	25	primary	primary	ADJ
fcis-28118	33	26	structure	structure	NOUN
fcis-28118	33	27	,	,	PUNCT
fcis-28118	33	28	which	which	PRON
fcis-28118	33	29	provides	provide	VERB
fcis-28118	33	30	the	the	DET
fcis-28118	33	31	amino	amino	NOUN
fcis-28118	33	32	acid	acid	NOUN
fcis-28118	33	33	composition	composition	NOUN
fcis-28118	33	34	for	for	ADP
fcis-28118	33	35	the	the	DET
fcis-28118	33	36	protein	protein	NOUN
fcis-28118	33	37	.	.	PUNCT
fcis-28118	34	1	the	the	DET
fcis-28118	34	2	secondary	secondary	ADJ
fcis-28118	34	3	structure	structure	NOUN
fcis-28118	34	4	can	can	AUX
fcis-28118	34	5	also	also	ADV
fcis-28118	34	6	be	be	AUX
fcis-28118	34	7	briefly	briefly	ADV
fcis-28118	34	8	described	describe	VERB
fcis-28118	34	9	as	as	ADP
fcis-28118	34	10	the	the	DET
fcis-28118	34	11	relative	relative	ADJ
fcis-28118	34	12	spatial	spatial	ADJ
fcis-28118	34	13	arrangement	arrangement	NOUN
fcis-28118	34	14	of	of	ADP
fcis-28118	34	15	amino	amino	ADJ
fcis-28118	34	16	acids	acid	NOUN
fcis-28118	34	17	,	,	PUNCT
fcis-28118	34	18	where	where	SCONJ
fcis-28118	34	19	20	20	NUM
fcis-28118	34	20	amino	amino	NOUN
fcis-28118	34	21	acids	acid	NOUN
fcis-28118	34	22	are	be	AUX
fcis-28118	34	23	connected	connect	VERB
fcis-28118	34	24	together	together	ADV
fcis-28118	34	25	in	in	ADP
fcis-28118	34	26	a	a	DET
fcis-28118	34	27	specific	specific	ADJ
fcis-28118	34	28	order	order	NOUN
fcis-28118	34	29	to	to	PART
fcis-28118	34	30	form	form	VERB
fcis-28118	34	31	a	a	DET
fcis-28118	34	32	protein	protein	NOUN
fcis-28118	34	33	chain	chain	NOUN
fcis-28118	34	34	.	.	PUNCT
fcis-28118	35	1	these	these	DET
fcis-28118	35	2	protein	protein	NOUN
fcis-28118	35	3	chains	chain	NOUN
fcis-28118	35	4	exhibit	exhibit	VERB
fcis-28118	35	5	different	different	ADJ
fcis-28118	35	6	spatial	spatial	ADJ
fcis-28118	35	7	structures	structure	NOUN
fcis-28118	35	8	in	in	ADP
fcis-28118	35	9	various	various	ADJ
fcis-28118	35	10	folded	fold	VERB
fcis-28118	35	11	spatial	spatial	ADJ
fcis-28118	35	12	forms	form	NOUN
fcis-28118	35	13	.	.	PUNCT
fcis-28118	36	1	the	the	DET
fcis-28118	36	2	following	follow	VERB
fcis-28118	36	3	table1lists	table1list	NOUN
fcis-28118	36	4	the	the	DET
fcis-28118	36	5	names	name	NOUN
fcis-28118	36	6	,	,	PUNCT
fcis-28118	36	7	abbreviations	abbreviation	NOUN
fcis-28118	36	8	,	,	PUNCT
fcis-28118	36	9	and	and	CCONJ
fcis-28118	36	10	symbols	symbol	NOUN
fcis-28118	36	11	of	of	ADP
fcis-28118	36	12	the	the	DET
fcis-28118	36	13	20	20	NUM
fcis-28118	36	14	amino	amino	NOUN
fcis-28118	36	15	acids[10	acids[10	PROPN
fcis-28118	36	16	]	]	X
fcis-28118	36	17	:	:	PUNCT
fcis-28118	36	18	24	24	NUM
fcis-28118	36	19	table	table	NOUN
fcis-28118	36	20	1	1	NUM
fcis-28118	36	21	.	.	PUNCT
fcis-28118	36	22	names	name	NOUN
fcis-28118	36	23	,	,	PUNCT
fcis-28118	36	24	abbreviations	abbreviation	NOUN
fcis-28118	36	25	and	and	CCONJ
fcis-28118	36	26	symbols	symbol	NOUN
fcis-28118	36	27	of	of	ADP
fcis-28118	36	28	20	20	NUM
fcis-28118	36	29	amino	amino	ADJ
fcis-28118	36	30	acids	acid	NOUN
fcis-28118	36	31	.	.	PUNCT
fcis-28118	37	1	name	name	NOUN
fcis-28118	37	2	abbreviation	abbreviation	NOUN
fcis-28118	37	3	symbol	symbol	NOUN
fcis-28118	37	4	name	name	NOUN
fcis-28118	37	5	abbreviation	abbreviation	NOUN
fcis-28118	37	6	symbol	symbol	NOUN
fcis-28118	37	7	alanina	alanina	VERB
fcis-28118	37	8	ala	ala	PROPN
fcis-28118	37	9	a	a	DET
fcis-28118	37	10	methionine	methionine	NOUN
fcis-28118	37	11	met	meet	VERB
fcis-28118	37	12	m	m	PROPN
fcis-28118	37	13	cysteine	cysteine	NOUN
fcis-28118	37	14	cys	cys	PROPN
fcis-28118	37	15	c	c	PROPN
fcis-28118	37	16	asparagine	asparagine	PROPN
fcis-28118	37	17	asn	asn	NOUN
fcis-28118	37	18	n	n	CCONJ
fcis-28118	37	19	aspartic	aspartic	ADJ
fcis-28118	37	20	acid	acid	NOUN
fcis-28118	37	21	asp	asp	NOUN
fcis-28118	37	22	d	d	PROPN
fcis-28118	37	23	proline	proline	NOUN
fcis-28118	37	24	pro	pro	X
fcis-28118	37	25	p	p	PROPN
fcis-28118	37	26	glutamicacid	glutamicacid	PROPN
fcis-28118	37	27	glu	glu	PROPN
fcis-28118	37	28	e	e	PROPN
fcis-28118	37	29	glutamine	glutamine	PROPN
fcis-28118	37	30	gln	gln	PROPN
fcis-28118	37	31	q	q	PROPN
fcis-28118	37	32	phenylalanine	phenylalanine	NOUN
fcis-28118	37	33	ph	ph	NOUN
fcis-28118	37	34	f	f	PROPN
fcis-28118	37	35	arginine	arginine	NOUN
fcis-28118	37	36	arg	arg	NOUN
fcis-28118	37	37	r	r	NOUN
fcis-28118	37	38	glycine	glycine	NOUN
fcis-28118	37	39	gl	gl	NOUN
fcis-28118	37	40	g	g	PROPN
fcis-28118	37	41	serine	serine	NOUN
fcis-28118	37	42	ser	ser	NOUN
fcis-28118	37	43	s	s	PART
fcis-28118	37	44	histidine	histidine	NOUN
fcis-28118	37	45	his	his	PRON
fcis-28118	37	46	h	h	NOUN
fcis-28118	37	47	threonine	threonine	NOUN
fcis-28118	37	48	thr	thr	PROPN
fcis-28118	37	49	t	t	PROPN
fcis-28118	37	50	isoleucine	isoleucine	PROPN
fcis-28118	37	51	ile	ile	PROPN
fcis-28118	37	52	i	i	PROPN
fcis-28118	37	53	valine	valine	NOUN
fcis-28118	37	54	val	val	PROPN
fcis-28118	37	55	v	v	PROPN
fcis-28118	37	56	lysine	lysine	NOUN
fcis-28118	37	57	lys	lys	PROPN
fcis-28118	37	58	k	k	PROPN
fcis-28118	37	59	tryptophan	tryptophan	PROPN
fcis-28118	37	60	trp	trp	PROPN
fcis-28118	37	61	w	w	PROPN
fcis-28118	37	62	leucine	leucine	PROPN
fcis-28118	37	63	leu	leu	PROPN
fcis-28118	37	64	l	l	PROPN
fcis-28118	37	65	tyrosine	tyrosine	PROPN
fcis-28118	37	66	tyr	tyr	PROPN
fcis-28118	37	67	y	y	PROPN
fcis-28118	37	68	in	in	ADP
fcis-28118	37	69	the	the	DET
fcis-28118	37	70	classification	classification	NOUN
fcis-28118	37	71	task	task	NOUN
fcis-28118	37	72	,	,	PUNCT
fcis-28118	37	73	the	the	DET
fcis-28118	37	74	secondary	secondary	ADJ
fcis-28118	37	75	structure	structure	NOUN
fcis-28118	37	76	of	of	ADP
fcis-28118	37	77	proteins	protein	NOUN
fcis-28118	37	78	is	be	AUX
fcis-28118	37	79	divided	divide	VERB
fcis-28118	37	80	into	into	ADP
fcis-28118	37	81	eight	eight	NUM
fcis-28118	37	82	categories[11	categories[11	NOUN
fcis-28118	37	83	]	]	PUNCT
fcis-28118	37	84	:	:	PUNCT
fcis-28118	37	85	l	l	NOUN
fcis-28118	37	86	(	(	PUNCT
fcis-28118	37	87	random	random	ADJ
fcis-28118	37	88	coil	coil	NOUN
fcis-28118	37	89	)	)	PUNCT
fcis-28118	37	90	,	,	PUNCT
fcis-28118	37	91	b	b	X
fcis-28118	37	92	(	(	PUNCT
fcis-28118	37	93	single	single	ADJ
fcis-28118	37	94			NOUN
fcis-28118	37	95	fold	fold	NOUN
fcis-28118	37	96	)	)	PUNCT
fcis-28118	37	97	,	,	PUNCT
fcis-28118	37	98	e	e	X
fcis-28118	37	99	(	(	PUNCT
fcis-28118	37	100	extended	extended	ADJ
fcis-28118	37	101			PROPN
fcis-28118	37	102	fold	fold	NOUN
fcis-28118	37	103	)	)	PUNCT
fcis-28118	37	104	,	,	PUNCT
fcis-28118	37	105	g	g	PROPN
fcis-28118	37	106	(	(	PUNCT
fcis-28118	37	107	103	103	NUM
fcis-28118	37	108	helix	helix	NOUN
fcis-28118	37	109	)	)	PUNCT
fcis-28118	37	110	,	,	PUNCT
fcis-28118	37	111	i	i	PRON
fcis-28118	37	112	(	(	PUNCT
fcis-28118	37	113			VERB
fcis-28118	37	114	-helix	-helix	NOUN
fcis-28118	37	115	)	)	PUNCT
fcis-28118	37	116	,	,	PUNCT
fcis-28118	37	117	h	h	NOUN
fcis-28118	37	118	(	(	PUNCT
fcis-28118	37	119			NOUN
fcis-28118	37	120	helix	helix	NOUN
fcis-28118	37	121	)	)	PUNCT
fcis-28118	37	122	,	,	PUNCT
fcis-28118	37	123	s	s	X
fcis-28118	37	124	(	(	PUNCT
fcis-28118	37	125	high	high	ADJ
fcis-28118	37	126	curvature	curvature	NOUN
fcis-28118	37	127	ring	ring	NOUN
fcis-28118	37	128	)	)	PUNCT
fcis-28118	37	129	,	,	PUNCT
fcis-28118	37	130	and	and	CCONJ
fcis-28118	37	131	t	t	PROPN
fcis-28118	37	132	(	(	PUNCT
fcis-28118	37	133	turn)and	turn)and	INTJ
fcis-28118	37	134	other	other	ADJ
fcis-28118	37	135	structures	structure	NOUN
fcis-28118	37	136	,	,	PUNCT
fcis-28118	37	137	collectively	collectively	ADV
fcis-28118	37	138	referred	refer	VERB
fcis-28118	37	139	to	to	ADP
fcis-28118	37	140	as	as	SCONJ
fcis-28118	37	141	the	the	DET
fcis-28118	37	142	q8	q8	PROPN
fcis-28118	37	143	classification	classification	NOUN
fcis-28118	37	144	introduced	introduce	VERB
fcis-28118	37	145	above	above	ADV
fcis-28118	37	146	.	.	PUNCT
fcis-28118	38	1	the	the	DET
fcis-28118	38	2	corresponding	corresponding	ADJ
fcis-28118	38	3	forms	form	NOUN
fcis-28118	38	4	of	of	ADP
fcis-28118	38	5	the	the	DET
fcis-28118	38	6	primary	primary	ADJ
fcis-28118	38	7	structure	structure	NOUN
fcis-28118	38	8	and	and	CCONJ
fcis-28118	38	9	secondary	secondary	ADJ
fcis-28118	38	10	structure	structure	NOUN
fcis-28118	38	11	are	be	AUX
fcis-28118	38	12	as	as	SCONJ
fcis-28118	38	13	follow	follow	VERB
fcis-28118	38	14	figure	figure	NOUN
fcis-28118	38	15	1	1	NUM
fcis-28118	38	16	:	:	PUNCT
fcis-28118	38	17	figure	figure	NOUN
fcis-28118	38	18	1	1	NUM
fcis-28118	38	19	.	.	PUNCT
fcis-28118	39	1	protein	protein	NOUN
fcis-28118	39	2	primary	primary	ADJ
fcis-28118	39	3	structure	structure	NOUN
fcis-28118	39	4	corresponds	correspond	VERB
fcis-28118	39	5	to	to	ADP
fcis-28118	39	6	secondary	secondary	ADJ
fcis-28118	39	7	structure	structure	NOUN
fcis-28118	39	8	.	.	PUNCT
fcis-28118	40	1	3	3	X
fcis-28118	40	2	.	.	X
fcis-28118	40	3	experimental	experimental	ADJ
fcis-28118	40	4	data	datum	NOUN
fcis-28118	40	5	and	and	CCONJ
fcis-28118	40	6	coding	code	VERB
fcis-28118	40	7	3.1	3.1	NUM
fcis-28118	40	8	.	.	PUNCT
fcis-28118	41	1	experimental	experimental	ADJ
fcis-28118	41	2	data	datum	NOUN
fcis-28118	41	3	in	in	ADP
fcis-28118	41	4	this	this	DET
fcis-28118	41	5	paper	paper	NOUN
fcis-28118	41	6	,	,	PUNCT
fcis-28118	41	7	the	the	DET
fcis-28118	41	8	cb6133	cb6133	NOUN
fcis-28118	41	9	and	and	CCONJ
fcis-28118	41	10	cb513	cb513	NOUN
fcis-28118	41	11	datasets	dataset	NOUN
fcis-28118	41	12	generated	generate	VERB
fcis-28118	41	13	by	by	ADP
fcis-28118	41	14	the	the	DET
fcis-28118	41	15	pisces	pisce	NOUN
fcis-28118	41	16	cullpdb	cullpdb	NOUN
fcis-28118	41	17	are	be	AUX
fcis-28118	41	18	used	use	VERB
fcis-28118	41	19	as	as	ADP
fcis-28118	41	20	input	input	NOUN
fcis-28118	41	21	data	datum	NOUN
fcis-28118	41	22	and	and	CCONJ
fcis-28118	41	23	training	training	NOUN
fcis-28118	41	24	data	datum	NOUN
fcis-28118	41	25	for	for	ADP
fcis-28118	41	26	the	the	DET
fcis-28118	41	27	model[12	model[12	NOUN
fcis-28118	41	28	]	]	X
fcis-28118	41	29	.	.	PUNCT
fcis-28118	42	1	the	the	DET
fcis-28118	42	2	cb513	cb513	PROPN
fcis-28118	42	3	dataset	dataset	VERB
fcis-28118	42	4	consists	consist	NOUN
fcis-28118	42	5	of	of	ADP
fcis-28118	42	6	514	514	NUM
fcis-28118	42	7	protein	protein	NOUN
fcis-28118	42	8	first	first	ADJ
fcis-28118	42	9	-	-	PUNCT
fcis-28118	42	10	order	order	NOUN
fcis-28118	42	11	sequences[13	sequences[13	NOUN
fcis-28118	42	12	]	]	PUNCT
fcis-28118	42	13	,	,	PUNCT
fcis-28118	42	14	and	and	CCONJ
fcis-28118	42	15	the	the	DET
fcis-28118	42	16	cb6133	cb6133	NOUN
fcis-28118	42	17	dataset	dataset	VERB
fcis-28118	42	18	consists	consist	NOUN
fcis-28118	42	19	of	of	ADP
fcis-28118	42	20	6218	6218	NUM
fcis-28118	42	21	protein	protein	NOUN
fcis-28118	42	22	first	first	ADJ
fcis-28118	42	23	-	-	PUNCT
fcis-28118	42	24	order	order	NOUN
fcis-28118	42	25	sequences	sequence	NOUN
fcis-28118	42	26	.	.	PUNCT
fcis-28118	43	1	due	due	ADP
fcis-28118	43	2	to	to	ADP
fcis-28118	43	3	the	the	DET
fcis-28118	43	4	large	large	ADJ
fcis-28118	43	5	amount	amount	NOUN
fcis-28118	43	6	of	of	ADP
fcis-28118	43	7	sequence	sequence	NOUN
fcis-28118	43	8	homology	homology	NOUN
fcis-28118	43	9	redundancy	redundancy	NOUN
fcis-28118	43	10	between	between	ADP
fcis-28118	43	11	and	and	CCONJ
fcis-28118	43	12	within	within	ADP
fcis-28118	43	13	the	the	DET
fcis-28118	43	14	datasets	dataset	NOUN
fcis-28118	43	15	,	,	PUNCT
fcis-28118	43	16	the	the	DET
fcis-28118	43	17	accuracy	accuracy	NOUN
fcis-28118	43	18	of	of	ADP
fcis-28118	43	19	the	the	DET
fcis-28118	43	20	prediction	prediction	NOUN
fcis-28118	43	21	algorithm	algorithm	NOUN
fcis-28118	43	22	may	may	AUX
fcis-28118	43	23	be	be	AUX
fcis-28118	43	24	overestimated[14	overestimated[14	NOUN
fcis-28118	43	25	]	]	PUNCT
fcis-28118	43	26	.	.	PUNCT
fcis-28118	44	1	to	to	PART
fcis-28118	44	2	reduce	reduce	VERB
fcis-28118	44	3	the	the	DET
fcis-28118	44	4	homology	homology	NOUN
fcis-28118	44	5	within	within	ADP
fcis-28118	44	6	the	the	DET
fcis-28118	44	7	data	datum	NOUN
fcis-28118	44	8	,	,	PUNCT
fcis-28118	44	9	the	the	DET
fcis-28118	44	10	cb6133	cb6133	NOUN
fcis-28118	44	11	dataset	dataset	NOUN
fcis-28118	44	12	is	be	AUX
fcis-28118	44	13	screened	screen	VERB
fcis-28118	44	14	(	(	PUNCT
fcis-28118	44	15	excluding	exclude	VERB
fcis-28118	44	16	sequences	sequence	NOUN
fcis-28118	44	17	with	with	ADP
fcis-28118	44	18	homology	homology	NOUN
fcis-28118	44	19	greater	great	ADJ
fcis-28118	44	20	than	than	ADP
fcis-28118	44	21	25	25	NUM
fcis-28118	44	22	%	%	NOUN
fcis-28118	44	23	to	to	PART
fcis-28118	44	24	cb513	cb513	VERB
fcis-28118	44	25	)	)	PUNCT
fcis-28118	44	26	.	.	PUNCT
fcis-28118	45	1	the	the	DET
fcis-28118	45	2	filtered	filter	VERB
fcis-28118	45	3	dataset	dataset	NOUN
fcis-28118	45	4	,	,	PUNCT
fcis-28118	45	5	named	name	VERB
fcis-28118	45	6	cb6133filtered	cb6133filtere	VERB
fcis-28118	45	7	,	,	PUNCT
fcis-28118	45	8	contains	contain	VERB
fcis-28118	45	9	5534	5534	NUM
fcis-28118	45	10	protein	protein	NOUN
fcis-28118	45	11	sequences	sequence	NOUN
fcis-28118	45	12	.	.	PUNCT
fcis-28118	46	1	in	in	ADP
fcis-28118	46	2	this	this	DET
fcis-28118	46	3	paper	paper	NOUN
fcis-28118	46	4	,	,	PUNCT
fcis-28118	46	5	the	the	DET
fcis-28118	46	6	cb6133filitered	cb6133filitere	VERB
fcis-28118	46	7	dataset	dataset	NOUN
fcis-28118	46	8	is	be	AUX
fcis-28118	46	9	divided	divide	VERB
fcis-28118	46	10	into	into	ADP
fcis-28118	46	11	80	80	NUM
fcis-28118	46	12	%	%	NOUN
fcis-28118	46	13	training	training	NOUN
fcis-28118	46	14	data	datum	NOUN
fcis-28118	46	15	,	,	PUNCT
fcis-28118	46	16	10	10	NUM
fcis-28118	46	17	%	%	NOUN
fcis-28118	46	18	validation	validation	NOUN
fcis-28118	46	19	data	datum	NOUN
fcis-28118	46	20	,	,	PUNCT
fcis-28118	46	21	and	and	CCONJ
fcis-28118	46	22	10	10	NUM
fcis-28118	46	23	%	%	NOUN
fcis-28118	46	24	test	test	NOUN
fcis-28118	46	25	data	datum	NOUN
fcis-28118	46	26	.	.	PUNCT
fcis-28118	47	1	the	the	DET
fcis-28118	47	2	length	length	NOUN
fcis-28118	47	3	of	of	ADP
fcis-28118	47	4	each	each	DET
fcis-28118	47	5	protein	protein	NOUN
fcis-28118	47	6	sequence	sequence	NOUN
fcis-28118	47	7	is	be	AUX
fcis-28118	47	8	700	700	NUM
fcis-28118	47	9	,	,	PUNCT
fcis-28118	47	10	and	and	CCONJ
fcis-28118	47	11	514	514	NUM
fcis-28118	47	12	data	datum	NOUN
fcis-28118	47	13	from	from	ADP
fcis-28118	47	14	the	the	DET
fcis-28118	47	15	cb513	cb513	NOUN
fcis-28118	47	16	dataset	dataset	NOUN
fcis-28118	47	17	are	be	AUX
fcis-28118	47	18	also	also	ADV
fcis-28118	47	19	used	use	VERB
fcis-28118	47	20	as	as	ADP
fcis-28118	47	21	test	test	NOUN
fcis-28118	47	22	data	datum	NOUN
fcis-28118	47	23	.	.	PUNCT
fcis-28118	48	1	the	the	DET
fcis-28118	48	2	length	length	NOUN
fcis-28118	48	3	of	of	ADP
fcis-28118	48	4	each	each	DET
fcis-28118	48	5	protein	protein	NOUN
fcis-28118	48	6	sequence	sequence	NOUN
fcis-28118	48	7	is	be	AUX
fcis-28118	48	8	700	700	NUM
fcis-28118	48	9	,	,	PUNCT
fcis-28118	48	10	and	and	CCONJ
fcis-28118	48	11	the	the	DET
fcis-28118	48	12	dataset	dataset	NOUN
fcis-28118	48	13	contains	contain	VERB
fcis-28118	48	14	21dimensional	21dimensional	ADJ
fcis-28118	48	15	amino	amino	NOUN
fcis-28118	48	16	acid	acid	NOUN
fcis-28118	48	17	residues	residue	NOUN
fcis-28118	48	18	,	,	PUNCT
fcis-28118	48	19	21	21	NUM
fcis-28118	48	20	-	-	PUNCT
fcis-28118	48	21	dimensional	dimensional	ADJ
fcis-28118	48	22	orthogonal	orthogonal	ADJ
fcis-28118	48	23	codes	code	NOUN
fcis-28118	48	24	as	as	ADP
fcis-28118	48	25	input	input	NOUN
fcis-28118	48	26	features	feature	NOUN
fcis-28118	48	27	,	,	PUNCT
fcis-28118	48	28	and	and	CCONJ
fcis-28118	48	29	unique	unique	ADJ
fcis-28118	48	30	thermal	thermal	ADJ
fcis-28118	48	31	codes	code	NOUN
fcis-28118	48	32	of	of	ADP
fcis-28118	48	33	eight	eight	NUM
fcis-28118	48	34	protein	protein	NOUN
fcis-28118	48	35	secondary	secondary	ADJ
fcis-28118	48	36	structures	structure	NOUN
fcis-28118	48	37	as	as	ADP
fcis-28118	48	38	classification	classification	NOUN
fcis-28118	48	39	labels	label	NOUN
fcis-28118	48	40	.	.	PUNCT
fcis-28118	49	1	in	in	ADP
fcis-28118	49	2	order	order	NOUN
fcis-28118	49	3	to	to	PART
fcis-28118	49	4	improve	improve	VERB
fcis-28118	49	5	the	the	DET
fcis-28118	49	6	accuracy	accuracy	NOUN
fcis-28118	49	7	of	of	ADP
fcis-28118	49	8	structure	structure	NOUN
fcis-28118	49	9	prediction	prediction	NOUN
fcis-28118	49	10	,	,	PUNCT
fcis-28118	49	11	the	the	DET
fcis-28118	49	12	physical	physical	ADJ
fcis-28118	49	13	and	and	CCONJ
fcis-28118	49	14	chemical	chemical	NOUN
fcis-28118	49	15	properties	property	NOUN
fcis-28118	49	16	of	of	ADP
fcis-28118	49	17	various	various	ADJ
fcis-28118	49	18	amino	amino	ADJ
fcis-28118	49	19	acids	acid	NOUN
fcis-28118	49	20	and	and	CCONJ
fcis-28118	49	21	the	the	DET
fcis-28118	49	22	logarithmic	logarithmic	ADJ
fcis-28118	49	23	relative	relative	ADJ
fcis-28118	49	24	probabilities	probability	NOUN
fcis-28118	49	25	of	of	ADP
fcis-28118	49	26	eight	eight	NUM
fcis-28118	49	27	secondary	secondary	ADJ
fcis-28118	49	28	structure	structure	NOUN
fcis-28118	49	29	amino	amino	NOUN
fcis-28118	49	30	acids	acid	NOUN
fcis-28118	49	31	were	be	AUX
fcis-28118	49	32	added	add	VERB
fcis-28118	49	33	in	in	ADP
fcis-28118	49	34	this	this	DET
fcis-28118	49	35	paper	paper	NOUN
fcis-28118	49	36	,	,	PUNCT
fcis-28118	49	37	and	and	CCONJ
fcis-28118	49	38	these	these	PRON
fcis-28118	49	39	were	be	AUX
fcis-28118	49	40	encoded	encode	VERB
fcis-28118	49	41	to	to	PART
fcis-28118	49	42	carry	carry	VERB
fcis-28118	49	43	out	out	ADP
fcis-28118	49	44	feature	feature	NOUN
fcis-28118	49	45	fusion	fusion	NOUN
fcis-28118	49	46	with	with	ADP
fcis-28118	49	47	the	the	DET
fcis-28118	49	48	input	input	NOUN
fcis-28118	49	49	features	feature	NOUN
fcis-28118	49	50	described	describe	VERB
fcis-28118	49	51	above	above	ADV
fcis-28118	49	52	.	.	PUNCT
fcis-28118	50	1	3.2	3.2	NUM
fcis-28118	50	2	.	.	PUNCT
fcis-28118	50	3	pssms	pssm	NOUN
fcis-28118	50	4	and	and	CCONJ
fcis-28118	50	5	orthogonal	orthogonal	ADJ
fcis-28118	50	6	coding	code	VERB
fcis-28118	50	7	position	position	NOUN
fcis-28118	50	8	specific	specific	ADJ
fcis-28118	50	9	scoring	scoring	NOUN
fcis-28118	50	10	matrix	matrix	NOUN
fcis-28118	50	11	(	(	PUNCT
fcis-28118	50	12	pssms	pssm	NOUN
fcis-28118	50	13	)	)	PUNCT
fcis-28118	50	14	was	be	AUX
fcis-28118	50	15	first	first	ADV
fcis-28118	50	16	proposed	propose	VERB
fcis-28118	50	17	by	by	ADP
fcis-28118	50	18	steven	steven	PROPN
fcis-28118	50	19	altshchulden	altshchulden	PROPN
fcis-28118	50	20	et	et	PROPN
fcis-28118	50	21	al[15	al[15	PROPN
fcis-28118	50	22	]	]	PUNCT
fcis-28118	50	23	.	.	PUNCT
fcis-28118	51	1	in	in	ADP
fcis-28118	51	2	2008	2008	NUM
fcis-28118	51	3	.	.	PUNCT
fcis-28118	52	1	pssms	pssm	NOUN
fcis-28118	52	2	were	be	AUX
fcis-28118	52	3	obtained	obtain	VERB
fcis-28118	52	4	from	from	ADP
fcis-28118	52	5	the	the	DET
fcis-28118	52	6	comparison	comparison	NOUN
fcis-28118	52	7	of	of	ADP
fcis-28118	52	8	multiple	multiple	ADJ
fcis-28118	52	9	sequences	sequence	NOUN
fcis-28118	52	10	,	,	PUNCT
fcis-28118	52	11	which	which	PRON
fcis-28118	52	12	can	can	AUX
fcis-28118	52	13	extract	extract	VERB
fcis-28118	52	14	protein	protein	NOUN
fcis-28118	52	15	sequence	sequence	NOUN
fcis-28118	52	16	features	feature	VERB
fcis-28118	52	17	well	well	ADV
fcis-28118	52	18	,	,	PUNCT
fcis-28118	52	19	and	and	CCONJ
fcis-28118	52	20	are	be	AUX
fcis-28118	52	21	generated	generate	VERB
fcis-28118	52	22	by	by	ADP
fcis-28118	52	23	psi	psi	NOUN
fcis-28118	52	24	-	-	PUNCT
fcis-28118	52	25	blast[16	blast[16	NOUN
fcis-28118	52	26	]	]	PUNCT
fcis-28118	52	27	searching	search	VERB
fcis-28118	52	28	for	for	ADP
fcis-28118	52	29	protein	protein	NOUN
fcis-28118	52	30	sequences	sequence	NOUN
fcis-28118	52	31	related	relate	VERB
fcis-28118	52	32	to	to	ADP
fcis-28118	52	33	the	the	DET
fcis-28118	52	34	evolution	evolution	NOUN
fcis-28118	52	35	of	of	ADP
fcis-28118	52	36	the	the	DET
fcis-28118	52	37	queried	query	VERB
fcis-28118	52	38	protein	protein	NOUN
fcis-28118	52	39	sequence	sequence	NOUN
fcis-28118	52	40	.	.	PUNCT
fcis-28118	53	1	after	after	ADP
fcis-28118	53	2	several	several	ADJ
fcis-28118	53	3	queries	query	NOUN
fcis-28118	53	4	,	,	PUNCT
fcis-28118	53	5	sequences	sequence	NOUN
fcis-28118	53	6	with	with	ADP
fcis-28118	53	7	more	more	ADJ
fcis-28118	53	8	evolutionary	evolutionary	ADJ
fcis-28118	53	9	information	information	NOUN
fcis-28118	53	10	were	be	AUX
fcis-28118	53	11	found	find	VERB
fcis-28118	53	12	until	until	SCONJ
fcis-28118	53	13	the	the	DET
fcis-28118	53	14	obtained	obtain	VERB
fcis-28118	53	15	sequence	sequence	NOUN
fcis-28118	53	16	contains	contain	VERB
fcis-28118	53	17	no	no	DET
fcis-28118	53	18	more	more	ADV
fcis-28118	53	19	evolutionary	evolutionary	ADJ
fcis-28118	53	20	information	information	NOUN
fcis-28118	53	21	.	.	PUNCT
fcis-28118	54	1	the	the	DET
fcis-28118	54	2	following	follow	VERB
fcis-28118	54	3	is	be	AUX
fcis-28118	54	4	a	a	DET
fcis-28118	54	5	pssm	pssm	ADJ
fcis-28118	54	6	matrix	matrix	NOUN
fcis-28118	54	7	based	base	VERB
fcis-28118	54	8	on	on	ADP
fcis-28118	54	9	protein	protein	NOUN
fcis-28118	54	10	sequences	sequence	NOUN
fcis-28118	54	11	:	:	PUNCT
fcis-28118	54	12	1	1	NUM
fcis-28118	54	13	1	1	NUM
fcis-28118	54	14	1	1	NUM
fcis-28118	54	15	201	201	NUM
fcis-28118	54	16	2	2	NUM
fcis-28118	54	17	1	1	NUM
fcis-28118	54	18	2	2	NUM
fcis-28118	54	19	202	202	NUM
fcis-28118	54	20	3	3	NUM
fcis-28118	54	21	1	1	NUM
fcis-28118	54	22	3	3	NUM
fcis-28118	54	23	3	3	NUM
fcis-28118	54	24	20	20	NUM
fcis-28118	54	25	1	1	NUM
fcis-28118	54	26	20	20	NUM
fcis-28118	54	27	j	j	NOUN
fcis-28118	55	1	j	j	PROPN
fcis-28118	55	2	j	j	PROPN
fcis-28118	55	3	i	i	PRON
fcis-28118	55	4	j	j	PROPN
fcis-28118	55	5	m	m	VERB
fcis-28118	55	6	jm	jm	PROPN
fcis-28118	55	7	m	m	PROPN
fcis-28118	55	8	p	p	NOUN
fcis-28118	55	9	pp	pp	ADP
fcis-28118	55	10	p	p	PROPN
fcis-28118	55	11	pp	pp	ADJ
fcis-28118	55	12	pssm	pssm	NOUN
fcis-28118	56	1	p	p	X
fcis-28118	56	2	p	p	PROPN
fcis-28118	56	3	p	p	X
fcis-28118	56	4	p	p	X
fcis-28118	56	5	pp	pp	ADP
fcis-28118	56	6	p	p	NOUN
fcis-28118	56	7			NOUN
fcis-28118	57	1			ADJ
fcis-28118	57	2			NOUN
fcis-28118	57	3			ADJ
fcis-28118	57	4			NOUN
fcis-28118	57	5			NOUN
fcis-28118	57	6			NOUN
fcis-28118	57	7			VERB
fcis-28118	57	8			ADJ
fcis-28118	57	9			NOUN
fcis-28118	57	10			ADJ
fcis-28118	57	11			ADJ
fcis-28118	57	12			NOUN
fcis-28118	57	13			PROPN
fcis-28118	57	14			NOUN
fcis-28118	57	15			NOUN
fcis-28118	57	16			NOUN
fcis-28118	58	1			NUM
fcis-28118	58	2			NOUN
fcis-28118	58	3			NOUN
fcis-28118	58	4			NOUN
fcis-28118	58	5			PROPN
fcis-28118	58	6			NOUN
fcis-28118	58	7			PROPN
fcis-28118	58	8			PROPN
fcis-28118	58	9			VERB
fcis-28118	58	10			PROPN
fcis-28118	58	11	(	(	PUNCT
fcis-28118	58	12	1	1	NUM
fcis-28118	58	13	)	)	PUNCT
fcis-28118	58	14	in	in	ADP
fcis-28118	58	15	the	the	DET
fcis-28118	58	16	given	give	VERB
fcis-28118	58	17	pssm	pssm	ADJ
fcis-28118	58	18	matrix	matrix	NOUN
fcis-28118	58	19	,	,	PUNCT
fcis-28118	58	20	i	i	PRON
fcis-28118	58	21	jp	jp	PROPN
fcis-28118	58	22	represents	represent	VERB
fcis-28118	58	23	the	the	DET
fcis-28118	58	24	probability	probability	NOUN
fcis-28118	58	25	that	that	SCONJ
fcis-28118	58	26	the	the	DET
fcis-28118	58	27	i	i	PRON
fcis-28118	58	28	th	th	NOUN
fcis-28118	58	29	amino	amino	NOUN
fcis-28118	58	30	acid	acid	NOUN
fcis-28118	58	31	residue	residue	NOUN
fcis-28118	58	32	replaces	replace	VERB
fcis-28118	58	33	the	the	DET
fcis-28118	58	34	j	j	PROPN
fcis-28118	58	35	th	th	X
fcis-28118	58	36	amino	amino	NOUN
fcis-28118	58	37	acid	acid	NOUN
fcis-28118	58	38	residue	residue	NOUN
fcis-28118	58	39	in	in	ADP
fcis-28118	58	40	the	the	DET
fcis-28118	58	41	protein	protein	NOUN
fcis-28118	58	42	sequence	sequence	NOUN
fcis-28118	58	43	.	.	PUNCT
fcis-28118	59	1	after	after	SCONJ
fcis-28118	59	2	the	the	DET
fcis-28118	59	3	pssm	pssm	ADJ
fcis-28118	59	4	matrix	matrix	NOUN
fcis-28118	59	5	is	be	AUX
fcis-28118	59	6	generated	generate	VERB
fcis-28118	59	7	through	through	ADP
fcis-28118	59	8	psi	psi	ADJ
fcis-28118	59	9	-	-	PUNCT
fcis-28118	59	10	blast	blast	NOUN
fcis-28118	59	11	iteration	iteration	NOUN
fcis-28118	59	12	,	,	PUNCT
fcis-28118	59	13	it	it	PRON
fcis-28118	59	14	is	be	AUX
fcis-28118	59	15	necessary	necessary	ADJ
fcis-28118	59	16	to	to	PART
fcis-28118	59	17	use	use	VERB
fcis-28118	59	18	functions	function	NOUN
fcis-28118	59	19	to	to	PART
fcis-28118	59	20	normalize	normalize	VERB
fcis-28118	59	21	the	the	DET
fcis-28118	59	22	generated	generate	VERB
fcis-28118	59	23	matrix	matrix	NOUN
fcis-28118	59	24	because	because	SCONJ
fcis-28118	59	25	the	the	DET
fcis-28118	59	26	generated	generate	VERB
fcis-28118	59	27	matrix	matrix	NOUN
fcis-28118	59	28	is	be	AUX
fcis-28118	59	29	an	an	DET
fcis-28118	59	30	integer	integer	NOUN
fcis-28118	59	31	matrix	matrix	NOUN
fcis-28118	59	32	.	.	PUNCT
fcis-28118	60	1	in	in	ADP
fcis-28118	60	2	the	the	DET
fcis-28118	60	3	normalization	normalization	NOUN
fcis-28118	60	4	process	process	NOUN
fcis-28118	60	5	,	,	PUNCT
fcis-28118	60	6	the	the	DET
fcis-28118	60	7	sigmoid	sigmoid	NOUN
fcis-28118	60	8	function	function	NOUN
fcis-28118	60	9	is	be	AUX
fcis-28118	60	10	generally	generally	ADV
fcis-28118	60	11	used	use	VERB
fcis-28118	60	12	.	.	PUNCT
fcis-28118	61	1	after	after	ADP
fcis-28118	61	2	processing	processing	NOUN
fcis-28118	61	3	,	,	PUNCT
fcis-28118	61	4	the	the	DET
fcis-28118	61	5	value	value	NOUN
fcis-28118	61	6	of	of	ADP
fcis-28118	61	7	the	the	DET
fcis-28118	61	8	matrix	matrix	NOUN
fcis-28118	61	9	will	will	AUX
fcis-28118	61	10	be	be	AUX
fcis-28118	61	11	remapped	remappe	VERB
fcis-28118	61	12	to	to	ADP
fcis-28118	61	13	the	the	DET
fcis-28118	61	14	interval	interval	NOUN
fcis-28118	61	15	[	[	X
fcis-28118	61	16	0,1	0,1	NUM
fcis-28118	61	17	]	]	PUNCT
fcis-28118	61	18	.	.	PUNCT
fcis-28118	62	1	the	the	DET
fcis-28118	62	2	sigmoid	sigmoid	NOUN
fcis-28118	62	3	function	function	NOUN
fcis-28118	62	4	calculation	calculation	NOUN
fcis-28118	62	5	formula	formula	NOUN
fcis-28118	62	6	is	be	AUX
fcis-28118	62	7	given	give	VERB
fcis-28118	62	8	below	below	ADP
fcis-28118	62	9	:	:	PUNCT
fcis-28118	62	10	1	1	NUM
fcis-28118	62	11	(	(	PUNCT
fcis-28118	62	12	)	)	PUNCT
fcis-28118	62	13	1	1	NUM
fcis-28118	63	1	x	x	SYM
fcis-28118	63	2	f	f	NOUN
fcis-28118	63	3	x	x	PUNCT
fcis-28118	63	4	e	e	SYM
fcis-28118	63	5			PROPN
fcis-28118	63	6			X
fcis-28118	63	7	(	(	PUNCT
fcis-28118	63	8	2	2	X
fcis-28118	63	9	)	)	PUNCT
fcis-28118	63	10	the	the	DET
fcis-28118	63	11	orthogonal	orthogonal	ADJ
fcis-28118	63	12	coding	coding	NOUN
fcis-28118	63	13	of	of	ADP
fcis-28118	63	14	proteins	protein	NOUN
fcis-28118	63	15	[	[	X
fcis-28118	63	16	17	17	NUM
fcis-28118	63	17	]	]	PUNCT
fcis-28118	63	18	is	be	AUX
fcis-28118	63	19	to	to	PART
fcis-28118	63	20	represent	represent	VERB
fcis-28118	63	21	20	20	NUM
fcis-28118	63	22	protein	protein	NOUN
fcis-28118	63	23	amino	amino	NOUN
fcis-28118	63	24	acid	acid	NOUN
fcis-28118	63	25	sequences	sequence	NOUN
fcis-28118	63	26	and	and	CCONJ
fcis-28118	63	27	unknown	unknown	ADJ
fcis-28118	63	28	amino	amino	NOUN
fcis-28118	63	29	acid	acid	NOUN
fcis-28118	63	30	sequences	sequence	NOUN
fcis-28118	63	31	as	as	ADP
fcis-28118	63	32	21	21	NUM
fcis-28118	63	33	-	-	PUNCT
fcis-28118	63	34	dimensional	dimensional	ADJ
fcis-28118	63	35	orthogonal	orthogonal	ADJ
fcis-28118	63	36	vectors	vector	NOUN
fcis-28118	63	37	containing	contain	VERB
fcis-28118	63	38	only	only	ADV
fcis-28118	63	39	0	0	NUM
fcis-28118	63	40	and	and	CCONJ
fcis-28118	63	41	1	1	NUM
fcis-28118	63	42	,	,	PUNCT
fcis-28118	63	43	among	among	ADP
fcis-28118	63	44	these	these	PRON
fcis-28118	63	45	,	,	PUNCT
fcis-28118	63	46	20	20	NUM
fcis-28118	63	47	known	know	VERB
fcis-28118	63	48	sequences	sequence	NOUN
fcis-28118	63	49	are	be	AUX
fcis-28118	63	50	represented	represent	VERB
fcis-28118	63	51	as	as	ADP
fcis-28118	63	52	a	a	DET
fcis-28118	63	53	,	,	PUNCT
fcis-28118	63	54	c	c	NOUN
fcis-28118	63	55	,	,	PUNCT
fcis-28118	63	56	d	d	NOUN
fcis-28118	63	57	,	,	PUNCT
fcis-28118	63	58	e	e	NOUN
fcis-28118	63	59	,	,	PUNCT
fcis-28118	63	60	f	f	PROPN
fcis-28118	63	61	,	,	PUNCT
fcis-28118	63	62	g	g	PROPN
fcis-28118	63	63	,	,	PUNCT
fcis-28118	63	64	h	h	NOUN
fcis-28118	63	65	,	,	PUNCT
fcis-28118	63	66	i	i	PRON
fcis-28118	63	67	,	,	PUNCT
fcis-28118	63	68	k	k	PROPN
fcis-28118	63	69	,	,	PUNCT
fcis-28118	63	70	l	l	PROPN
fcis-28118	63	71	,	,	PUNCT
fcis-28118	63	72	m	m	PROPN
fcis-28118	63	73	,	,	PUNCT
fcis-28118	63	74	n	n	CCONJ
fcis-28118	63	75	,	,	PUNCT
fcis-28118	63	76	p	p	X
fcis-28118	63	77	,	,	PUNCT
fcis-28118	63	78	q	q	ADJ
fcis-28118	63	79	,	,	PUNCT
fcis-28118	63	80	r	r	NOUN
fcis-28118	63	81	,	,	PUNCT
fcis-28118	63	82	s	s	PROPN
fcis-28118	63	83	,	,	PUNCT
fcis-28118	63	84	t	t	PROPN
fcis-28118	63	85	,	,	PUNCT
fcis-28118	63	86	v	v	NOUN
fcis-28118	63	87	,	,	PUNCT
fcis-28118	63	88	w	w	NOUN
fcis-28118	63	89	,	,	PUNCT
fcis-28118	63	90	and	and	CCONJ
fcis-28118	63	91	y	y	PROPN
fcis-28118	63	92	as	as	ADP
fcis-28118	63	93	seen	see	VERB
fcis-28118	63	94	above	above	ADV
fcis-28118	63	95	.	.	PUNCT
fcis-28118	64	1	since	since	SCONJ
fcis-28118	64	2	the	the	DET
fcis-28118	64	3	codes	code	NOUN
fcis-28118	64	4	of	of	ADP
fcis-28118	64	5	any	any	DET
fcis-28118	64	6	two	two	NUM
fcis-28118	64	7	amino	amino	ADJ
fcis-28118	64	8	acids	acid	NOUN
fcis-28118	64	9	are	be	AUX
fcis-28118	64	10	orthogonal	orthogonal	ADJ
fcis-28118	64	11	,	,	PUNCT
fcis-28118	64	12	it	it	PRON
fcis-28118	64	13	can	can	AUX
fcis-28118	64	14	be	be	AUX
fcis-28118	64	15	seen	see	VERB
fcis-28118	64	16	that	that	SCONJ
fcis-28118	64	17	the	the	DET
fcis-28118	64	18	inner	inner	ADJ
fcis-28118	64	19	product	product	NOUN
fcis-28118	64	20	between	between	ADP
fcis-28118	64	21	each	each	DET
fcis-28118	64	22	amino	amino	NOUN
fcis-28118	64	23	acid	acid	NOUN
fcis-28118	64	24	is	be	AUX
fcis-28118	64	25	0	0	NUM
fcis-28118	64	26	.	.	PUNCT
fcis-28118	65	1	in	in	ADP
fcis-28118	65	2	addition	addition	NOUN
fcis-28118	65	3	to	to	ADP
fcis-28118	65	4	the	the	DET
fcis-28118	65	5	20	20	NUM
fcis-28118	65	6	amino	amino	NOUN
fcis-28118	65	7	acids	acid	NOUN
fcis-28118	65	8	introduced	introduce	VERB
fcis-28118	65	9	above	above	ADV
fcis-28118	65	10	,	,	PUNCT
fcis-28118	65	11	x	x	VERB
fcis-28118	65	12	is	be	AUX
fcis-28118	65	13	added	add	VERB
fcis-28118	65	14	to	to	PART
fcis-28118	65	15	represent	represent	VERB
fcis-28118	65	16	the	the	DET
fcis-28118	65	17	21st	21st	ADJ
fcis-28118	65	18	unknown	unknown	ADJ
fcis-28118	65	19	amino	amino	NOUN
fcis-28118	65	20	acid	acid	NOUN
fcis-28118	65	21	sequence	sequence	NOUN
fcis-28118	65	22	because	because	SCONJ
fcis-28118	65	23	the	the	DET
fcis-28118	65	24	simulation	simulation	NOUN
fcis-28118	65	25	experiment	experiment	NOUN
fcis-28118	65	26	sometimes	sometimes	ADV
fcis-28118	65	27	can	can	AUX
fcis-28118	65	28	not	not	PART
fcis-28118	65	29	determine	determine	VERB
fcis-28118	65	30	the	the	DET
fcis-28118	65	31	specific	specific	ADJ
fcis-28118	65	32	type	type	NOUN
fcis-28118	65	33	of	of	ADP
fcis-28118	65	34	a	a	DET
fcis-28118	65	35	certain	certain	ADJ
fcis-28118	65	36	amino	amino	NOUN
fcis-28118	65	37	acid	acid	NOUN
fcis-28118	65	38	.	.	PUNCT
fcis-28118	66	1	the	the	DET
fcis-28118	66	2	following	follow	VERB
fcis-28118	66	3	figure2	figure2	NOUN
fcis-28118	66	4	shows	show	VERB
fcis-28118	66	5	the	the	DET
fcis-28118	66	6	orthogonal	orthogonal	ADJ
fcis-28118	66	7	codes	code	NOUN
fcis-28118	66	8	corresponding	correspond	VERB
fcis-28118	66	9	to	to	ADP
fcis-28118	66	10	20	20	NUM
fcis-28118	66	11	amino	amino	ADJ
fcis-28118	66	12	acids	acid	NOUN
fcis-28118	66	13	and	and	CCONJ
fcis-28118	66	14	x	x	NOUN
fcis-28118	66	15	:	:	PUNCT
fcis-28118	66	16	25	25	NUM
fcis-28118	66	17	figure	figure	NOUN
fcis-28118	66	18	2	2	NUM
fcis-28118	66	19	.	.	PUNCT
fcis-28118	66	20	amino	amino	ADJ
fcis-28118	66	21	acids	acid	NOUN
fcis-28118	66	22	correspond	correspond	VERB
fcis-28118	66	23	to	to	ADP
fcis-28118	66	24	orthogonal	orthogonal	ADJ
fcis-28118	66	25	coding	coding	NOUN
fcis-28118	66	26	.	.	PUNCT
fcis-28118	67	1	4	4	X
fcis-28118	67	2	.	.	X
fcis-28118	67	3	network	network	NOUN
fcis-28118	67	4	construction	construction	NOUN
fcis-28118	67	5	4.1	4.1	NUM
fcis-28118	67	6	.	.	PUNCT
fcis-28118	68	1	sm	sm	PROPN
fcis-28118	68	2	convolution	convolution	NOUN
fcis-28118	68	3	block	block	VERB
fcis-28118	68	4	convolutional	convolutional	ADJ
fcis-28118	68	5	blocks	block	NOUN
fcis-28118	68	6	,	,	PUNCT
fcis-28118	68	7	as	as	ADP
fcis-28118	68	8	the	the	DET
fcis-28118	68	9	basic	basic	ADJ
fcis-28118	68	10	constituent	constituent	NOUN
fcis-28118	68	11	unit	unit	NOUN
fcis-28118	68	12	of	of	ADP
fcis-28118	68	13	convolutional	convolutional	ADJ
fcis-28118	68	14	neural	neural	ADJ
fcis-28118	68	15	networks	network	NOUN
fcis-28118	68	16	,	,	PUNCT
fcis-28118	68	17	are	be	AUX
fcis-28118	68	18	usually	usually	ADV
fcis-28118	68	19	composed	compose	VERB
fcis-28118	68	20	of	of	ADP
fcis-28118	68	21	convolutional	convolutional	ADJ
fcis-28118	68	22	layers	layer	NOUN
fcis-28118	68	23	,	,	PUNCT
fcis-28118	68	24	activation	activation	NOUN
fcis-28118	68	25	function	function	NOUN
fcis-28118	68	26	layers	layer	NOUN
fcis-28118	68	27	,	,	PUNCT
fcis-28118	68	28	and	and	CCONJ
fcis-28118	68	29	other	other	ADJ
fcis-28118	68	30	optional	optional	ADJ
fcis-28118	68	31	layers	layer	NOUN
fcis-28118	68	32	(	(	PUNCT
fcis-28118	68	33	which	which	PRON
fcis-28118	68	34	vary	vary	VERB
fcis-28118	68	35	from	from	ADP
fcis-28118	68	36	data	datum	NOUN
fcis-28118	68	37	to	to	ADP
fcis-28118	68	38	data	datum	NOUN
fcis-28118	68	39	)	)	PUNCT
fcis-28118	68	40	.	.	PUNCT
fcis-28118	69	1	these	these	DET
fcis-28118	69	2	blocks	block	NOUN
fcis-28118	69	3	are	be	AUX
fcis-28118	69	4	designed	design	VERB
fcis-28118	69	5	to	to	PART
fcis-28118	69	6	extract	extract	VERB
fcis-28118	69	7	features	feature	NOUN
fcis-28118	69	8	from	from	ADP
fcis-28118	69	9	data	datum	NOUN
fcis-28118	69	10	and	and	CCONJ
fcis-28118	69	11	pass	pass	VERB
fcis-28118	69	12	them	they	PRON
fcis-28118	69	13	to	to	ADP
fcis-28118	69	14	the	the	DET
fcis-28118	69	15	next	next	ADJ
fcis-28118	69	16	layer	layer	NOUN
fcis-28118	69	17	of	of	ADP
fcis-28118	69	18	the	the	DET
fcis-28118	69	19	network	network	NOUN
fcis-28118	69	20	for	for	ADP
fcis-28118	69	21	further	further	ADJ
fcis-28118	69	22	feature	feature	NOUN
fcis-28118	69	23	extraction	extraction	NOUN
fcis-28118	69	24	or	or	CCONJ
fcis-28118	69	25	processing	processing	NOUN
fcis-28118	69	26	.	.	PUNCT
fcis-28118	70	1	convolutional	convolutional	ADJ
fcis-28118	70	2	blocks	block	NOUN
fcis-28118	70	3	typically	typically	ADV
fcis-28118	70	4	begin	begin	VERB
fcis-28118	70	5	with	with	ADP
fcis-28118	70	6	the	the	DET
fcis-28118	70	7	convolutional	convolutional	ADJ
fcis-28118	70	8	layer	layer	NOUN
fcis-28118	70	9	.	.	PUNCT
fcis-28118	71	1	where	where	SCONJ
fcis-28118	71	2	the	the	DET
fcis-28118	71	3	convolution	convolution	NOUN
fcis-28118	71	4	kernel	kernel	NOUN
fcis-28118	71	5	(	(	PUNCT
fcis-28118	71	6	i.e.	i.e.	X
fcis-28118	71	7	filter	filter	NOUN
fcis-28118	71	8	)	)	PUNCT
fcis-28118	71	9	is	be	AUX
fcis-28118	71	10	used	use	VERB
fcis-28118	71	11	to	to	PART
fcis-28118	71	12	convolve	convolve	VERB
fcis-28118	71	13	the	the	DET
fcis-28118	71	14	input	input	NOUN
fcis-28118	71	15	data	datum	NOUN
fcis-28118	71	16	and	and	CCONJ
fcis-28118	71	17	extract	extract	VERB
fcis-28118	71	18	its	its	PRON
fcis-28118	71	19	feature	feature	NOUN
fcis-28118	71	20	.	.	PUNCT
fcis-28118	72	1	figure	figure	NOUN
fcis-28118	72	2	3	3	NUM
fcis-28118	72	3	.	.	PUNCT
fcis-28118	72	4	msc	msc	PROPN
fcis-28118	72	5	convolutional	convolutional	ADJ
fcis-28118	72	6	block	block	NOUN
fcis-28118	72	7	after	after	ADP
fcis-28118	72	8	the	the	DET
fcis-28118	72	9	convolutional	convolutional	ADJ
fcis-28118	72	10	layer	layer	NOUN
fcis-28118	72	11	,	,	PUNCT
fcis-28118	72	12	in	in	ADP
fcis-28118	72	13	order	order	NOUN
fcis-28118	72	14	to	to	PART
fcis-28118	72	15	introduce	introduce	VERB
fcis-28118	72	16	nonlinearity	nonlinearity	NOUN
fcis-28118	72	17	,	,	PUNCT
fcis-28118	72	18	the	the	DET
fcis-28118	72	19	activation	activation	NOUN
fcis-28118	72	20	function	function	NOUN
fcis-28118	72	21	layer	layer	NOUN
fcis-28118	72	22	is	be	AUX
fcis-28118	72	23	usually	usually	ADV
fcis-28118	72	24	added	add	VERB
fcis-28118	72	25	,	,	PUNCT
fcis-28118	72	26	and	and	CCONJ
fcis-28118	72	27	the	the	DET
fcis-28118	72	28	commonly	commonly	ADV
fcis-28118	72	29	used	use	VERB
fcis-28118	72	30	activation	activation	NOUN
fcis-28118	72	31	functions	function	NOUN
fcis-28118	72	32	include	include	VERB
fcis-28118	72	33	relu(rectified	relu(rectifie	VERB
fcis-28118	72	34	linear	linear	PROPN
fcis-28118	72	35	unit	unit	NOUN
fcis-28118	72	36	)	)	PUNCT
fcis-28118	72	37	,	,	PUNCT
fcis-28118	72	38	tanh	tanh	NOUN
fcis-28118	72	39	,	,	PUNCT
fcis-28118	72	40	sigmoid	sigmoid	NOUN
fcis-28118	72	41	,	,	PUNCT
fcis-28118	72	42	etc	etc	X
fcis-28118	72	43	.	.	X
fcis-28118	73	1	in	in	ADP
fcis-28118	73	2	order	order	NOUN
fcis-28118	73	3	to	to	PART
fcis-28118	73	4	improve	improve	VERB
fcis-28118	73	5	the	the	DET
fcis-28118	73	6	stability	stability	NOUN
fcis-28118	73	7	and	and	CCONJ
fcis-28118	73	8	performance	performance	NOUN
fcis-28118	73	9	of	of	ADP
fcis-28118	73	10	the	the	DET
fcis-28118	73	11	model	model	NOUN
fcis-28118	73	12	,	,	PUNCT
fcis-28118	73	13	both	both	CCONJ
fcis-28118	73	14	the	the	DET
fcis-28118	73	15	bn	bn	ADJ
fcis-28118	73	16	layer	layer	NOUN
fcis-28118	73	17	and	and	CCONJ
fcis-28118	73	18	the	the	DET
fcis-28118	73	19	dropout	dropout	NOUN
fcis-28118	73	20	layer	layer	NOUN
fcis-28118	73	21	are	be	AUX
fcis-28118	73	22	added	add	VERB
fcis-28118	73	23	during	during	ADP
fcis-28118	73	24	the	the	DET
fcis-28118	73	25	experiment	experiment	NOUN
fcis-28118	73	26	.	.	PUNCT
fcis-28118	74	1	batch	batch	NOUN
fcis-28118	74	2	normalization	normalization	NOUN
fcis-28118	74	3	can	can	AUX
fcis-28118	74	4	retain	retain	VERB
fcis-28118	74	5	more	more	ADJ
fcis-28118	74	6	feature	feature	NOUN
fcis-28118	74	7	information	information	NOUN
fcis-28118	74	8	even	even	ADV
fcis-28118	74	9	with	with	ADP
fcis-28118	74	10	a	a	DET
fcis-28118	74	11	smaller	small	ADJ
fcis-28118	74	12	data	datum	NOUN
fcis-28118	74	13	volume	volume	NOUN
fcis-28118	74	14	.	.	PUNCT
fcis-28118	75	1	figure	figure	NOUN
fcis-28118	75	2	4	4	NUM
fcis-28118	75	3	.	.	PUNCT
fcis-28118	75	4	ssc	ssc	VERB
fcis-28118	75	5	convolutional	convolutional	ADJ
fcis-28118	75	6	block	block	NOUN
fcis-28118	75	7	.	.	PUNCT
fcis-28118	76	1	in	in	ADP
fcis-28118	76	2	addition	addition	NOUN
fcis-28118	76	3	,	,	PUNCT
fcis-28118	76	4	it	it	PRON
fcis-28118	76	5	can	can	AUX
fcis-28118	76	6	also	also	ADV
fcis-28118	76	7	mitigate	mitigate	VERB
fcis-28118	76	8	the	the	DET
fcis-28118	76	9	problems	problem	NOUN
fcis-28118	76	10	of	of	ADP
fcis-28118	76	11	gradient	gradient	ADJ
fcis-28118	76	12	disappearance	disappearance	NOUN
fcis-28118	76	13	and	and	CCONJ
fcis-28118	76	14	explosion	explosion	NOUN
fcis-28118	76	15	,	,	PUNCT
fcis-28118	76	16	thus	thus	ADV
fcis-28118	76	17	contributing	contribute	VERB
fcis-28118	76	18	to	to	PART
fcis-28118	76	19	better	well	ADJ
fcis-28118	76	20	and	and	CCONJ
fcis-28118	76	21	more	more	ADV
fcis-28118	76	22	stable	stable	ADJ
fcis-28118	76	23	training	training	NOUN
fcis-28118	76	24	of	of	ADP
fcis-28118	76	25	the	the	DET
fcis-28118	76	26	model	model	NOUN
fcis-28118	76	27	.	.	PUNCT
fcis-28118	77	1	in	in	ADP
fcis-28118	77	2	this	this	DET
fcis-28118	77	3	experiment	experiment	NOUN
fcis-28118	77	4	,	,	PUNCT
fcis-28118	77	5	two	two	NUM
fcis-28118	77	6	types	type	NOUN
fcis-28118	77	7	of	of	ADP
fcis-28118	77	8	convolution	convolution	NOUN
fcis-28118	77	9	blocks	block	NOUN
fcis-28118	77	10	are	be	AUX
fcis-28118	77	11	implemented	implement	VERB
fcis-28118	77	12	in	in	ADP
fcis-28118	77	13	the	the	DET
fcis-28118	77	14	network	network	NOUN
fcis-28118	77	15	:	:	PUNCT
fcis-28118	77	16	namely	namely	ADV
fcis-28118	77	17	ssc	ssc	NOUN
fcis-28118	77	18	convolution	convolution	NOUN
fcis-28118	77	19	block	block	NOUN
fcis-28118	77	20	(	(	PUNCT
fcis-28118	77	21	as	as	ADP
fcis-28118	77	22	figure3	figure3	NOUN
fcis-28118	77	23	)	)	PUNCT
fcis-28118	77	24	with	with	ADP
fcis-28118	77	25	single	single	ADJ
fcis-28118	77	26	scale	scale	NOUN
fcis-28118	77	27	convolution	convolution	NOUN
fcis-28118	77	28	block	block	NOUN
fcis-28118	77	29	ss	ss	PROPN
fcis-28118	77	30	-	-	NOUN
fcis-28118	77	31	cnn	cnn	PROPN
fcis-28118	77	32	(	(	PUNCT
fcis-28118	77	33	as	as	ADP
fcis-28118	77	34	figure4	figure4	ADJ
fcis-28118	77	35	)	)	PUNCT
fcis-28118	77	36	and	and	CCONJ
fcis-28118	77	37	msc	msc	PROPN
fcis-28118	77	38	convolution	convolution	NOUN
fcis-28118	77	39	block	block	NOUN
fcis-28118	77	40	with	with	ADP
fcis-28118	77	41	multi	multi	ADJ
fcis-28118	77	42	-	-	ADJ
fcis-28118	77	43	scale	scale	ADJ
fcis-28118	77	44	convolution	convolution	NOUN
fcis-28118	77	45	block	block	NOUN
fcis-28118	77	46	mscnn	mscnn	PROPN
fcis-28118	77	47	.	.	PUNCT
fcis-28118	78	1	by	by	ADP
fcis-28118	78	2	combining	combine	VERB
fcis-28118	78	3	the	the	DET
fcis-28118	78	4	two	two	NUM
fcis-28118	78	5	,	,	PUNCT
fcis-28118	78	6	this	this	DET
fcis-28118	78	7	paper	paper	NOUN
fcis-28118	78	8	refers	refer	VERB
fcis-28118	78	9	to	to	ADP
fcis-28118	78	10	it	it	PRON
fcis-28118	78	11	as	as	ADP
fcis-28118	78	12	the	the	DET
fcis-28118	78	13	smcnn	smcnn	NOUN
fcis-28118	78	14	network	network	NOUN
fcis-28118	78	15	,	,	PUNCT
fcis-28118	78	16	hereafter	hereafter	ADV
fcis-28118	78	17	denoted	denote	VERB
fcis-28118	78	18	as	as	ADP
fcis-28118	78	19	smc	smc	PROPN
fcis-28118	78	20	network	network	PROPN
fcis-28118	78	21	.	.	PUNCT
fcis-28118	79	1	the	the	DET
fcis-28118	79	2	data	datum	NOUN
fcis-28118	79	3	processed	process	VERB
fcis-28118	79	4	by	by	ADP
fcis-28118	79	5	the	the	DET
fcis-28118	79	6	smc	smc	PROPN
fcis-28118	79	7	network	network	PROPN
fcis-28118	79	8	exhibits	exhibit	VERB
fcis-28118	79	9	certain	certain	ADJ
fcis-28118	79	10	robustness	robustness	NOUN
fcis-28118	79	11	,	,	PUNCT
fcis-28118	79	12	which	which	PRON
fcis-28118	79	13	can	can	AUX
fcis-28118	79	14	improve	improve	VERB
fcis-28118	79	15	the	the	DET
fcis-28118	79	16	generalization	generalization	NOUN
fcis-28118	79	17	ability	ability	NOUN
fcis-28118	79	18	of	of	ADP
fcis-28118	79	19	the	the	DET
fcis-28118	79	20	model	model	NOUN
fcis-28118	79	21	.	.	PUNCT
fcis-28118	80	1	4.2	4.2	NUM
fcis-28118	80	2	.	.	PUNCT
fcis-28118	80	3	bidirectional	bidirectional	PROPN
fcis-28118	80	4	gru	gru	PROPN
fcis-28118	80	5	recurrent	recurrent	PROPN
fcis-28118	80	6	neural	neural	ADJ
fcis-28118	80	7	networks	network	NOUN
fcis-28118	80	8	(	(	PUNCT
fcis-28118	80	9	rnns	rnns	PROPN
fcis-28118	80	10	)	)	PUNCT
fcis-28118	80	11	have	have	AUX
fcis-28118	80	12	been	be	AUX
fcis-28118	80	13	widely	widely	ADV
fcis-28118	80	14	used	use	VERB
fcis-28118	80	15	in	in	ADP
fcis-28118	80	16	sequence	sequence	NOUN
fcis-28118	80	17	data	datum	NOUN
fcis-28118	80	18	processing	processing	NOUN
fcis-28118	80	19	.	.	PUNCT
fcis-28118	81	1	experiments	experiment	NOUN
fcis-28118	81	2	have	have	AUX
fcis-28118	81	3	utilized	utilize	VERB
fcis-28118	81	4	rnns	rnn	NOUN
fcis-28118	81	5	to	to	PART
fcis-28118	81	6	address	address	VERB
fcis-28118	81	7	the	the	DET
fcis-28118	81	8	time	time	NOUN
fcis-28118	81	9	dependence	dependence	NOUN
fcis-28118	81	10	of	of	ADP
fcis-28118	81	11	sequence	sequence	NOUN
fcis-28118	81	12	.	.	PUNCT
fcis-28118	82	1	however	however	ADV
fcis-28118	82	2	,	,	PUNCT
fcis-28118	82	3	rnns	rnns	PROPN
fcis-28118	82	4	may	may	AUX
fcis-28118	82	5	struggle	struggle	VERB
fcis-28118	82	6	to	to	PART
fcis-28118	82	7	deal	deal	VERB
fcis-28118	82	8	with	with	ADP
fcis-28118	82	9	long	long	ADJ
fcis-28118	82	10	-	-	PUNCT
fcis-28118	82	11	term	term	NOUN
fcis-28118	82	12	dependence	dependence	NOUN
fcis-28118	82	13	due	due	ADP
fcis-28118	82	14	to	to	ADP
fcis-28118	82	15	the	the	DET
fcis-28118	82	16	problem	problem	NOUN
fcis-28118	82	17	of	of	ADP
fcis-28118	82	18	gradient	gradient	ADJ
fcis-28118	82	19	explosion	explosion	NOUN
fcis-28118	82	20	and	and	CCONJ
fcis-28118	82	21	disappearance	disappearance	NOUN
fcis-28118	82	22	.	.	PUNCT
fcis-28118	83	1	in	in	ADP
fcis-28118	83	2	the	the	DET
fcis-28118	83	3	development	development	NOUN
fcis-28118	83	4	of	of	ADP
fcis-28118	83	5	sequence	sequence	NOUN
fcis-28118	83	6	data	datum	NOUN
fcis-28118	83	7	processing	processing	NOUN
fcis-28118	83	8	with	with	ADP
fcis-28118	83	9	rnns	rnn	NOUN
fcis-28118	83	10	,	,	PUNCT
fcis-28118	83	11	to	to	PART
fcis-28118	83	12	tackle	tackle	VERB
fcis-28118	83	13	the	the	DET
fcis-28118	83	14	issue	issue	NOUN
fcis-28118	83	15	of	of	ADP
fcis-28118	83	16	long	long	ADJ
fcis-28118	83	17	-	-	PUNCT
fcis-28118	83	18	term	term	NOUN
fcis-28118	83	19	dependence	dependence	NOUN
fcis-28118	83	20	,	,	PUNCT
fcis-28118	83	21	the	the	DET
fcis-28118	83	22	long	long	ADJ
fcis-28118	83	23	-	-	PUNCT
fcis-28118	83	24	short	short	ADJ
fcis-28118	83	25	memory	memory	NOUN
fcis-28118	83	26	networks	network	NOUN
fcis-28118	83	27	(	(	PUNCT
fcis-28118	83	28	lstm	lstm	NOUN
fcis-28118	83	29	)	)	PUNCT
fcis-28118	83	30	were	be	AUX
fcis-28118	83	31	introduced	introduce	VERB
fcis-28118	83	32	.	.	PUNCT
fcis-28118	84	1	as	as	ADP
fcis-28118	84	2	an	an	DET
fcis-28118	84	3	enhanced	enhanced	ADJ
fcis-28118	84	4	version	version	NOUN
fcis-28118	84	5	of	of	ADP
fcis-28118	84	6	rnns	rnn	NOUN
fcis-28118	84	7	,	,	PUNCT
fcis-28118	84	8	the	the	DET
fcis-28118	84	9	lstm	lstm	PROPN
fcis-28118	84	10	network	network	NOUN
fcis-28118	84	11	incorporates	incorporate	VERB
fcis-28118	84	12	additional	additional	ADJ
fcis-28118	84	13	memory	memory	NOUN
fcis-28118	84	14	units	unit	NOUN
fcis-28118	84	15	and	and	CCONJ
fcis-28118	84	16	gates	gate	NOUN
fcis-28118	84	17	.	.	PUNCT
fcis-28118	85	1	these	these	DET
fcis-28118	85	2	memory	memory	NOUN
fcis-28118	85	3	units	unit	NOUN
fcis-28118	85	4	and	and	CCONJ
fcis-28118	85	5	gates	gate	NOUN
fcis-28118	85	6	allow	allow	VERB
fcis-28118	85	7	for	for	ADP
fcis-28118	85	8	selective	selective	ADJ
fcis-28118	85	9	forgetting	forgetting	NOUN
fcis-28118	85	10	or	or	CCONJ
fcis-28118	85	11	storage	storage	NOUN
fcis-28118	85	12	,	,	PUNCT
fcis-28118	85	13	enabling	enable	VERB
fcis-28118	85	14	effective	effective	ADJ
fcis-28118	85	15	handling	handling	NOUN
fcis-28118	85	16	of	of	ADP
fcis-28118	85	17	long	long	ADJ
fcis-28118	85	18	-	-	PUNCT
fcis-28118	85	19	term	term	NOUN
fcis-28118	85	20	dependencies	dependency	NOUN
fcis-28118	85	21	.	.	PUNCT
fcis-28118	86	1	however	however	ADV
fcis-28118	86	2	,	,	PUNCT
fcis-28118	86	3	since	since	SCONJ
fcis-28118	86	4	the	the	DET
fcis-28118	86	5	design	design	NOUN
fcis-28118	86	6	of	of	ADP
fcis-28118	86	7	lstm	lstm	NOUN
fcis-28118	86	8	network	network	NOUN
fcis-28118	86	9	is	be	AUX
fcis-28118	86	10	more	more	ADV
fcis-28118	86	11	complex	complex	ADJ
fcis-28118	86	12	and	and	CCONJ
fcis-28118	86	13	the	the	DET
fcis-28118	86	14	parameters	parameter	NOUN
fcis-28118	86	15	used	use	VERB
fcis-28118	86	16	are	be	AUX
fcis-28118	86	17	very	very	ADV
fcis-28118	86	18	large	large	ADJ
fcis-28118	86	19	,	,	PUNCT
fcis-28118	86	20	this	this	DET
fcis-28118	86	21	paper	paper	NOUN
fcis-28118	86	22	introduces	introduce	NOUN
fcis-28118	86	23	gated	gate	VERB
fcis-28118	86	24	recurrent	recurrent	ADJ
fcis-28118	86	25	unit	unit	NOUN
fcis-28118	86	26	(	(	PUNCT
fcis-28118	86	27	gru	gru	NOUN
fcis-28118	86	28	)	)	PUNCT
fcis-28118	86	29	network	network	NOUN
fcis-28118	86	30	.	.	PUNCT
fcis-28118	87	1	as	as	ADP
fcis-28118	87	2	an	an	DET
fcis-28118	87	3	improvement	improvement	NOUN
fcis-28118	87	4	of	of	ADP
fcis-28118	87	5	lstm	lstm	ADJ
fcis-28118	87	6	network	network	NOUN
fcis-28118	87	7	,	,	PUNCT
fcis-28118	87	8	the	the	DET
fcis-28118	87	9	gru	gru	NOUN
fcis-28118	87	10	network	network	NOUN
fcis-28118	87	11	also	also	ADV
fcis-28118	87	12	features	feature	VERB
fcis-28118	87	13	memory	memory	NOUN
fcis-28118	87	14	units	unit	NOUN
fcis-28118	87	15	and	and	CCONJ
fcis-28118	87	16	gates	gate	NOUN
fcis-28118	87	17	.	.	PUNCT
fcis-28118	88	1	in	in	ADP
fcis-28118	88	2	comparison	comparison	NOUN
fcis-28118	88	3	to	to	ADP
fcis-28118	88	4	the	the	DET
fcis-28118	88	5	lstm	lstm	PROPN
fcis-28118	88	6	network	network	NOUN
fcis-28118	88	7	,	,	PUNCT
fcis-28118	88	8	the	the	DET
fcis-28118	88	9	gru	gru	NOUN
fcis-28118	88	10	network	network	NOUN
fcis-28118	88	11	integrates	integrate	VERB
fcis-28118	88	12	an	an	DET
fcis-28118	88	13	update	update	NOUN
fcis-28118	88	14	gate	gate	NOUN
fcis-28118	88	15	and	and	CCONJ
fcis-28118	88	16	a	a	DET
fcis-28118	88	17	reset	reset	ADJ
fcis-28118	88	18	gate	gate	NOUN
fcis-28118	88	19	,	,	PUNCT
fcis-28118	88	20	reduces	reduce	VERB
fcis-28118	88	21	certain	certain	ADJ
fcis-28118	88	22	26	26	NUM
fcis-28118	88	23	lstm	lstm	NOUN
fcis-28118	88	24	parameters	parameter	NOUN
fcis-28118	88	25	,	,	PUNCT
fcis-28118	88	26	optimizes	optimize	VERB
fcis-28118	88	27	its	its	PRON
fcis-28118	88	28	network	network	NOUN
fcis-28118	88	29	structure	structure	NOUN
fcis-28118	88	30	,	,	PUNCT
fcis-28118	88	31	simplifying	simplify	VERB
fcis-28118	88	32	the	the	DET
fcis-28118	88	33	network	network	NOUN
fcis-28118	88	34	structure	structure	NOUN
fcis-28118	88	35	to	to	ADP
fcis-28118	88	36	a	a	DET
fcis-28118	88	37	relatively	relatively	ADV
fcis-28118	88	38	simpler	simple	ADJ
fcis-28118	88	39	form	form	NOUN
fcis-28118	88	40	,	,	PUNCT
fcis-28118	88	41	and	and	CCONJ
fcis-28118	88	42	exhibits	exhibit	VERB
fcis-28118	88	43	better	well	ADJ
fcis-28118	88	44	generalization	generalization	NOUN
fcis-28118	88	45	ability	ability	NOUN
fcis-28118	88	46	with	with	ADP
fcis-28118	88	47	less	less	ADJ
fcis-28118	88	48	training	training	NOUN
fcis-28118	88	49	data	datum	NOUN
fcis-28118	88	50	,	,	PUNCT
fcis-28118	88	51	attributed	attribute	VERB
fcis-28118	88	52	to	to	ADP
fcis-28118	88	53	the	the	DET
fcis-28118	88	54	improvement	improvement	NOUN
fcis-28118	88	55	of	of	ADP
fcis-28118	88	56	its	its	PRON
fcis-28118	88	57	gate	gate	NOUN
fcis-28118	88	58	control	control	NOUN
fcis-28118	88	59	mechanism	mechanism	NOUN
fcis-28118	88	60	.	.	PUNCT
fcis-28118	89	1	figure	figure	NOUN
fcis-28118	89	2	5	5	NUM
fcis-28118	89	3	.	.	PUNCT
fcis-28118	89	4	gru	gru	NOUN
fcis-28118	89	5	cell	cell	NOUN
fcis-28118	89	6	.	.	PUNCT
fcis-28118	90	1	gru	gru	PROPN
fcis-28118	90	2	(	(	PUNCT
fcis-28118	90	3	as	as	ADP
fcis-28118	90	4	figure5	figure5	NOUN
fcis-28118	90	5	)	)	PUNCT
fcis-28118	90	6	contains	contain	VERB
fcis-28118	90	7	update	update	NOUN
fcis-28118	90	8	gate	gate	NOUN
fcis-28118	90	9	(	(	PUNCT
fcis-28118	90	10	tz	tz	PROPN
fcis-28118	90	11	)	)	PUNCT
fcis-28118	90	12	and	and	CCONJ
fcis-28118	90	13	reset	reset	NOUN
fcis-28118	90	14	gate	gate	NOUN
fcis-28118	90	15	(	(	PUNCT
fcis-28118	90	16	tr	tr	NOUN
fcis-28118	90	17	)	)	PUNCT
fcis-28118	90	18	.	.	PUNCT
fcis-28118	91	1	tz	tz	PROPN
fcis-28118	91	2	determines	determine	VERB
fcis-28118	91	3	the	the	DET
fcis-28118	91	4	amount	amount	NOUN
fcis-28118	91	5	of	of	ADP
fcis-28118	91	6	saving	save	VERB
fcis-28118	91	7	the	the	DET
fcis-28118	91	8	previous	previous	ADJ
fcis-28118	91	9	memory	memory	NOUN
fcis-28118	91	10	to	to	ADP
fcis-28118	91	11	the	the	DET
fcis-28118	91	12	current	current	ADJ
fcis-28118	91	13	memory	memory	NOUN
fcis-28118	91	14	unit	unit	NOUN
fcis-28118	91	15	,	,	PUNCT
fcis-28118	91	16	and	and	CCONJ
fcis-28118	91	17	tr	tr	NOUN
fcis-28118	91	18	determines	determine	VERB
fcis-28118	91	19	the	the	DET
fcis-28118	91	20	ratio	ratio	NOUN
fcis-28118	91	21	of	of	ADP
fcis-28118	91	22	new	new	ADJ
fcis-28118	91	23	information	information	NOUN
fcis-28118	91	24	to	to	ADP
fcis-28118	91	25	the	the	DET
fcis-28118	91	26	previous	previous	ADJ
fcis-28118	91	27	memory	memory	NOUN
fcis-28118	91	28	unit	unit	NOUN
fcis-28118	91	29	.	.	PUNCT
fcis-28118	92	1	these	these	DET
fcis-28118	92	2	two	two	NUM
fcis-28118	92	3	gates	gate	NOUN
fcis-28118	92	4	determine	determine	VERB
fcis-28118	92	5	what	what	DET
fcis-28118	92	6	information	information	NOUN
fcis-28118	92	7	can	can	AUX
fcis-28118	92	8	be	be	AUX
fcis-28118	92	9	used	use	VERB
fcis-28118	92	10	as	as	ADP
fcis-28118	92	11	the	the	DET
fcis-28118	92	12	output	output	NOUN
fcis-28118	92	13	of	of	ADP
fcis-28118	92	14	gru	gru	PROPN
fcis-28118	92	15	unit	unit	NOUN
fcis-28118	92	16	.	.	PUNCT
fcis-28118	93	1	the	the	DET
fcis-28118	93	2	special	special	ADJ
fcis-28118	93	3	feature	feature	NOUN
fcis-28118	93	4	of	of	ADP
fcis-28118	93	5	the	the	DET
fcis-28118	93	6	gated	gate	VERB
fcis-28118	93	7	unit	unit	NOUN
fcis-28118	93	8	is	be	AUX
fcis-28118	93	9	that	that	SCONJ
fcis-28118	93	10	it	it	PRON
fcis-28118	93	11	can	can	AUX
fcis-28118	93	12	store	store	VERB
fcis-28118	93	13	long	long	ADJ
fcis-28118	93	14	-	-	PUNCT
fcis-28118	93	15	term	term	NOUN
fcis-28118	93	16	sequence	sequence	NOUN
fcis-28118	93	17	information	information	NOUN
fcis-28118	93	18	,	,	PUNCT
fcis-28118	93	19	ensuring	ensure	VERB
fcis-28118	93	20	that	that	SCONJ
fcis-28118	93	21	the	the	DET
fcis-28118	93	22	information	information	NOUN
fcis-28118	93	23	will	will	AUX
fcis-28118	93	24	not	not	PART
fcis-28118	93	25	be	be	AUX
fcis-28118	93	26	erased	erase	VERB
fcis-28118	93	27	over	over	ADP
fcis-28118	93	28	time	time	NOUN
fcis-28118	93	29	.	.	PUNCT
fcis-28118	94	1	the	the	DET
fcis-28118	94	2	update	update	NOUN
fcis-28118	94	3	gate	gate	NOUN
fcis-28118	94	4	helps	help	VERB
fcis-28118	94	5	the	the	DET
fcis-28118	94	6	model	model	NOUN
fcis-28118	94	7	determine	determine	VERB
fcis-28118	94	8	how	how	SCONJ
fcis-28118	94	9	much	much	ADJ
fcis-28118	94	10	information	information	NOUN
fcis-28118	94	11	can	can	AUX
fcis-28118	94	12	be	be	AUX
fcis-28118	94	13	passed	pass	VERB
fcis-28118	94	14	on	on	ADP
fcis-28118	94	15	to	to	ADP
fcis-28118	94	16	the	the	DET
fcis-28118	94	17	next	next	ADJ
fcis-28118	94	18	step	step	NOUN
fcis-28118	94	19	,	,	PUNCT
fcis-28118	94	20	or	or	CCONJ
fcis-28118	94	21	how	how	SCONJ
fcis-28118	94	22	much	much	ADJ
fcis-28118	94	23	information	information	NOUN
fcis-28118	94	24	in	in	ADP
fcis-28118	94	25	the	the	DET
fcis-28118	94	26	previous	previous	ADJ
fcis-28118	94	27	memory	memory	NOUN
fcis-28118	94	28	unit	unit	NOUN
fcis-28118	94	29	needs	need	VERB
fcis-28118	94	30	to	to	PART
fcis-28118	94	31	be	be	AUX
fcis-28118	94	32	passed	pass	VERB
fcis-28118	94	33	on	on	ADP
fcis-28118	94	34	to	to	ADP
fcis-28118	94	35	the	the	DET
fcis-28118	94	36	future	future	NOUN
fcis-28118	94	37	.	.	PUNCT
fcis-28118	95	1	where	where	SCONJ
fcis-28118	95	2	tx	tx	PROPN
fcis-28118	95	3	is	be	AUX
fcis-28118	95	4	the	the	DET
fcis-28118	95	5	input	input	NOUN
fcis-28118	95	6	vector	vector	NOUN
fcis-28118	95	7	at	at	ADP
fcis-28118	95	8	time	time	NOUN
fcis-28118	95	9	t	t	PROPN
fcis-28118	95	10	,	,	PUNCT
fcis-28118	95	11	that	that	ADV
fcis-28118	95	12	is	is	ADV
fcis-28118	95	13	,	,	PUNCT
fcis-28118	95	14	the	the	DET
fcis-28118	95	15	t	t	PROPN
fcis-28118	95	16	component	component	NOUN
fcis-28118	95	17	of	of	ADP
fcis-28118	95	18	the	the	DET
fcis-28118	95	19	data	data	NOUN
fcis-28118	95	20	series	series	NOUN
fcis-28118	95	21	x	x	X
fcis-28118	95	22	,	,	PUNCT
fcis-28118	95	23	which	which	PRON
fcis-28118	95	24	is	be	AUX
fcis-28118	95	25	linearly	linearly	ADV
fcis-28118	95	26	transformed	transform	VERB
fcis-28118	95	27	(	(	PUNCT
fcis-28118	95	28	that	that	ADV
fcis-28118	95	29	is	is	ADV
fcis-28118	95	30	,	,	PUNCT
fcis-28118	95	31	multiplied	multiply	VERB
fcis-28118	95	32	by	by	ADP
fcis-28118	95	33	the	the	DET
fcis-28118	95	34	weight	weight	NOUN
fcis-28118	95	35	matrix	matrix	NOUN
fcis-28118	95	36	)	)	PUNCT
fcis-28118	95	37	,	,	PUNCT
fcis-28118	95	38			PROPN
fcis-28118	95	39	1h	1h	PROPN
fcis-28118	95	40	t	t	PROPN
fcis-28118	95	41			PROPN
fcis-28118	95	42	contains	contain	VERB
fcis-28118	95	43	the	the	DET
fcis-28118	95	44	information	information	NOUN
fcis-28118	95	45	of	of	ADP
fcis-28118	95	46	the	the	DET
fcis-28118	95	47	previous	previous	ADJ
fcis-28118	95	48	memory	memory	NOUN
fcis-28118	95	49	unit	unit	NOUN
fcis-28118	95	50	,	,	PUNCT
fcis-28118	95	51	that	that	ADV
fcis-28118	95	52	is	is	ADV
fcis-28118	95	53	,	,	PUNCT
fcis-28118	95	54	the	the	DET
fcis-28118	95	55	information	information	NOUN
fcis-28118	95	56	at	at	ADP
fcis-28118	95	57	time	time	NOUN
fcis-28118	95	58	1	1	NUM
fcis-28118	95	59	t	t	NOUN
fcis-28118	95	60			NOUN
fcis-28118	95	61	,	,	PUNCT
fcis-28118	95	62	which	which	PRON
fcis-28118	95	63	also	also	ADV
fcis-28118	95	64	goes	go	VERB
fcis-28118	95	65	through	through	ADP
fcis-28118	95	66	a	a	DET
fcis-28118	95	67	linear	linear	ADJ
fcis-28118	95	68	transformation	transformation	NOUN
fcis-28118	95	69	,	,	PUNCT
fcis-28118	95	70	and	and	CCONJ
fcis-28118	95	71	then	then	ADV
fcis-28118	95	72	combines	combine	VERB
fcis-28118	95	73	the	the	DET
fcis-28118	95	74	information	information	NOUN
fcis-28118	95	75	of	of	ADP
fcis-28118	95	76	these	these	DET
fcis-28118	95	77	two	two	NUM
fcis-28118	95	78	parts	part	NOUN
fcis-28118	95	79	and	and	CCONJ
fcis-28118	95	80	sends	send	VERB
fcis-28118	95	81	it	it	PRON
fcis-28118	95	82	into	into	ADP
fcis-28118	95	83	the	the	DET
fcis-28118	95	84	sigmoid	sigmoid	NOUN
fcis-28118	95	85	function	function	NOUN
fcis-28118	95	86	.	.	PUNCT
fcis-28118	96	1	to	to	PART
fcis-28118	96	2	map	map	VERB
fcis-28118	96	3	the	the	DET
fcis-28118	96	4	result	result	NOUN
fcis-28118	96	5	between	between	ADP
fcis-28118	96	6	0	0	NUM
fcis-28118	96	7	and	and	CCONJ
fcis-28118	96	8	1	1	NUM
fcis-28118	96	9	,	,	PUNCT
fcis-28118	96	10	here	here	ADV
fcis-28118	96	11	is	be	AUX
fcis-28118	96	12	the	the	DET
fcis-28118	96	13	formula	formula	NOUN
fcis-28118	96	14	for	for	ADP
fcis-28118	96	15	this	this	DET
fcis-28118	96	16	step	step	NOUN
fcis-28118	96	17	:	:	PUNCT
fcis-28118	96	18			PROPN
fcis-28118	96	19			PROPN
fcis-28118	96	20	(	(	PUNCT
fcis-28118	96	21	)	)	PUNCT
fcis-28118	96	22	(	(	PUNCT
fcis-28118	96	23	)	)	PUNCT
fcis-28118	96	24	1	1	NUM
fcis-28118	96	25	z	z	NOUN
fcis-28118	96	26	z	z	NOUN
fcis-28118	96	27	t	t	NOUN
fcis-28118	96	28	t	t	PROPN
fcis-28118	96	29	tz	tz	PROPN
fcis-28118	96	30	w	w	PROPN
fcis-28118	96	31	x	x	PUNCT
fcis-28118	96	32	u	u	NOUN
fcis-28118	96	33	h	h	PROPN
fcis-28118	96	34			ADJ
fcis-28118	96	35			X
fcis-28118	96	36	(	(	PUNCT
fcis-28118	96	37	3	3	X
fcis-28118	96	38	)	)	PUNCT
fcis-28118	96	39	the	the	DET
fcis-28118	96	40	reset	reset	NOUN
fcis-28118	96	41	gate	gate	NOUN
fcis-28118	96	42	determines	determine	VERB
fcis-28118	96	43	how	how	SCONJ
fcis-28118	96	44	much	much	ADJ
fcis-28118	96	45	information	information	NOUN
fcis-28118	96	46	needs	need	VERB
fcis-28118	96	47	to	to	PART
fcis-28118	96	48	be	be	AUX
fcis-28118	96	49	forgotten	forget	VERB
fcis-28118	96	50	in	in	ADP
fcis-28118	96	51	this	this	DET
fcis-28118	96	52	memory	memory	NOUN
fcis-28118	96	53	unit	unit	NOUN
fcis-28118	96	54	.	.	PUNCT
fcis-28118	97	1	the	the	DET
fcis-28118	97	2	same	same	ADJ
fcis-28118	97	3			PROPN
fcis-28118	97	4	1h	1h	PROPN
fcis-28118	97	5	t	t	PROPN
fcis-28118	97	6			PROPN
fcis-28118	97	7	and	and	CCONJ
fcis-28118	97	8	tx	tx	PROPN
fcis-28118	97	9	need	need	VERB
fcis-28118	97	10	to	to	PART
fcis-28118	97	11	undergo	undergo	VERB
fcis-28118	97	12	a	a	DET
fcis-28118	97	13	linear	linear	ADJ
fcis-28118	97	14	transformation	transformation	NOUN
fcis-28118	97	15	,	,	PUNCT
fcis-28118	97	16	and	and	CCONJ
fcis-28118	97	17	then	then	ADV
fcis-28118	97	18	send	send	VERB
fcis-28118	97	19	the	the	DET
fcis-28118	97	20	information	information	NOUN
fcis-28118	97	21	of	of	ADP
fcis-28118	97	22	both	both	DET
fcis-28118	97	23	parts	part	NOUN
fcis-28118	97	24	to	to	ADP
fcis-28118	97	25	the	the	DET
fcis-28118	97	26	sigmoid	sigmoid	NOUN
fcis-28118	97	27	function	function	NOUN
fcis-28118	97	28	output	output	NOUN
fcis-28118	97	29	:	:	PUNCT
fcis-28118	97	30			PROPN
fcis-28118	97	31			PROPN
fcis-28118	97	32	(	(	PUNCT
fcis-28118	97	33	)	)	PUNCT
fcis-28118	97	34	(	(	PUNCT
fcis-28118	97	35	)	)	PUNCT
fcis-28118	97	36	1	1	NUM
fcis-28118	97	37	r	r	NOUN
fcis-28118	97	38	r	r	NOUN
fcis-28118	97	39	t	t	NOUN
fcis-28118	97	40	t	t	NOUN
fcis-28118	97	41	tr	tr	NOUN
fcis-28118	97	42	w	w	PROPN
fcis-28118	97	43	x	x	SYM
fcis-28118	97	44	u	u	NOUN
fcis-28118	97	45	h	h	PROPN
fcis-28118	97	46			ADJ
fcis-28118	97	47			X
fcis-28118	97	48	(	(	PUNCT
fcis-28118	97	49	4	4	NUM
fcis-28118	97	50	)	)	PUNCT
fcis-28118	97	51	after	after	SCONJ
fcis-28118	97	52	the	the	DET
fcis-28118	97	53	reset	reset	NOUN
fcis-28118	97	54	gate	gate	NOUN
fcis-28118	97	55	is	be	AUX
fcis-28118	97	56	processed	process	VERB
fcis-28118	97	57	,	,	PUNCT
fcis-28118	97	58	the	the	DET
fcis-28118	97	59	new	new	ADJ
fcis-28118	97	60	memory	memory	NOUN
fcis-28118	97	61	information	information	NOUN
fcis-28118	97	62	will	will	AUX
fcis-28118	97	63	store	store	VERB
fcis-28118	97	64	some	some	DET
fcis-28118	97	65	relevant	relevant	ADJ
fcis-28118	97	66	information	information	NOUN
fcis-28118	97	67	in	in	ADP
fcis-28118	97	68	the	the	DET
fcis-28118	97	69	past	past	NOUN
fcis-28118	97	70	,	,	PUNCT
fcis-28118	97	71	and	and	CCONJ
fcis-28118	97	72	the	the	DET
fcis-28118	97	73	hadamard	hadamard	ADJ
fcis-28118	97	74	product	product	NOUN
fcis-28118	97	75	(	(	PUNCT
fcis-28118	97	76	that	that	PRON
fcis-28118	97	77	is	is	ADV
fcis-28118	97	78	,	,	PUNCT
fcis-28118	97	79	the	the	DET
fcis-28118	97	80	product	product	NOUN
fcis-28118	97	81	of	of	ADP
fcis-28118	97	82	corresponding	correspond	VERB
fcis-28118	97	83	elements	element	NOUN
fcis-28118	97	84	)	)	PUNCT
fcis-28118	97	85	is	be	AUX
fcis-28118	97	86	performed	perform	VERB
fcis-28118	97	87	between	between	ADP
fcis-28118	97	88	tr	tr	NOUN
fcis-28118	97	89	and	and	CCONJ
fcis-28118	97	90			PROPN
fcis-28118	97	91	1h	1h	PROPN
fcis-28118	97	92	t	t	PROPN
fcis-28118	97	93			PROPN
fcis-28118	97	94	,	,	PUNCT
fcis-28118	97	95	where	where	SCONJ
fcis-28118	97	96	the	the	DET
fcis-28118	97	97	hadamard	hadamard	ADJ
fcis-28118	97	98	product	product	NOUN
fcis-28118	97	99	will	will	AUX
fcis-28118	97	100	determine	determine	VERB
fcis-28118	97	101	the	the	DET
fcis-28118	97	102	information	information	NOUN
fcis-28118	97	103	that	that	PRON
fcis-28118	97	104	needs	need	VERB
fcis-28118	97	105	to	to	PART
fcis-28118	97	106	be	be	AUX
fcis-28118	97	107	retained	retain	VERB
fcis-28118	97	108	and	and	CCONJ
fcis-28118	97	109	forgotten	forget	VERB
fcis-28118	97	110	at	at	ADP
fcis-28118	97	111	this	this	DET
fcis-28118	97	112	moment	moment	NOUN
fcis-28118	97	113	,	,	PUNCT
fcis-28118	97	114	because	because	SCONJ
fcis-28118	97	115	the	the	DET
fcis-28118	97	116	reset	reset	NOUN
fcis-28118	97	117	gate	gate	NOUN
fcis-28118	97	118	vector	vector	NOUN
fcis-28118	97	119	is	be	AUX
fcis-28118	97	120	composed	compose	VERB
fcis-28118	97	121	of	of	ADP
fcis-28118	97	122	0	0	NUM
fcis-28118	97	123	to	to	PART
fcis-28118	97	124	1	1	NUM
fcis-28118	97	125	.	.	PUNCT
fcis-28118	98	1	so	so	ADV
fcis-28118	98	2	it	it	PRON
fcis-28118	98	3	will	will	AUX
fcis-28118	98	4	measure	measure	VERB
fcis-28118	98	5	the	the	DET
fcis-28118	98	6	value	value	NOUN
fcis-28118	98	7	of	of	ADP
fcis-28118	98	8	the	the	DET
fcis-28118	98	9	gated	gate	VERB
fcis-28118	98	10	open	open	ADJ
fcis-28118	98	11	,	,	PUNCT
fcis-28118	98	12	if	if	SCONJ
fcis-28118	98	13	the	the	DET
fcis-28118	98	14	element	element	NOUN
fcis-28118	98	15	gated	gate	VERB
fcis-28118	98	16	value	value	NOUN
fcis-28118	98	17	is	be	AUX
fcis-28118	98	18	0	0	NUM
fcis-28118	98	19	,	,	PUNCT
fcis-28118	98	20	it	it	PRON
fcis-28118	98	21	means	mean	VERB
fcis-28118	98	22	that	that	SCONJ
fcis-28118	98	23	the	the	DET
fcis-28118	98	24	information	information	NOUN
fcis-28118	98	25	carried	carry	VERB
fcis-28118	98	26	by	by	ADP
fcis-28118	98	27	the	the	DET
fcis-28118	98	28	element	element	NOUN
fcis-28118	98	29	will	will	AUX
fcis-28118	98	30	be	be	AUX
fcis-28118	98	31	completely	completely	ADV
fcis-28118	98	32	forgotten	forget	VERB
fcis-28118	98	33	:	:	PUNCT
fcis-28118	98	34			PROPN
fcis-28118	98	35	1tanht	1tanht	NOUN
fcis-28118	99	1	t	t	PROPN
fcis-28118	99	2	t	t	PROPN
fcis-28118	99	3	th	th	X
fcis-28118	99	4	wx	wx	PROPN
fcis-28118	99	5	r	r	NOUN
fcis-28118	99	6	uh	uh	INTJ
fcis-28118	99	7			ADJ
fcis-28118	99	8			NOUN
fcis-28118	99	9			PROPN
fcis-28118	99	10	(	(	PUNCT
fcis-28118	99	11	5	5	NUM
fcis-28118	99	12	)	)	PUNCT
fcis-28118	99	13	in	in	ADP
fcis-28118	99	14	the	the	DET
fcis-28118	99	15	final	final	ADJ
fcis-28118	99	16	memory	memory	NOUN
fcis-28118	99	17	moment	moment	NOUN
fcis-28118	99	18	of	of	ADP
fcis-28118	99	19	this	this	DET
fcis-28118	99	20	moment	moment	NOUN
fcis-28118	99	21	,	,	PUNCT
fcis-28118	99	22	the	the	DET
fcis-28118	99	23	update	update	NOUN
fcis-28118	99	24	gate	gate	NOUN
fcis-28118	99	25	determines	determine	VERB
fcis-28118	99	26	the	the	DET
fcis-28118	99	27	information	information	NOUN
fcis-28118	99	28	~	~	PUNCT
fcis-28118	99	29	(	(	PUNCT
fcis-28118	99	30	)	)	PUNCT
fcis-28118	99	31	h	h	NOUN
fcis-28118	99	32	t	t	NOUN
fcis-28118	99	33	of	of	ADP
fcis-28118	99	34	the	the	DET
fcis-28118	99	35	current	current	ADJ
fcis-28118	99	36	memory	memory	NOUN
fcis-28118	99	37	moment	moment	NOUN
fcis-28118	99	38	and	and	CCONJ
fcis-28118	99	39	the	the	DET
fcis-28118	99	40	information	information	NOUN
fcis-28118	99	41	that	that	PRON
fcis-28118	99	42	needs	need	VERB
fcis-28118	99	43	to	to	PART
fcis-28118	99	44	be	be	AUX
fcis-28118	99	45	collected	collect	VERB
fcis-28118	99	46	in	in	ADP
fcis-28118	99	47	the	the	DET
fcis-28118	99	48	previous	previous	ADJ
fcis-28118	99	49			PROPN
fcis-28118	99	50	1h	1h	PROPN
fcis-28118	99	51	t	t	PROPN
fcis-28118	99	52			PROPN
fcis-28118	99	53	moment	moment	NOUN
fcis-28118	99	54	.	.	PUNCT
fcis-28118	100	1	tz	tz	AUX
fcis-28118	100	2	activated	activate	VERB
fcis-28118	100	3	by	by	ADP
fcis-28118	100	4	the	the	DET
fcis-28118	100	5	activation	activation	NOUN
fcis-28118	100	6	function	function	NOUN
fcis-28118	100	7	will	will	AUX
fcis-28118	100	8	also	also	ADV
fcis-28118	100	9	control	control	VERB
fcis-28118	100	10	the	the	DET
fcis-28118	100	11	input	input	NOUN
fcis-28118	100	12	of	of	ADP
fcis-28118	100	13	information	information	NOUN
fcis-28118	100	14	in	in	ADP
fcis-28118	100	15	the	the	DET
fcis-28118	100	16	form	form	NOUN
fcis-28118	100	17	of	of	ADP
fcis-28118	100	18	gate	gate	PROPN
fcis-28118	100	19	control	control	PROPN
fcis-28118	100	20	.	.	PUNCT
fcis-28118	101	1	the	the	DET
fcis-28118	101	2	hadamard	hadamard	ADJ
fcis-28118	101	3	product	product	NOUN
fcis-28118	101	4	of	of	ADP
fcis-28118	101	5	tz	tz	PROPN
fcis-28118	101	6	and	and	CCONJ
fcis-28118	101	7			PROPN
fcis-28118	101	8	1h	1h	PROPN
fcis-28118	101	9	t	t	PROPN
fcis-28118	101	10			PROPN
fcis-28118	101	11	represents	represent	VERB
fcis-28118	101	12	the	the	DET
fcis-28118	101	13	final	final	ADJ
fcis-28118	101	14	memory	memory	NOUN
fcis-28118	101	15	information	information	NOUN
fcis-28118	101	16	retained	retain	VERB
fcis-28118	101	17	after	after	ADP
fcis-28118	101	18	screening	screen	VERB
fcis-28118	101	19	.	.	PUNCT
fcis-28118	102	1	this	this	DET
fcis-28118	102	2	information	information	NOUN
fcis-28118	102	3	,	,	PUNCT
fcis-28118	102	4	together	together	ADV
fcis-28118	102	5	with	with	ADP
fcis-28118	102	6	the	the	DET
fcis-28118	102	7	final	final	ADJ
fcis-28118	102	8	memory	memory	NOUN
fcis-28118	102	9	information	information	NOUN
fcis-28118	102	10	for	for	ADP
fcis-28118	102	11	the	the	DET
fcis-28118	102	12	current	current	ADJ
fcis-28118	102	13	memory	memory	NOUN
fcis-28118	102	14	moment	moment	NOUN
fcis-28118	102	15	(	(	PUNCT
fcis-28118	102	16	t	t	NOUN
fcis-28118	102	17	moment	moment	NOUN
fcis-28118	102	18	)	)	PUNCT
fcis-28118	102	19	,	,	PUNCT
fcis-28118	102	20	forms	form	VERB
fcis-28118	102	21	the	the	DET
fcis-28118	102	22	final	final	ADJ
fcis-28118	102	23	output	output	NOUN
fcis-28118	102	24	of	of	ADP
fcis-28118	102	25	the	the	DET
fcis-28118	102	26	gru	gru	PROPN
fcis-28118	102	27	unit	unit	NOUN
fcis-28118	102	28	.	.	PUNCT
fcis-28118	103	1	4.3	4.3	NUM
fcis-28118	103	2	.	.	X
fcis-28118	103	3	integrate	integrate	VERB
fcis-28118	103	4	deep	deep	ADJ
fcis-28118	103	5	learning	learning	NOUN
fcis-28118	103	6	models	model	NOUN
fcis-28118	103	7	the	the	DET
fcis-28118	103	8	smc	smc	NOUN
fcis-28118	103	9	-	-	ADJ
fcis-28118	103	10	bigru	bigru	ADJ
fcis-28118	103	11	network	network	NOUN
fcis-28118	103	12	used	use	VERB
fcis-28118	103	13	in	in	ADP
fcis-28118	103	14	this	this	DET
fcis-28118	103	15	work	work	NOUN
fcis-28118	103	16	can	can	AUX
fcis-28118	103	17	effectively	effectively	ADV
fcis-28118	103	18	predict	predict	VERB
fcis-28118	103	19	pss	pss	PROPN
fcis-28118	103	20	.	.	PUNCT
fcis-28118	104	1	this	this	DET
fcis-28118	104	2	network	network	NOUN
fcis-28118	104	3	combines	combine	VERB
fcis-28118	104	4	single	single	ADJ
fcis-28118	104	5	-	-	PUNCT
fcis-28118	104	6	scale	scale	NOUN
fcis-28118	104	7	convolutional	convolutional	ADJ
fcis-28118	104	8	blocks	block	NOUN
fcis-28118	104	9	,	,	PUNCT
fcis-28118	104	10	multi	multi	ADJ
fcis-28118	104	11	-	-	ADJ
fcis-28118	104	12	scale	scale	ADJ
fcis-28118	104	13	convolutional	convolutional	ADJ
fcis-28118	104	14	blocks	block	NOUN
fcis-28118	104	15	,	,	PUNCT
fcis-28118	104	16	residual	residual	ADJ
fcis-28118	104	17	networks	network	NOUN
fcis-28118	104	18	,	,	PUNCT
fcis-28118	104	19	and	and	CCONJ
fcis-28118	104	20	bidirectional	bidirectional	PROPN
fcis-28118	104	21	gru	gru	PROPN
fcis-28118	104	22	neural	neural	PROPN
fcis-28118	104	23	networks	network	NOUN
fcis-28118	104	24	to	to	PART
fcis-28118	104	25	extract	extract	VERB
fcis-28118	104	26	local	local	ADJ
fcis-28118	104	27	information	information	NOUN
fcis-28118	104	28	from	from	ADP
fcis-28118	104	29	input	input	NOUN
fcis-28118	104	30	data	datum	NOUN
fcis-28118	104	31	by	by	ADP
fcis-28118	104	32	combining	combine	VERB
fcis-28118	104	33	single	single	ADJ
fcis-28118	104	34	-	-	PUNCT
fcis-28118	104	35	scale	scale	NOUN
fcis-28118	104	36	and	and	CCONJ
fcis-28118	104	37	multi	multi	ADJ
fcis-28118	104	38	-	-	ADJ
fcis-28118	104	39	scale	scale	ADJ
fcis-28118	104	40	convolutional	convolutional	ADJ
fcis-28118	104	41	blocks	block	NOUN
fcis-28118	104	42	.	.	PUNCT
fcis-28118	105	1	after	after	ADP
fcis-28118	105	2	the	the	DET
fcis-28118	105	3	information	information	NOUN
fcis-28118	105	4	extraction	extraction	NOUN
fcis-28118	105	5	,	,	PUNCT
fcis-28118	105	6	data	datum	NOUN
fcis-28118	105	7	are	be	AUX
fcis-28118	105	8	input	input	NOUN
fcis-28118	105	9	to	to	ADP
fcis-28118	105	10	the	the	DET
fcis-28118	105	11	bi	bi	ADJ
fcis-28118	105	12	-	-	NOUN
fcis-28118	105	13	gru	gru	NOUN
fcis-28118	105	14	layer	layer	NOUN
fcis-28118	105	15	to	to	PART
fcis-28118	105	16	further	far	ADV
fcis-28118	105	17	extract	extract	VERB
fcis-28118	105	18	long	long	ADJ
fcis-28118	105	19	-	-	PUNCT
fcis-28118	105	20	range	range	NOUN
fcis-28118	105	21	sequence	sequence	NOUN
fcis-28118	105	22	data	datum	NOUN
fcis-28118	105	23	.	.	PUNCT
fcis-28118	106	1	this	this	PRON
fcis-28118	106	2	enables	enable	VERB
fcis-28118	106	3	the	the	DET
fcis-28118	106	4	extraction	extraction	NOUN
fcis-28118	106	5	of	of	ADP
fcis-28118	106	6	protein	protein	NOUN
fcis-28118	106	7	sequence	sequence	NOUN
fcis-28118	106	8	information	information	NOUN
fcis-28118	106	9	more	more	ADV
fcis-28118	106	10	completely	completely	ADV
fcis-28118	106	11	.	.	PUNCT
fcis-28118	107	1	5	5	X
fcis-28118	107	2	.	.	X
fcis-28118	107	3	experimental	experimental	ADJ
fcis-28118	107	4	design	design	NOUN
fcis-28118	107	5	since	since	SCONJ
fcis-28118	107	6	the	the	DET
fcis-28118	107	7	length	length	NOUN
fcis-28118	107	8	of	of	ADP
fcis-28118	107	9	each	each	DET
fcis-28118	107	10	sequence	sequence	NOUN
fcis-28118	107	11	in	in	ADP
fcis-28118	107	12	the	the	DET
fcis-28118	107	13	data	data	NOUN
fcis-28118	107	14	set	set	VERB
fcis-28118	107	15	is	be	AUX
fcis-28118	107	16	different	different	ADJ
fcis-28118	107	17	,	,	PUNCT
fcis-28118	107	18	each	each	DET
fcis-28118	107	19	sequence	sequence	NOUN
fcis-28118	107	20	is	be	AUX
fcis-28118	107	21	normalized	normalize	VERB
fcis-28118	107	22	to	to	ADP
fcis-28118	107	23	a	a	DET
fcis-28118	107	24	length	length	NOUN
fcis-28118	107	25	of	of	ADP
fcis-28118	107	26	700	700	NUM
fcis-28118	107	27	.	.	PUNCT
fcis-28118	108	1	if	if	SCONJ
fcis-28118	108	2	the	the	DET
fcis-28118	108	3	protein	protein	NOUN
fcis-28118	108	4	sequence	sequence	NOUN
fcis-28118	108	5	is	be	AUX
fcis-28118	108	6	shorter	short	ADJ
fcis-28118	108	7	than	than	ADP
fcis-28118	108	8	700	700	NUM
fcis-28118	108	9	,	,	PUNCT
fcis-28118	108	10	fill	fill	VERB
fcis-28118	108	11	it	it	PRON
fcis-28118	108	12	with	with	ADP
fcis-28118	108	13	zeros	zero	NOUN
fcis-28118	108	14	.	.	PUNCT
fcis-28118	109	1	if	if	SCONJ
fcis-28118	109	2	it	it	PRON
fcis-28118	109	3	exceeds	exceed	VERB
fcis-28118	109	4	700	700	NUM
fcis-28118	109	5	,	,	PUNCT
fcis-28118	109	6	it	it	PRON
fcis-28118	109	7	is	be	AUX
fcis-28118	109	8	truncated	truncate	VERB
fcis-28118	109	9	to	to	ADP
fcis-28118	109	10	700	700	NUM
fcis-28118	109	11	characters	character	NOUN
fcis-28118	109	12	.	.	PUNCT
fcis-28118	110	1	truncated	truncate	VERB
fcis-28118	110	2	characters	character	NOUN
fcis-28118	110	3	will	will	AUX
fcis-28118	110	4	continue	continue	VERB
fcis-28118	110	5	to	to	PART
fcis-28118	110	6	be	be	AUX
fcis-28118	110	7	filled	fill	VERB
fcis-28118	110	8	to	to	ADP
fcis-28118	110	9	700	700	NUM
fcis-28118	110	10	.	.	PUNCT
fcis-28118	111	1	to	to	PART
fcis-28118	111	2	better	well	ADV
fcis-28118	111	3	preserve	preserve	VERB
fcis-28118	111	4	amino	amino	NOUN
fcis-28118	111	5	acid	acid	NOUN
fcis-28118	111	6	characteristic	characteristic	ADJ
fcis-28118	111	7	information	information	NOUN
fcis-28118	111	8	,	,	PUNCT
fcis-28118	111	9	two	two	NUM
fcis-28118	111	10	data	datum	NOUN
fcis-28118	111	11	input	input	NOUN
fcis-28118	111	12	channels	channel	NOUN
fcis-28118	111	13	were	be	AUX
fcis-28118	111	14	set	set	VERB
fcis-28118	111	15	up	up	ADP
fcis-28118	111	16	in	in	ADP
fcis-28118	111	17	the	the	DET
fcis-28118	111	18	experiment	experiment	NOUN
fcis-28118	111	19	.	.	PUNCT
fcis-28118	112	1	channel	channel	PROPN
fcis-28118	112	2	one	one	NUM
fcis-28118	112	3	carried	carry	VERB
fcis-28118	112	4	the	the	DET
fcis-28118	112	5	orthogonal	orthogonal	ADJ
fcis-28118	112	6	coding	code	VERB
fcis-28118	112	7	information	information	NOUN
fcis-28118	112	8	of	of	ADP
fcis-28118	112	9	amino	amino	NOUN
fcis-28118	112	10	acid	acid	NOUN
fcis-28118	112	11	residues	residue	NOUN
fcis-28118	112	12	,	,	PUNCT
fcis-28118	112	13	and	and	CCONJ
fcis-28118	112	14	this	this	DET
fcis-28118	112	15	orthogonal	orthogonal	ADJ
fcis-28118	112	16	coding	code	VERB
fcis-28118	112	17	information	information	NOUN
fcis-28118	112	18	was	be	AUX
fcis-28118	112	19	recorded	record	VERB
fcis-28118	112	20	as	as	ADP
fcis-28118	112	21	vector	vector	NOUN
fcis-28118	112	22	.	.	PUNCT
fcis-28118	113	1	an	an	DET
fcis-28118	113	2	eigenmatrix	eigenmatrix	NOUN
fcis-28118	113	3	1p	1p	NOUN
fcis-28118	113	4	with	with	ADP
fcis-28118	113	5	the	the	DET
fcis-28118	113	6	shape	shape	NOUN
fcis-28118	113	7	of	of	ADP
fcis-28118	113	8	n*700	n*700	NOUN
fcis-28118	113	9	*	*	SYM
fcis-28118	113	10	21	21	NUM
fcis-28118	113	11	was	be	AUX
fcis-28118	113	12	obtained	obtain	VERB
fcis-28118	113	13	,	,	PUNCT
fcis-28118	113	14	composed	compose	VERB
fcis-28118	113	15	of	of	ADP
fcis-28118	113	16	20	20	NUM
fcis-28118	113	17	amino	amino	ADJ
fcis-28118	113	18	acids	acid	NOUN
fcis-28118	113	19	and	and	CCONJ
fcis-28118	113	20	the	the	DET
fcis-28118	113	21	assumed	assume	VERB
fcis-28118	113	22	unknown	unknown	ADJ
fcis-28118	113	23	amino	amino	NOUN
fcis-28118	113	24	acid	acid	NOUN
fcis-28118	113	25	x	x	X
fcis-28118	113	26	(	(	PUNCT
fcis-28118	113	27	n	n	PRON
fcis-28118	113	28	represents	represent	VERB
fcis-28118	113	29	the	the	DET
fcis-28118	113	30	sequence	sequence	NOUN
fcis-28118	113	31	quantity	quantity	NOUN
fcis-28118	113	32	)	)	PUNCT
fcis-28118	113	33	.	.	PUNCT
fcis-28118	114	1	channel	channel	PROPN
fcis-28118	114	2	2	2	NUM
fcis-28118	114	3	carries	carry	VERB
fcis-28118	114	4	amino	amino	NOUN
fcis-28118	114	5	acid	acid	NOUN
fcis-28118	114	6	pssm	pssm	NOUN
fcis-28118	114	7	matrix	matrix	NOUN
fcis-28118	114	8	information	information	NOUN
fcis-28118	114	9	and	and	CCONJ
fcis-28118	114	10	other	other	ADJ
fcis-28118	114	11	coding	code	VERB
fcis-28118	114	12	information	information	NOUN
fcis-28118	114	13	.	.	PUNCT
fcis-28118	115	1	in	in	ADP
fcis-28118	115	2	this	this	DET
fcis-28118	115	3	channel	channel	NOUN
fcis-28118	115	4	,	,	PUNCT
fcis-28118	115	5	the	the	DET
fcis-28118	115	6	pssm	pssm	PROPN
fcis-28118	115	7	code	code	NOUN
fcis-28118	115	8	of	of	ADP
fcis-28118	115	9	each	each	DET
fcis-28118	115	10	amino	amino	NOUN
fcis-28118	115	11	acid	acid	NOUN
fcis-28118	115	12	is	be	AUX
fcis-28118	115	13	recorded	record	VERB
fcis-28118	115	14	as	as	ADP
fcis-28118	115	15	a	a	DET
fcis-28118	115	16	20	20	NUM
fcis-28118	115	17	-	-	PUNCT
fcis-28118	115	18	dimensional	dimensional	ADJ
fcis-28118	115	19	vector	vector	NOUN
fcis-28118	115	20	2	2	NUM
fcis-28118	115	21	(	(	PUNCT
fcis-28118	115	22	1	1	NUM
fcis-28118	115	23	,	,	PUNCT
fcis-28118	115	24	,	,	PUNCT
fcis-28118	115	25	20)i	20)i	NUM
fcis-28118	115	26	iw	iw	INTJ
fcis-28118	115	27	p	p	NOUN
fcis-28118	115	28	p	p	NOUN
fcis-28118	115	29			NOUN
fcis-28118	115	30	.	.	PUNCT
fcis-28118	116	1	the	the	DET
fcis-28118	116	2	meaning	meaning	NOUN
fcis-28118	116	3	of	of	ADP
fcis-28118	116	4	1ip	1ip	NOUN
fcis-28118	116	5	can	can	AUX
fcis-28118	116	6	be	be	AUX
fcis-28118	116	7	seen	see	VERB
fcis-28118	116	8	from	from	ADP
fcis-28118	116	9	the	the	DET
fcis-28118	116	10	previous	previous	ADJ
fcis-28118	116	11	introduction	introduction	NOUN
fcis-28118	116	12	,	,	PUNCT
fcis-28118	116	13	where	where	SCONJ
fcis-28118	116	14	20ip	20ip	NOUN
fcis-28118	116	15	represents	represent	VERB
fcis-28118	116	16	the	the	DET
fcis-28118	116	17	probability	probability	NOUN
fcis-28118	116	18	that	that	SCONJ
fcis-28118	116	19	the	the	DET
fcis-28118	116	20	i	i	PROPN
fcis-28118	116	21	amino	amino	NOUN
fcis-28118	116	22	acid	acid	NOUN
fcis-28118	116	23	residue	residue	NOUN
fcis-28118	116	24	replaces	replace	VERB
fcis-28118	116	25	the	the	DET
fcis-28118	116	26	first	first	ADJ
fcis-28118	116	27	amino	amino	NOUN
fcis-28118	116	28	acid	acid	NOUN
fcis-28118	116	29	residue	residue	NOUN
fcis-28118	116	30	in	in	ADP
fcis-28118	116	31	the	the	DET
fcis-28118	116	32	protein	protein	NOUN
fcis-28118	116	33	sequence	sequence	NOUN
fcis-28118	116	34	.	.	PUNCT
fcis-28118	117	1	this	this	PRON
fcis-28118	117	2	holds	hold	VERB
fcis-28118	117	3	true	true	ADJ
fcis-28118	117	4	for	for	ADP
fcis-28118	117	5	different	different	ADJ
fcis-28118	117	6	types	type	NOUN
fcis-28118	117	7	of	of	ADP
fcis-28118	117	8	input	input	NOUN
fcis-28118	117	9	data	datum	NOUN
fcis-28118	117	10	as	as	ADV
fcis-28118	117	11	well	well	ADV
fcis-28118	117	12	.	.	PUNCT
fcis-28118	118	1	to	to	PART
fcis-28118	118	2	maintain	maintain	VERB
fcis-28118	118	3	consistent	consistent	ADJ
fcis-28118	118	4	data	datum	NOUN
fcis-28118	118	5	,	,	PUNCT
fcis-28118	118	6	feature	feature	NOUN
fcis-28118	118	7	types	type	NOUN
fcis-28118	118	8	when	when	SCONJ
fcis-28118	118	9	inputting	inputte	VERB
fcis-28118	118	10	into	into	ADP
fcis-28118	118	11	the	the	DET
fcis-28118	118	12	smc	smc	PROPN
fcis-28118	118	13	layer	layer	NOUN
fcis-28118	118	14	,	,	PUNCT
fcis-28118	118	15	vector	vector	NOUN
fcis-28118	118	16	mapping	mapping	NOUN
fcis-28118	118	17	is	be	AUX
fcis-28118	118	18	carried	carry	VERB
fcis-28118	118	19	out	out	ADP
fcis-28118	118	20	on	on	ADP
fcis-28118	118	21	the	the	DET
fcis-28118	118	22	data	datum	NOUN
fcis-28118	118	23	from	from	ADP
fcis-28118	118	24	input	input	NOUN
fcis-28118	118	25	channel	channel	NOUN
fcis-28118	118	26	1	1	NUM
fcis-28118	118	27	at	at	ADP
fcis-28118	118	28	the	the	DET
fcis-28118	118	29	feature	feature	NOUN
fcis-28118	118	30	fusion	fusion	NOUN
fcis-28118	118	31	layer	layer	NOUN
fcis-28118	118	32	,	,	PUNCT
fcis-28118	118	33	transforming	transform	VERB
fcis-28118	118	34	sparse	sparse	ADJ
fcis-28118	118	35	data	datum	NOUN
fcis-28118	118	36	into	into	ADP
fcis-28118	118	37	dense	dense	ADJ
fcis-28118	118	38	data	datum	NOUN
fcis-28118	118	39	.	.	PUNCT
fcis-28118	119	1	the	the	DET
fcis-28118	119	2	resulting	result	VERB
fcis-28118	119	3	dense	dense	ADJ
fcis-28118	119	4	data	datum	NOUN
fcis-28118	119	5	is	be	AUX
fcis-28118	119	6	then	then	ADV
fcis-28118	119	7	fused	fuse	VERB
fcis-28118	119	8	with	with	ADP
fcis-28118	119	9	the	the	DET
fcis-28118	119	10	data	datum	NOUN
fcis-28118	119	11	from	from	ADP
fcis-28118	119	12	channel	channel	NOUN
fcis-28118	119	13	2	2	NUM
fcis-28118	119	14	.	.	PUNCT
fcis-28118	119	15	from	from	ADP
fcis-28118	119	16	the	the	DET
fcis-28118	119	17	previous	previous	ADJ
fcis-28118	119	18	section	section	NOUN
fcis-28118	119	19	,	,	PUNCT
fcis-28118	119	20	it	it	PRON
fcis-28118	119	21	can	can	AUX
fcis-28118	119	22	be	be	AUX
fcis-28118	119	23	observed	observe	VERB
fcis-28118	119	24	that	that	SCONJ
fcis-28118	119	25	the	the	DET
fcis-28118	119	26	smc	smc	PROPN
fcis-28118	119	27	layer	layer	NOUN
fcis-28118	119	28	contains	contain	VERB
fcis-28118	119	29	two	two	NUM
fcis-28118	119	30	convolutional	convolutional	ADJ
fcis-28118	119	31	blocks	block	NOUN
fcis-28118	119	32	,	,	PUNCT
fcis-28118	119	33	namely	namely	ADV
fcis-28118	119	34	the	the	DET
fcis-28118	119	35	msc	msc	PROPN
fcis-28118	119	36	convolution	convolution	NOUN
fcis-28118	119	37	block	block	NOUN
fcis-28118	119	38	and	and	CCONJ
fcis-28118	119	39	the	the	DET
fcis-28118	119	40	ssc	ssc	NOUN
fcis-28118	119	41	convolution	convolution	NOUN
fcis-28118	119	42	block	block	NOUN
fcis-28118	119	43	.	.	PUNCT
fcis-28118	120	1	the	the	DET
fcis-28118	120	2	msc	msc	PROPN
fcis-28118	120	3	convolution	convolution	PROPN
fcis-28118	120	4	block	block	NOUN
fcis-28118	120	5	consists	consist	VERB
fcis-28118	120	6	of	of	ADP
fcis-28118	120	7	a	a	DET
fcis-28118	120	8	convolutional	convolutional	ADJ
fcis-28118	120	9	layer	layer	NOUN
fcis-28118	120	10	,	,	PUNCT
fcis-28118	120	11	activation	activation	NOUN
fcis-28118	120	12	function	function	NOUN
fcis-28118	120	13	,	,	PUNCT
fcis-28118	120	14	and	and	CCONJ
fcis-28118	120	15	batch	batch	VERB
fcis-28118	120	16	standardization	standardization	NOUN
fcis-28118	120	17	processing	processing	NOUN
fcis-28118	120	18	(	(	PUNCT
fcis-28118	120	19	bn	bn	INTJ
fcis-28118	120	20	layer	layer	NOUN
fcis-28118	120	21	)	)	PUNCT
fcis-28118	120	22	.	.	PUNCT
fcis-28118	121	1	to	to	PART
fcis-28118	121	2	capture	capture	VERB
fcis-28118	121	3	information	information	NOUN
fcis-28118	121	4	from	from	ADP
fcis-28118	121	5	different	different	ADJ
fcis-28118	121	6	scales	scale	NOUN
fcis-28118	121	7	of	of	ADP
fcis-28118	121	8	the	the	DET
fcis-28118	121	9	input	input	NOUN
fcis-28118	121	10	sequence	sequence	NOUN
fcis-28118	121	11	more	more	ADV
fcis-28118	121	12	effectively	effectively	ADV
fcis-28118	121	13	,	,	PUNCT
fcis-28118	121	14	three	three	NUM
fcis-28118	121	15	convolutional	convolutional	ADJ
fcis-28118	121	16	kernels	kernel	NOUN
fcis-28118	121	17	of	of	ADP
fcis-28118	121	18	different	different	ADJ
fcis-28118	121	19	sizes	size	NOUN
fcis-28118	121	20	are	be	AUX
fcis-28118	121	21	set	set	VERB
fcis-28118	121	22	in	in	ADP
fcis-28118	121	23	the	the	DET
fcis-28118	121	24	convolutional	convolutional	ADJ
fcis-28118	121	25	layer	layer	NOUN
fcis-28118	121	26	.	.	PUNCT
fcis-28118	122	1	additionally	additionally	ADV
fcis-28118	122	2	,	,	PUNCT
fcis-28118	122	3	1	1	NUM
fcis-28118	122	4	(	(	PUNCT
fcis-28118	122	5	0	0	NUM
fcis-28118	122	6	,	,	PUNCT
fcis-28118	122	7	,	,	PUNCT
fcis-28118	122	8	0	0	NUM
fcis-28118	122	9	,	,	PUNCT
fcis-28118	122	10	,	,	PUNCT
fcis-28118	122	11	0	0	NUM
fcis-28118	122	12	,	,	PUNCT
fcis-28118	122	13	,	,	PUNCT
fcis-28118	122	14	0)w	0)w	NOUN
fcis-28118	122	15	i	i	NOUN
fcis-28118	122	16			NOUN
fcis-28118	122	17			NOUN
fcis-28118	122	18	27	27	NUM
fcis-28118	122	19	to	to	PART
fcis-28118	122	20	maintain	maintain	VERB
fcis-28118	122	21	the	the	DET
fcis-28118	122	22	length	length	NOUN
fcis-28118	122	23	of	of	ADP
fcis-28118	122	24	the	the	DET
fcis-28118	122	25	input	input	NOUN
fcis-28118	122	26	and	and	CCONJ
fcis-28118	122	27	output	output	NOUN
fcis-28118	122	28	sequences	sequence	NOUN
fcis-28118	122	29	,	,	PUNCT
fcis-28118	122	30	the	the	DET
fcis-28118	122	31	`	`	PUNCT
fcis-28118	122	32	`	`	PUNCT
fcis-28118	122	33	same	same	ADJ
fcis-28118	122	34	''	''	PUNCT
fcis-28118	122	35	mode	mode	NOUN
fcis-28118	122	36	is	be	AUX
fcis-28118	122	37	used	use	VERB
fcis-28118	122	38	for	for	ADP
fcis-28118	122	39	padding	padding	NOUN
fcis-28118	122	40	.	.	PUNCT
fcis-28118	123	1	subsequently	subsequently	ADV
fcis-28118	123	2	,	,	PUNCT
fcis-28118	123	3	the	the	DET
fcis-28118	123	4	relu	relu	NOUN
fcis-28118	123	5	activation	activation	NOUN
fcis-28118	123	6	function	function	NOUN
fcis-28118	123	7	is	be	AUX
fcis-28118	123	8	applied	apply	VERB
fcis-28118	123	9	to	to	PART
fcis-28118	123	10	nonlinearly	nonlinearly	ADV
fcis-28118	123	11	correct	correct	VERB
fcis-28118	123	12	the	the	DET
fcis-28118	123	13	output	output	NOUN
fcis-28118	123	14	of	of	ADP
fcis-28118	123	15	the	the	DET
fcis-28118	123	16	previous	previous	ADJ
fcis-28118	123	17	convolutional	convolutional	ADJ
fcis-28118	123	18	layer	layer	NOUN
fcis-28118	123	19	.	.	PUNCT
fcis-28118	124	1	batch	batch	NOUN
fcis-28118	124	2	standardization	standardization	NOUN
fcis-28118	124	3	processing	processing	NOUN
fcis-28118	124	4	(	(	PUNCT
fcis-28118	124	5	i.e.	i.e.	X
fcis-28118	124	6	,	,	PUNCT
fcis-28118	124	7	bn	bn	NOUN
fcis-28118	124	8	layer	layer	NOUN
fcis-28118	124	9	)	)	PUNCT
fcis-28118	124	10	is	be	AUX
fcis-28118	124	11	utilized	utilize	VERB
fcis-28118	124	12	to	to	PART
fcis-28118	124	13	stabilize	stabilize	VERB
fcis-28118	124	14	the	the	DET
fcis-28118	124	15	data	datum	NOUN
fcis-28118	124	16	,	,	PUNCT
fcis-28118	124	17	and	and	CCONJ
fcis-28118	124	18	the	the	DET
fcis-28118	124	19	outputs	output	NOUN
fcis-28118	124	20	of	of	ADP
fcis-28118	124	21	the	the	DET
fcis-28118	124	22	three	three	NUM
fcis-28118	124	23	convolutional	convolutional	ADJ
fcis-28118	124	24	kernels	kernel	NOUN
fcis-28118	124	25	of	of	ADP
fcis-28118	124	26	different	different	ADJ
fcis-28118	124	27	sizes	size	NOUN
fcis-28118	124	28	are	be	AUX
fcis-28118	124	29	merged	merge	VERB
fcis-28118	124	30	with	with	ADP
fcis-28118	124	31	the	the	DET
fcis-28118	124	32	original	original	ADJ
fcis-28118	124	33	output	output	NOUN
fcis-28118	124	34	to	to	PART
fcis-28118	124	35	form	form	VERB
fcis-28118	124	36	a	a	DET
fcis-28118	124	37	richer	rich	ADJ
fcis-28118	124	38	feature	feature	NOUN
fcis-28118	124	39	set	set	NOUN
fcis-28118	124	40	.	.	PUNCT
fcis-28118	125	1	finally	finally	ADV
fcis-28118	125	2	,	,	PUNCT
fcis-28118	125	3	a	a	DET
fcis-28118	125	4	dropout	dropout	NOUN
fcis-28118	125	5	layer	layer	NOUN
fcis-28118	125	6	with	with	ADP
fcis-28118	125	7	a	a	DET
fcis-28118	125	8	dropout	dropout	NOUN
fcis-28118	125	9	rate	rate	NOUN
fcis-28118	125	10	of	of	ADP
fcis-28118	125	11	0.5	0.5	NUM
fcis-28118	125	12	is	be	AUX
fcis-28118	125	13	added	add	VERB
fcis-28118	125	14	after	after	ADP
fcis-28118	125	15	the	the	DET
fcis-28118	125	16	feature	feature	NOUN
fcis-28118	125	17	fusion	fusion	NOUN
fcis-28118	125	18	layer	layer	NOUN
fcis-28118	125	19	to	to	PART
fcis-28118	125	20	prevent	prevent	VERB
fcis-28118	125	21	overfitting	overfitting	NOUN
fcis-28118	125	22	.	.	PUNCT
fcis-28118	126	1	the	the	DET
fcis-28118	126	2	ssc	ssc	NOUN
fcis-28118	126	3	convolutional	convolutional	ADJ
fcis-28118	126	4	block	block	NOUN
fcis-28118	126	5	also	also	ADV
fcis-28118	126	6	includes	include	VERB
fcis-28118	126	7	a	a	DET
fcis-28118	126	8	convolutional	convolutional	ADJ
fcis-28118	126	9	layer	layer	NOUN
fcis-28118	126	10	,	,	PUNCT
fcis-28118	126	11	activation	activation	NOUN
fcis-28118	126	12	function	function	NOUN
fcis-28118	126	13	,	,	PUNCT
fcis-28118	126	14	batch	batch	VERB
fcis-28118	126	15	normalization	normalization	NOUN
fcis-28118	126	16	processing	processing	NOUN
fcis-28118	126	17	(	(	PUNCT
fcis-28118	126	18	bn	bn	INTJ
fcis-28118	126	19	layer	layer	NOUN
fcis-28118	126	20	)	)	PUNCT
fcis-28118	126	21	,	,	PUNCT
fcis-28118	126	22	and	and	CCONJ
fcis-28118	126	23	dropout	dropout	NOUN
fcis-28118	126	24	layer	layer	NOUN
fcis-28118	126	25	.	.	PUNCT
fcis-28118	127	1	the	the	DET
fcis-28118	127	2	convolutional	convolutional	ADJ
fcis-28118	127	3	layer	layer	NOUN
fcis-28118	127	4	employs	employ	VERB
fcis-28118	127	5	a	a	DET
fcis-28118	127	6	one	one	NUM
fcis-28118	127	7	-	-	PUNCT
fcis-28118	127	8	dimensional	dimensional	ADJ
fcis-28118	127	9	convolutional	convolutional	ADJ
fcis-28118	127	10	layer	layer	NOUN
fcis-28118	127	11	to	to	PART
fcis-28118	127	12	capture	capture	VERB
fcis-28118	127	13	local	local	ADJ
fcis-28118	127	14	information	information	NOUN
fcis-28118	127	15	from	from	ADP
fcis-28118	127	16	the	the	DET
fcis-28118	127	17	protein	protein	NOUN
fcis-28118	127	18	sequence	sequence	NOUN
fcis-28118	127	19	,	,	PUNCT
fcis-28118	127	20	and	and	CCONJ
fcis-28118	127	21	the	the	DET
fcis-28118	127	22	`	`	PUNCT
fcis-28118	127	23	`	`	PUNCT
fcis-28118	127	24	same	same	ADJ
fcis-28118	127	25	''	''	PUNCT
fcis-28118	127	26	mode	mode	NOUN
fcis-28118	127	27	is	be	AUX
fcis-28118	127	28	used	use	VERB
fcis-28118	127	29	for	for	ADP
fcis-28118	127	30	padding	padding	NOUN
fcis-28118	127	31	.	.	PUNCT
fcis-28118	128	1	following	follow	VERB
fcis-28118	128	2	this	this	PRON
fcis-28118	128	3	,	,	PUNCT
fcis-28118	128	4	the	the	DET
fcis-28118	128	5	relu	relu	NOUN
fcis-28118	128	6	activation	activation	NOUN
fcis-28118	128	7	function	function	NOUN
fcis-28118	128	8	and	and	CCONJ
fcis-28118	128	9	batch	batch	VERB
fcis-28118	128	10	normalization	normalization	NOUN
fcis-28118	128	11	processing	processing	NOUN
fcis-28118	128	12	,	,	PUNCT
fcis-28118	128	13	similar	similar	ADJ
fcis-28118	128	14	to	to	ADP
fcis-28118	128	15	the	the	DET
fcis-28118	128	16	msc	msc	PROPN
fcis-28118	128	17	convolution	convolution	PROPN
fcis-28118	128	18	kernel	kernel	PROPN
fcis-28118	128	19	,	,	PUNCT
fcis-28118	128	20	are	be	AUX
fcis-28118	128	21	applied	apply	VERB
fcis-28118	128	22	.	.	PUNCT
fcis-28118	129	1	a	a	DET
fcis-28118	129	2	dropout	dropout	NOUN
fcis-28118	129	3	layer	layer	NOUN
fcis-28118	129	4	with	with	ADP
fcis-28118	129	5	a	a	DET
fcis-28118	129	6	dropout	dropout	NOUN
fcis-28118	129	7	rate	rate	NOUN
fcis-28118	129	8	of	of	ADP
fcis-28118	129	9	0.5	0.5	NUM
fcis-28118	129	10	is	be	AUX
fcis-28118	129	11	added	add	VERB
fcis-28118	129	12	to	to	PART
fcis-28118	129	13	prevent	prevent	VERB
fcis-28118	129	14	overfitting	overfitting	NOUN
fcis-28118	129	15	.	.	PUNCT
fcis-28118	130	1	in	in	ADP
fcis-28118	130	2	contrast	contrast	NOUN
fcis-28118	130	3	to	to	ADP
fcis-28118	130	4	the	the	DET
fcis-28118	130	5	msc	msc	PROPN
fcis-28118	130	6	convolution	convolution	PROPN
fcis-28118	130	7	kernel	kernel	PROPN
fcis-28118	130	8	,	,	PUNCT
fcis-28118	130	9	a	a	DET
fcis-28118	130	10	residual	residual	ADJ
fcis-28118	130	11	connection	connection	NOUN
fcis-28118	130	12	layer	layer	NOUN
fcis-28118	130	13	is	be	AUX
fcis-28118	130	14	included	include	VERB
fcis-28118	130	15	in	in	ADP
fcis-28118	130	16	order	order	NOUN
fcis-28118	130	17	to	to	PART
fcis-28118	130	18	mitigate	mitigate	VERB
fcis-28118	130	19	the	the	DET
fcis-28118	130	20	problem	problem	NOUN
fcis-28118	130	21	of	of	ADP
fcis-28118	130	22	gradient	gradient	ADJ
fcis-28118	130	23	disappearance	disappearance	NOUN
fcis-28118	130	24	.	.	PUNCT
fcis-28118	131	1	this	this	DET
fcis-28118	131	2	layer	layer	NOUN
fcis-28118	131	3	can	can	AUX
fcis-28118	131	4	retain	retain	VERB
fcis-28118	131	5	initial	initial	ADJ
fcis-28118	131	6	data	datum	NOUN
fcis-28118	131	7	while	while	SCONJ
fcis-28118	131	8	processing	process	VERB
fcis-28118	131	9	new	new	ADJ
fcis-28118	131	10	information	information	NOUN
fcis-28118	131	11	,	,	PUNCT
fcis-28118	131	12	thereby	thereby	ADV
fcis-28118	131	13	enhancing	enhance	VERB
fcis-28118	131	14	the	the	DET
fcis-28118	131	15	completeness	completeness	NOUN
fcis-28118	131	16	of	of	ADP
fcis-28118	131	17	the	the	DET
fcis-28118	131	18	sequence	sequence	NOUN
fcis-28118	131	19	information	information	NOUN
fcis-28118	131	20	obtained	obtain	VERB
fcis-28118	131	21	by	by	ADP
fcis-28118	131	22	the	the	DET
fcis-28118	131	23	network	network	NOUN
fcis-28118	131	24	.	.	PUNCT
fcis-28118	132	1	by	by	ADP
fcis-28118	132	2	employing	employ	VERB
fcis-28118	132	3	two	two	NUM
fcis-28118	132	4	different	different	ADJ
fcis-28118	132	5	connection	connection	NOUN
fcis-28118	132	6	methods	method	NOUN
fcis-28118	132	7	,	,	PUNCT
fcis-28118	132	8	the	the	DET
fcis-28118	132	9	sequence	sequence	NOUN
fcis-28118	132	10	feature	feature	NOUN
fcis-28118	132	11	information	information	NOUN
fcis-28118	132	12	can	can	AUX
fcis-28118	132	13	be	be	AUX
fcis-28118	132	14	extracted	extract	VERB
fcis-28118	132	15	more	more	ADV
fcis-28118	132	16	comprehensively	comprehensively	ADV
fcis-28118	132	17	.	.	PUNCT
fcis-28118	133	1	once	once	SCONJ
fcis-28118	133	2	the	the	DET
fcis-28118	133	3	data	datum	NOUN
fcis-28118	133	4	extracted	extract	VERB
fcis-28118	133	5	by	by	ADP
fcis-28118	133	6	the	the	DET
fcis-28118	133	7	smc	smc	PROPN
fcis-28118	133	8	network	network	NOUN
fcis-28118	133	9	is	be	AUX
fcis-28118	133	10	input	input	VERB
fcis-28118	133	11	into	into	ADP
fcis-28118	133	12	the	the	DET
fcis-28118	133	13	bi	bi	PROPN
fcis-28118	133	14	-	-	PROPN
fcis-28118	133	15	gru	gru	NOUN
fcis-28118	133	16	network	network	NOUN
fcis-28118	133	17	,	,	PUNCT
fcis-28118	133	18	the	the	DET
fcis-28118	133	19	bi	bi	PROPN
fcis-28118	133	20	-	-	PROPN
fcis-28118	133	21	gru	gru	NOUN
fcis-28118	133	22	network	network	NOUN
fcis-28118	133	23	extracts	extract	NOUN
fcis-28118	133	24	feature	feature	NOUN
fcis-28118	133	25	from	from	ADP
fcis-28118	133	26	the	the	DET
fcis-28118	133	27	input	input	NOUN
fcis-28118	133	28	data	datum	NOUN
fcis-28118	133	29	in	in	ADP
fcis-28118	133	30	both	both	CCONJ
fcis-28118	133	31	forward	forward	ADJ
fcis-28118	133	32	and	and	CCONJ
fcis-28118	133	33	backward	backward	ADJ
fcis-28118	133	34	directions	direction	NOUN
fcis-28118	133	35	,	,	PUNCT
fcis-28118	133	36	and	and	CCONJ
fcis-28118	133	37	subsequently	subsequently	ADV
fcis-28118	133	38	concatenates	concatenate	VERB
fcis-28118	133	39	the	the	DET
fcis-28118	133	40	feature	feature	NOUN
fcis-28118	133	41	outputs	output	NOUN
fcis-28118	133	42	obtained	obtain	VERB
fcis-28118	133	43	from	from	ADP
fcis-28118	133	44	both	both	DET
fcis-28118	133	45	directions	direction	NOUN
fcis-28118	133	46	.	.	PUNCT
fcis-28118	134	1	additionally	additionally	ADV
fcis-28118	134	2	,	,	PUNCT
fcis-28118	134	3	to	to	PART
fcis-28118	134	4	reduce	reduce	VERB
fcis-28118	134	5	overfitting	overfitting	NOUN
fcis-28118	134	6	,	,	PUNCT
fcis-28118	134	7	an	an	DET
fcis-28118	134	8	l2	l2	NOUN
fcis-28118	134	9	regularization	regularization	NOUN
fcis-28118	134	10	term	term	NOUN
fcis-28118	134	11	is	be	AUX
fcis-28118	134	12	applied	apply	VERB
fcis-28118	134	13	in	in	ADP
fcis-28118	134	14	the	the	DET
fcis-28118	134	15	bi	bi	PROPN
fcis-28118	134	16	-	-	PROPN
fcis-28118	134	17	gru	gru	NOUN
fcis-28118	134	18	layer	layer	NOUN
fcis-28118	134	19	.	.	PUNCT
fcis-28118	135	1	in	in	ADP
fcis-28118	135	2	this	this	DET
fcis-28118	135	3	model	model	NOUN
fcis-28118	135	4	,	,	PUNCT
fcis-28118	135	5	a	a	DET
fcis-28118	135	6	regularization	regularization	NOUN
fcis-28118	135	7	parameter	parameter	NOUN
fcis-28118	135	8	with	with	ADP
fcis-28118	135	9	a	a	DET
fcis-28118	135	10	value	value	NOUN
fcis-28118	135	11	of	of	ADP
fcis-28118	135	12	0.2	0.2	NUM
fcis-28118	135	13	is	be	AUX
fcis-28118	135	14	chosen	choose	VERB
fcis-28118	135	15	.	.	PUNCT
fcis-28118	136	1	furthermore	furthermore	ADV
fcis-28118	136	2	,	,	PUNCT
fcis-28118	136	3	temporal	temporal	ADJ
fcis-28118	136	4	distribution	distribution	NOUN
fcis-28118	136	5	dropout	dropout	NOUN
fcis-28118	136	6	is	be	AUX
fcis-28118	136	7	applied	apply	VERB
fcis-28118	136	8	in	in	ADP
fcis-28118	136	9	this	this	DET
fcis-28118	136	10	layer	layer	NOUN
fcis-28118	136	11	,	,	PUNCT
fcis-28118	136	12	where	where	SCONJ
fcis-28118	136	13	a	a	DET
fcis-28118	136	14	portion	portion	NOUN
fcis-28118	136	15	of	of	ADP
fcis-28118	136	16	neurons	neuron	NOUN
fcis-28118	136	17	at	at	ADP
fcis-28118	136	18	each	each	DET
fcis-28118	136	19	time	time	NOUN
fcis-28118	136	20	step	step	NOUN
fcis-28118	136	21	is	be	AUX
fcis-28118	136	22	independently	independently	ADV
fcis-28118	136	23	dropped	drop	VERB
fcis-28118	136	24	at	at	ADP
fcis-28118	136	25	a	a	DET
fcis-28118	136	26	dropout	dropout	NOUN
fcis-28118	136	27	rate	rate	NOUN
fcis-28118	136	28	of	of	ADP
fcis-28118	136	29	0.5	0.5	NUM
fcis-28118	136	30	.	.	PUNCT
fcis-28118	137	1	after	after	ADP
fcis-28118	137	2	the	the	DET
fcis-28118	137	3	bi	bi	PROPN
fcis-28118	137	4	-	-	PROPN
fcis-28118	137	5	gru	gru	NOUN
fcis-28118	137	6	layer	layer	NOUN
fcis-28118	137	7	processing	processing	NOUN
fcis-28118	137	8	,	,	PUNCT
fcis-28118	137	9	the	the	DET
fcis-28118	137	10	data	datum	NOUN
fcis-28118	137	11	is	be	AUX
fcis-28118	137	12	converted	convert	VERB
fcis-28118	137	13	into	into	ADP
fcis-28118	137	14	bidirectional	bidirectional	ADJ
fcis-28118	137	15	output	output	NOUN
fcis-28118	137	16	,	,	PUNCT
fcis-28118	137	17	and	and	CCONJ
fcis-28118	137	18	then	then	ADV
fcis-28118	137	19	inputted	inputte	VERB
fcis-28118	137	20	into	into	ADP
fcis-28118	137	21	the	the	DET
fcis-28118	137	22	fully	fully	ADV
fcis-28118	137	23	connected	connected	ADJ
fcis-28118	137	24	dense	dense	ADJ
fcis-28118	137	25	layer	layer	NOUN
fcis-28118	137	26	.	.	PUNCT
fcis-28118	138	1	in	in	ADP
fcis-28118	138	2	this	this	DET
fcis-28118	138	3	layer	layer	NOUN
fcis-28118	138	4	,	,	PUNCT
fcis-28118	138	5	the	the	DET
fcis-28118	138	6	timedistributed	timedistribute	VERB
fcis-28118	138	7	function	function	NOUN
fcis-28118	138	8	is	be	AUX
fcis-28118	138	9	used	use	VERB
fcis-28118	138	10	to	to	PART
fcis-28118	138	11	apply	apply	VERB
fcis-28118	138	12	the	the	DET
fcis-28118	138	13	fully	fully	ADV
fcis-28118	138	14	connected	connect	VERB
fcis-28118	138	15	layer	layer	NOUN
fcis-28118	138	16	to	to	ADP
fcis-28118	138	17	the	the	DET
fcis-28118	138	18	output	output	NOUN
fcis-28118	138	19	of	of	ADP
fcis-28118	138	20	each	each	DET
fcis-28118	138	21	time	time	NOUN
fcis-28118	138	22	step	step	NOUN
fcis-28118	138	23	,	,	PUNCT
fcis-28118	138	24	referred	refer	VERB
fcis-28118	138	25	to	to	ADP
fcis-28118	138	26	as	as	ADP
fcis-28118	138	27	the	the	DET
fcis-28118	138	28	tddense	tddense	NOUN
fcis-28118	138	29	layer	layer	NOUN
fcis-28118	138	30	,	,	PUNCT
fcis-28118	138	31	with	with	ADP
fcis-28118	138	32	relu	relu	NOUN
fcis-28118	138	33	being	be	AUX
fcis-28118	138	34	used	use	VERB
fcis-28118	138	35	as	as	ADP
fcis-28118	138	36	the	the	DET
fcis-28118	138	37	activation	activation	NOUN
fcis-28118	138	38	function	function	NOUN
fcis-28118	138	39	.	.	PUNCT
fcis-28118	139	1	the	the	DET
fcis-28118	139	2	dropout	dropout	NOUN
fcis-28118	139	3	rate	rate	NOUN
fcis-28118	139	4	for	for	ADP
fcis-28118	139	5	time	time	NOUN
fcis-28118	139	6	distribution	distribution	NOUN
fcis-28118	139	7	is	be	AUX
fcis-28118	139	8	also	also	ADV
fcis-28118	139	9	applied	apply	VERB
fcis-28118	139	10	,	,	PUNCT
fcis-28118	139	11	with	with	ADP
fcis-28118	139	12	a	a	DET
fcis-28118	139	13	dropout	dropout	NOUN
fcis-28118	139	14	rate	rate	NOUN
fcis-28118	139	15	of	of	ADP
fcis-28118	139	16	0.5	0.5	NUM
fcis-28118	139	17	.	.	PUNCT
fcis-28118	140	1	in	in	ADP
fcis-28118	140	2	the	the	DET
fcis-28118	140	3	final	final	ADJ
fcis-28118	140	4	output	output	NOUN
fcis-28118	140	5	layer	layer	NOUN
fcis-28118	140	6	,	,	PUNCT
fcis-28118	140	7	a	a	DET
fcis-28118	140	8	label	label	NOUN
fcis-28118	140	9	is	be	AUX
fcis-28118	140	10	defined	define	VERB
fcis-28118	140	11	to	to	PART
fcis-28118	140	12	predict	predict	VERB
fcis-28118	140	13	each	each	DET
fcis-28118	140	14	time	time	NOUN
fcis-28118	140	15	step	step	NOUN
fcis-28118	140	16	,	,	PUNCT
fcis-28118	140	17	with	with	ADP
fcis-28118	140	18	the	the	DET
fcis-28118	140	19	output	output	NOUN
fcis-28118	140	20	dimension	dimension	NOUN
fcis-28118	140	21	of	of	ADP
fcis-28118	140	22	this	this	DET
fcis-28118	140	23	layer	layer	NOUN
fcis-28118	140	24	being	be	AUX
fcis-28118	140	25	the	the	DET
fcis-28118	140	26	number	number	NOUN
fcis-28118	140	27	of	of	ADP
fcis-28118	140	28	labels	label	NOUN
fcis-28118	140	29	.	.	PUNCT
fcis-28118	141	1	the	the	DET
fcis-28118	141	2	activation	activation	NOUN
fcis-28118	141	3	function	function	NOUN
fcis-28118	141	4	is	be	AUX
fcis-28118	141	5	specified	specify	VERB
fcis-28118	141	6	as	as	ADP
fcis-28118	141	7	softmax	softmax	NOUN
fcis-28118	141	8	to	to	PART
fcis-28118	141	9	convert	convert	VERB
fcis-28118	141	10	the	the	DET
fcis-28118	141	11	output	output	NOUN
fcis-28118	141	12	data	datum	NOUN
fcis-28118	141	13	into	into	ADP
fcis-28118	141	14	a	a	DET
fcis-28118	141	15	probability	probability	NOUN
fcis-28118	141	16	distribution	distribution	NOUN
fcis-28118	141	17	of	of	ADP
fcis-28118	141	18	the	the	DET
fcis-28118	141	19	class	class	NOUN
fcis-28118	141	20	.	.	PUNCT
fcis-28118	142	1	the	the	DET
fcis-28118	142	2	glorot	glorot	PROPN
fcis-28118	142	3	initialization	initialization	NOUN
fcis-28118	142	4	weight	weight	NOUN
fcis-28118	142	5	is	be	AUX
fcis-28118	142	6	applied	apply	VERB
fcis-28118	142	7	to	to	PART
fcis-28118	142	8	evenly	evenly	ADV
fcis-28118	142	9	distribute	distribute	VERB
fcis-28118	142	10	the	the	DET
fcis-28118	142	11	initialization	initialization	NOUN
fcis-28118	142	12	weight	weight	NOUN
fcis-28118	142	13	,	,	PUNCT
fcis-28118	142	14	and	and	CCONJ
fcis-28118	142	15	the	the	DET
fcis-28118	142	16	initializer	initializer	NOUN
fcis-28118	142	17	of	of	ADP
fcis-28118	142	18	the	the	DET
fcis-28118	142	19	offset	offset	ADJ
fcis-28118	142	20	term	term	NOUN
fcis-28118	142	21	is	be	AUX
fcis-28118	142	22	specified	specify	VERB
fcis-28118	142	23	as	as	ADP
fcis-28118	142	24	zero	zero	NUM
fcis-28118	142	25	initialization	initialization	NOUN
fcis-28118	142	26	.	.	PUNCT
fcis-28118	143	1	6	6	NUM
fcis-28118	143	2	.	.	X
fcis-28118	143	3	evaluation	evaluation	NOUN
fcis-28118	143	4	and	and	CCONJ
fcis-28118	143	5	optimization	optimization	NOUN
fcis-28118	143	6	6.1	6.1	NUM
fcis-28118	143	7	.	.	PUNCT
fcis-28118	144	1	evaluation	evaluation	NOUN
fcis-28118	144	2	index	index	NOUN
fcis-28118	144	3	to	to	PART
fcis-28118	144	4	evaluate	evaluate	VERB
fcis-28118	144	5	the	the	DET
fcis-28118	144	6	performance	performance	NOUN
fcis-28118	144	7	of	of	ADP
fcis-28118	144	8	an	an	DET
fcis-28118	144	9	experimental	experimental	ADJ
fcis-28118	144	10	model	model	NOUN
fcis-28118	144	11	in	in	ADP
fcis-28118	144	12	forecasting	forecasting	NOUN
fcis-28118	144	13	,	,	PUNCT
fcis-28118	144	14	q8	q8	PROPN
fcis-28118	144	15	accuracy	accuracy	NOUN
fcis-28118	144	16	is	be	AUX
fcis-28118	144	17	often	often	ADV
fcis-28118	144	18	used	use	VERB
fcis-28118	144	19	as	as	ADP
fcis-28118	144	20	a	a	DET
fcis-28118	144	21	measure	measure	NOUN
fcis-28118	144	22	.	.	PUNCT
fcis-28118	145	1	the	the	PRON
fcis-28118	145	2	higher	high	ADJ
fcis-28118	145	3	the	the	DET
fcis-28118	145	4	accuracy	accuracy	NOUN
fcis-28118	145	5	of	of	ADP
fcis-28118	145	6	q8	q8	PROPN
fcis-28118	145	7	,	,	PUNCT
fcis-28118	145	8	the	the	PRON
fcis-28118	145	9	stronger	strong	ADJ
fcis-28118	145	10	the	the	DET
fcis-28118	145	11	predictive	predictive	ADJ
fcis-28118	145	12	ability	ability	NOUN
fcis-28118	145	13	of	of	ADP
fcis-28118	145	14	the	the	DET
fcis-28118	145	15	model	model	NOUN
fcis-28118	145	16	.	.	PUNCT
fcis-28118	146	1	q8	q8	PROPN
fcis-28118	146	2	accuracy	accuracy	NOUN
fcis-28118	146	3	calculation	calculation	NOUN
fcis-28118	146	4	formula	formula	NOUN
fcis-28118	146	5	:	:	PUNCT
fcis-28118	146	6	8	8	NUM
fcis-28118	146	7	18	18	NUM
fcis-28118	146	8	100	100	NUM
fcis-28118	146	9	ii	ii	NUM
fcis-28118	147	1	i	i	PRON
fcis-28118	147	2	p	p	X
fcis-28118	147	3	q	q	NOUN
fcis-28118	147	4	n	n	NUM
fcis-28118	147	5			NUM
fcis-28118	147	6			NOUN
fcis-28118	147	7			X
fcis-28118	147	8	(	(	PUNCT
fcis-28118	147	9	6	6	NUM
fcis-28118	147	10	)	)	PUNCT
fcis-28118	147	11	where	where	SCONJ
fcis-28118	147	12	iip	iip	PROPN
fcis-28118	147	13	represents	represent	VERB
fcis-28118	147	14	the	the	DET
fcis-28118	147	15	correctly	correctly	ADV
fcis-28118	147	16	predicted	predict	VERB
fcis-28118	147	17	number	number	NOUN
fcis-28118	147	18	of	of	ADP
fcis-28118	147	19	amino	amino	ADJ
fcis-28118	147	20	acids	acid	NOUN
fcis-28118	147	21	,	,	PUNCT
fcis-28118	147	22	n	n	PRON
fcis-28118	147	23	represents	represent	VERB
fcis-28118	147	24	the	the	DET
fcis-28118	147	25	total	total	ADJ
fcis-28118	147	26	number	number	NOUN
fcis-28118	147	27	of	of	ADP
fcis-28118	147	28	amino	amino	NOUN
fcis-28118	147	29	acid	acid	NOUN
fcis-28118	147	30	residues	residue	NOUN
fcis-28118	147	31	.	.	PUNCT
fcis-28118	148	1	6.2	6.2	NUM
fcis-28118	148	2	.	.	PUNCT
fcis-28118	148	3	loss	loss	NOUN
fcis-28118	148	4	function	function	VERB
fcis-28118	148	5	the	the	DET
fcis-28118	148	6	categorical_crossentropy	categorical_crossentropy	NOUN
fcis-28118	148	7	loss	loss	NOUN
fcis-28118	148	8	function	function	NOUN
fcis-28118	148	9	is	be	AUX
fcis-28118	148	10	selected	select	VERB
fcis-28118	148	11	for	for	ADP
fcis-28118	148	12	this	this	DET
fcis-28118	148	13	experiment	experiment	NOUN
fcis-28118	148	14	,	,	PUNCT
fcis-28118	148	15	as	as	SCONJ
fcis-28118	148	16	it	it	PRON
fcis-28118	148	17	is	be	AUX
fcis-28118	148	18	a	a	DET
fcis-28118	148	19	type	type	NOUN
fcis-28118	148	20	of	of	ADP
fcis-28118	148	21	crossentropy	crossentropy	NOUN
fcis-28118	148	22	loss	loss	NOUN
fcis-28118	148	23	function	function	NOUN
fcis-28118	148	24	that	that	PRON
fcis-28118	148	25	is	be	AUX
fcis-28118	148	26	suitable	suitable	ADJ
fcis-28118	148	27	for	for	ADP
fcis-28118	148	28	multi	multi	ADJ
fcis-28118	148	29	-	-	ADJ
fcis-28118	148	30	classification	classification	ADJ
fcis-28118	148	31	problems	problem	NOUN
fcis-28118	148	32	.	.	PUNCT
fcis-28118	149	1	it	it	PRON
fcis-28118	149	2	is	be	AUX
fcis-28118	149	3	chosen	choose	VERB
fcis-28118	149	4	to	to	PART
fcis-28118	149	5	adapt	adapt	VERB
fcis-28118	149	6	to	to	ADP
fcis-28118	149	7	the	the	DET
fcis-28118	149	8	data	datum	NOUN
fcis-28118	149	9	type	type	NOUN
fcis-28118	149	10	and	and	CCONJ
fcis-28118	149	11	to	to	PART
fcis-28118	149	12	improve	improve	VERB
fcis-28118	149	13	the	the	DET
fcis-28118	149	14	accuracy	accuracy	NOUN
fcis-28118	149	15	of	of	ADP
fcis-28118	149	16	predictions	prediction	NOUN
fcis-28118	149	17	.	.	PUNCT
fcis-28118	150	1	the	the	DET
fcis-28118	150	2	cross	cross	ADJ
fcis-28118	150	3	-	-	ADJ
fcis-28118	150	4	entropy	entropy	ADJ
fcis-28118	150	5	loss	loss	NOUN
fcis-28118	150	6	function	function	NOUN
fcis-28118	150	7	is	be	AUX
fcis-28118	150	8	often	often	ADV
fcis-28118	150	9	used	use	VERB
fcis-28118	150	10	to	to	PART
fcis-28118	150	11	measure	measure	VERB
fcis-28118	150	12	the	the	DET
fcis-28118	150	13	difference	difference	NOUN
fcis-28118	150	14	between	between	ADP
fcis-28118	150	15	probability	probability	NOUN
fcis-28118	150	16	distributions	distribution	NOUN
fcis-28118	150	17	.	.	PUNCT
fcis-28118	151	1	since	since	SCONJ
fcis-28118	151	2	the	the	DET
fcis-28118	151	3	q8	q8	PROPN
fcis-28118	151	4	classification	classification	NOUN
fcis-28118	151	5	problem	problem	NOUN
fcis-28118	151	6	entails	entail	VERB
fcis-28118	151	7	multiple	multiple	ADJ
fcis-28118	151	8	classification	classification	NOUN
fcis-28118	151	9	problems	problem	NOUN
fcis-28118	151	10	,	,	PUNCT
fcis-28118	151	11	the	the	DET
fcis-28118	151	12	output	output	NOUN
fcis-28118	151	13	format	format	NOUN
fcis-28118	151	14	of	of	ADP
fcis-28118	151	15	the	the	DET
fcis-28118	151	16	model	model	NOUN
fcis-28118	151	17	consists	consist	VERB
fcis-28118	151	18	of	of	ADP
fcis-28118	151	19	a	a	DET
fcis-28118	151	20	probability	probability	NOUN
fcis-28118	151	21	distribution	distribution	NOUN
fcis-28118	151	22	that	that	PRON
fcis-28118	151	23	represents	represent	VERB
fcis-28118	151	24	the	the	DET
fcis-28118	151	25	probability	probability	NOUN
fcis-28118	151	26	of	of	ADP
fcis-28118	151	27	each	each	DET
fcis-28118	151	28	class	class	NOUN
fcis-28118	151	29	.	.	PUNCT
fcis-28118	152	1	the	the	DET
fcis-28118	152	2	categorical_crossentropy	categorical_crossentropy	NOUN
fcis-28118	152	3	function	function	NOUN
fcis-28118	152	4	calculates	calculate	VERB
fcis-28118	152	5	the	the	DET
fcis-28118	152	6	crossentropy	crossentropy	NOUN
fcis-28118	152	7	between	between	ADP
fcis-28118	152	8	the	the	DET
fcis-28118	152	9	output	output	NOUN
fcis-28118	152	10	probability	probability	NOUN
fcis-28118	152	11	distribution	distribution	NOUN
fcis-28118	152	12	and	and	CCONJ
fcis-28118	152	13	the	the	DET
fcis-28118	152	14	probability	probability	NOUN
fcis-28118	152	15	distribution	distribution	NOUN
fcis-28118	152	16	of	of	ADP
fcis-28118	152	17	the	the	DET
fcis-28118	152	18	actual	actual	ADJ
fcis-28118	152	19	labels	label	NOUN
fcis-28118	152	20	,	,	PUNCT
fcis-28118	152	21	as	as	SCONJ
fcis-28118	152	22	follows	follow	VERB
fcis-28118	152	23	:	:	PUNCT
fcis-28118	152	24			NOUN
fcis-28118	152	25	true	true	ADJ
fcis-28118	152	26	,	,	PUNCT
fcis-28118	152	27	pred	pre	VERB
fcis-28118	152	28	,	,	PUNCT
fcis-28118	152	29	loss	loss	NOUN
fcis-28118	152	30	logi	logi	NOUN
fcis-28118	153	1	i	i	PRON
fcis-28118	153	2	i	i	VERB
fcis-28118	153	3	y	y	PROPN
fcis-28118	153	4	y	y	PROPN
fcis-28118	153	5			PROPN
fcis-28118	153	6			NOUN
fcis-28118	153	7	(	(	PUNCT
fcis-28118	153	8	7	7	NUM
fcis-28118	153	9	)	)	PUNCT
fcis-28118	153	10	where	where	SCONJ
fcis-28118	153	11	,	,	PUNCT
fcis-28118	153	12	ture	ture	PROPN
fcis-28118	153	13	iy	iy	PROPN
fcis-28118	153	14	is	be	AUX
fcis-28118	153	15	the	the	DET
fcis-28118	153	16	probability	probability	NOUN
fcis-28118	153	17	of	of	ADP
fcis-28118	153	18	the	the	DET
fcis-28118	153	19	i	i	NOUN
fcis-28118	153	20	-	-	PUNCT
fcis-28118	153	21	class	class	NOUN
fcis-28118	153	22	of	of	ADP
fcis-28118	153	23	the	the	DET
fcis-28118	153	24	true	true	ADJ
fcis-28118	153	25	label	label	NOUN
fcis-28118	153	26	,	,	PUNCT
fcis-28118	153	27	and	and	CCONJ
fcis-28118	153	28	,	,	PUNCT
fcis-28118	153	29	pred	pred	PROPN
fcis-28118	153	30	iy	iy	PROPN
fcis-28118	153	31	is	be	AUX
fcis-28118	153	32	the	the	DET
fcis-28118	153	33	probability	probability	NOUN
fcis-28118	153	34	of	of	ADP
fcis-28118	153	35	the	the	DET
fcis-28118	153	36	i	i	PROPN
fcis-28118	153	37	-	-	PUNCT
fcis-28118	153	38	class	class	NOUN
fcis-28118	153	39	predicted	predict	VERB
fcis-28118	153	40	by	by	ADP
fcis-28118	153	41	the	the	DET
fcis-28118	153	42	model	model	NOUN
fcis-28118	153	43	.	.	PUNCT
fcis-28118	154	1	6.3	6.3	NUM
fcis-28118	154	2	.	.	PUNCT
fcis-28118	155	1	optimization	optimization	NOUN
fcis-28118	155	2	algorithm	algorithm	NOUN
fcis-28118	155	3	optimization	optimization	NOUN
fcis-28118	155	4	algorithms	algorithm	NOUN
fcis-28118	155	5	are	be	AUX
fcis-28118	155	6	generally	generally	ADV
fcis-28118	155	7	used	use	VERB
fcis-28118	155	8	to	to	PART
fcis-28118	155	9	adjust	adjust	VERB
fcis-28118	155	10	the	the	DET
fcis-28118	155	11	parameters	parameter	NOUN
fcis-28118	155	12	of	of	ADP
fcis-28118	155	13	neural	neural	ADJ
fcis-28118	155	14	networks	network	NOUN
fcis-28118	155	15	to	to	PART
fcis-28118	155	16	minimize	minimize	VERB
fcis-28118	155	17	the	the	DET
fcis-28118	155	18	loss	loss	NOUN
fcis-28118	155	19	function	function	NOUN
fcis-28118	155	20	.	.	PUNCT
fcis-28118	156	1	commonly	commonly	ADV
fcis-28118	156	2	used	use	VERB
fcis-28118	156	3	optimization	optimization	NOUN
fcis-28118	156	4	algorithms	algorithm	NOUN
fcis-28118	156	5	include	include	VERB
fcis-28118	156	6	sgd	sgd	NOUN
fcis-28118	156	7	,	,	PUNCT
fcis-28118	156	8	adagrad	adagrad	ADJ
fcis-28118	156	9	,	,	PUNCT
fcis-28118	156	10	adam	adam	PROPN
fcis-28118	156	11	,	,	PUNCT
fcis-28118	156	12	and	and	CCONJ
fcis-28118	156	13	rmsprop	rmsprop	NOUN
fcis-28118	156	14	,	,	PUNCT
fcis-28118	156	15	etc	etc	X
fcis-28118	156	16	.	.	X
fcis-28118	157	1	in	in	ADP
fcis-28118	157	2	this	this	DET
fcis-28118	157	3	experiment	experiment	NOUN
fcis-28118	157	4	,	,	PUNCT
fcis-28118	157	5	rmsprop	rmsprop	NOUN
fcis-28118	157	6	is	be	AUX
fcis-28118	157	7	selected	select	VERB
fcis-28118	157	8	as	as	ADP
fcis-28118	157	9	the	the	DET
fcis-28118	157	10	optimization	optimization	NOUN
fcis-28118	157	11	algorithm	algorithm	NOUN
fcis-28118	157	12	for	for	ADP
fcis-28118	157	13	neural	neural	ADJ
fcis-28118	157	14	networks	network	NOUN
fcis-28118	157	15	.	.	PUNCT
fcis-28118	158	1	as	as	ADP
fcis-28118	158	2	a	a	DET
fcis-28118	158	3	variant	variant	NOUN
fcis-28118	158	4	of	of	ADP
fcis-28118	158	5	the	the	DET
fcis-28118	158	6	sgd	sgd	PROPN
fcis-28118	158	7	algorithm	algorithm	NOUN
fcis-28118	158	8	,	,	PUNCT
fcis-28118	158	9	rmsprop	rmsprop	NOUN
fcis-28118	158	10	can	can	AUX
fcis-28118	158	11	solve	solve	VERB
fcis-28118	158	12	the	the	DET
fcis-28118	158	13	problem	problem	NOUN
fcis-28118	158	14	of	of	ADP
fcis-28118	158	15	learning	learn	VERB
fcis-28118	158	16	rate	rate	NOUN
fcis-28118	158	17	in	in	ADP
fcis-28118	158	18	the	the	DET
fcis-28118	158	19	standard	standard	ADJ
fcis-28118	158	20	gradient	gradient	ADJ
fcis-28118	158	21	descent	descent	NOUN
fcis-28118	158	22	method	method	NOUN
fcis-28118	158	23	.	.	PUNCT
fcis-28118	159	1	the	the	DET
fcis-28118	159	2	rmsprop	rmsprop	NOUN
fcis-28118	159	3	algorithm	algorithm	NOUN
fcis-28118	159	4	can	can	AUX
fcis-28118	159	5	adjust	adjust	VERB
fcis-28118	159	6	the	the	DET
fcis-28118	159	7	learning	learning	NOUN
fcis-28118	159	8	rate	rate	NOUN
fcis-28118	159	9	adaptively	adaptively	ADV
fcis-28118	159	10	by	by	ADP
fcis-28118	159	11	maintaining	maintain	VERB
fcis-28118	159	12	the	the	DET
fcis-28118	159	13	moving	move	VERB
fcis-28118	159	14	average	average	NOUN
fcis-28118	159	15	of	of	ADP
fcis-28118	159	16	the	the	DET
fcis-28118	159	17	gradient	gradient	PROPN
fcis-28118	159	18	square	square	NOUN
fcis-28118	159	19	.	.	PUNCT
fcis-28118	160	1	its	its	PRON
fcis-28118	160	2	update	update	NOUN
fcis-28118	160	3	rules	rule	NOUN
fcis-28118	160	4	are	be	AUX
fcis-28118	160	5	as	as	SCONJ
fcis-28118	160	6	follows	follow	VERB
fcis-28118	160	7	:	:	PUNCT
fcis-28118	160	8	calculate	calculate	VERB
fcis-28118	160	9	the	the	DET
fcis-28118	160	10	exponential	exponential	NOUN
fcis-28118	160	11	weighted	weight	VERB
fcis-28118	160	12	average	average	ADJ
fcis-28118	160	13	motion	motion	NOUN
fcis-28118	160	14	of	of	ADP
fcis-28118	160	15	gradient	gradient	ADJ
fcis-28118	160	16	square	square	ADJ
fcis-28118	160	17	method	method	NOUN
fcis-28118	160	18	:	:	PUNCT
fcis-28118	160	19			NUM
fcis-28118	160	20	1	1	NOUN
fcis-28118	160	21	(	(	PUNCT
fcis-28118	160	22	1	1	NUM
fcis-28118	160	23	)	)	PUNCT
fcis-28118	160	24	t	t	NOUN
fcis-28118	160	25	t	t	PROPN
fcis-28118	160	26	t	t	PROPN
fcis-28118	160	27	tv	tv	NOUN
fcis-28118	160	28	v	v	ADP
fcis-28118	160	29	g	g	PROPN
fcis-28118	160	30	g	g	NOUN
fcis-28118	160	31			NOUN
fcis-28118	160	32			NOUN
fcis-28118	160	33			ADV
fcis-28118	160	34			PROPN
fcis-28118	160	35			PRON
fcis-28118	160	36			PROPN
fcis-28118	160	37	(	(	PUNCT
fcis-28118	160	38	8)	8)	NUM
fcis-28118	160	39	represents	represent	VERB
fcis-28118	160	40	the	the	DET
fcis-28118	160	41	element	element	NOUN
fcis-28118	160	42	performance	performance	NOUN
fcis-28118	160	43	operation	operation	NOUN
fcis-28118	160	44	,	,	PUNCT
fcis-28118	160	45	tv	tv	NOUN
fcis-28118	160	46	is	be	AUX
fcis-28118	160	47	the	the	DET
fcis-28118	160	48	moving	move	VERB
fcis-28118	160	49	average	average	NOUN
fcis-28118	160	50	of	of	ADP
fcis-28118	160	51	the	the	DET
fcis-28118	160	52	gradient	gradient	NOUN
fcis-28118	160	53	squared	square	VERB
fcis-28118	160	54	at	at	ADP
fcis-28118	160	55	time	time	NOUN
fcis-28118	160	56	t	t	PROPN
fcis-28118	160	57	,	,	PUNCT
fcis-28118	160	58			PROPN
fcis-28118	160	59	is	be	AUX
fcis-28118	160	60	the	the	DET
fcis-28118	160	61	decay	decay	NOUN
fcis-28118	160	62	rate	rate	NOUN
fcis-28118	160	63	,	,	PUNCT
fcis-28118	160	64	tg	tg	PROPN
fcis-28118	160	65	is	be	AUX
fcis-28118	160	66	the	the	DET
fcis-28118	160	67	current	current	ADJ
fcis-28118	160	68	gradient	gradient	NOUN
fcis-28118	160	69	.	.	PUNCT
fcis-28118	161	1	use	use	VERB
fcis-28118	161	2	the	the	DET
fcis-28118	161	3	square	square	ADJ
fcis-28118	161	4	root	root	NOUN
fcis-28118	161	5	of	of	ADP
fcis-28118	161	6	the	the	DET
fcis-28118	161	7	normal	normal	ADJ
fcis-28118	161	8	gradient	gradient	NOUN
fcis-28118	161	9	as	as	ADP
fcis-28118	161	10	the	the	DET
fcis-28118	161	11	scaling	scaling	ADJ
fcis-28118	161	12	factor	factor	NOUN
fcis-28118	161	13	of	of	ADP
fcis-28118	161	14	the	the	DET
fcis-28118	161	15	learning	learning	NOUN
fcis-28118	161	16	rate	rate	NOUN
fcis-28118	161	17	:	:	PUNCT
fcis-28118	161	18	t	t	PROPN
fcis-28118	161	19	t	t	PROPN
fcis-28118	161	20	t	t	PROPN
fcis-28118	161	21	w	w	PROPN
fcis-28118	161	22	g	g	PROPN
fcis-28118	161	23	v	v	PRON
fcis-28118	161	24			NUM
fcis-28118	161	25			PROPN
fcis-28118	161	26			NOUN
fcis-28118	161	27			PROPN
fcis-28118	161	28			ADJ
fcis-28118	161	29			PROPN
fcis-28118	161	30	(	(	PUNCT
fcis-28118	161	31	9	9	NUM
fcis-28118	161	32	)	)	PUNCT
fcis-28118	161	33			NUM
fcis-28118	161	34	is	be	AUX
fcis-28118	161	35	the	the	DET
fcis-28118	161	36	learning	learning	NOUN
fcis-28118	161	37	rate	rate	NOUN
fcis-28118	161	38	and	and	CCONJ
fcis-28118	161	39			ADV
fcis-28118	161	40	is	be	AUX
fcis-28118	161	41	a	a	DET
fcis-28118	161	42	constant	constant	ADJ
fcis-28118	161	43	to	to	PART
fcis-28118	161	44	avoid	avoid	VERB
fcis-28118	161	45	zero	zero	NUM
fcis-28118	161	46	errors	error	NOUN
fcis-28118	161	47	.	.	PUNCT
fcis-28118	162	1	update	update	NOUN
fcis-28118	162	2	parameters	parameter	NOUN
fcis-28118	162	3	:	:	PUNCT
fcis-28118	162	4	1	1	NUM
fcis-28118	162	5	t	t	NOUN
fcis-28118	162	6	t	t	X
fcis-28118	162	7	tw	tw	X
fcis-28118	162	8	w	w	PROPN
fcis-28118	162	9	w	w	PROPN
fcis-28118	162	10			PROPN
fcis-28118	162	11			PUNCT
fcis-28118	162	12			ADJ
fcis-28118	162	13	(	(	PUNCT
fcis-28118	162	14	10	10	NUM
fcis-28118	162	15	)	)	PUNCT
fcis-28118	162	16	through	through	ADP
fcis-28118	162	17	the	the	DET
fcis-28118	162	18	above	above	ADJ
fcis-28118	162	19	updating	update	VERB
fcis-28118	162	20	steps	step	NOUN
fcis-28118	162	21	,	,	PUNCT
fcis-28118	162	22	the	the	DET
fcis-28118	162	23	rmsprop	rmsprop	NOUN
fcis-28118	162	24	algorithm	algorithm	NOUN
fcis-28118	162	25	can	can	AUX
fcis-28118	162	26	gradually	gradually	ADV
fcis-28118	162	27	optimize	optimize	VERB
fcis-28118	162	28	the	the	DET
fcis-28118	162	29	parameters	parameter	NOUN
fcis-28118	162	30	of	of	ADP
fcis-28118	162	31	the	the	DET
fcis-28118	162	32	model	model	NOUN
fcis-28118	162	33	and	and	CCONJ
fcis-28118	162	34	finally	finally	ADV
fcis-28118	162	35	minimize	minimize	VERB
fcis-28118	162	36	the	the	DET
fcis-28118	162	37	loss	loss	NOUN
fcis-28118	162	38	function	function	NOUN
fcis-28118	162	39	to	to	PART
fcis-28118	162	40	enhance	enhance	VERB
fcis-28118	162	41	the	the	DET
fcis-28118	162	42	model	model	NOUN
fcis-28118	162	43	's	's	PART
fcis-28118	162	44	performance	performance	NOUN
fcis-28118	162	45	.	.	PUNCT
fcis-28118	163	1	6.4	6.4	NUM
fcis-28118	163	2	.	.	PUNCT
fcis-28118	164	1	regularization	regularization	NOUN
fcis-28118	164	2	l2	l2	NOUN
fcis-28118	164	3	regularization	regularization	NOUN
fcis-28118	164	4	is	be	AUX
fcis-28118	164	5	to	to	PART
fcis-28118	164	6	add	add	VERB
fcis-28118	164	7	a	a	DET
fcis-28118	164	8	penalty	penalty	NOUN
fcis-28118	164	9	term	term	NOUN
fcis-28118	164	10	to	to	ADP
fcis-28118	164	11	the	the	DET
fcis-28118	164	12	loss	loss	NOUN
fcis-28118	164	13	function	function	NOUN
fcis-28118	164	14	,	,	PUNCT
fcis-28118	164	15	which	which	PRON
fcis-28118	164	16	is	be	AUX
fcis-28118	164	17	in	in	ADP
fcis-28118	164	18	the	the	DET
fcis-28118	164	19	form	form	NOUN
fcis-28118	164	20	of	of	ADP
fcis-28118	164	21	the	the	DET
fcis-28118	164	22	original	original	ADJ
fcis-28118	164	23	loss	loss	NOUN
fcis-28118	164	24	function	function	NOUN
fcis-28118	164	25	and	and	CCONJ
fcis-28118	164	26	the	the	DET
fcis-28118	164	27	sum	sum	NOUN
fcis-28118	164	28	of	of	ADP
fcis-28118	164	29	squares	square	NOUN
fcis-28118	164	30	of	of	ADP
fcis-28118	164	31	the	the	DET
fcis-28118	164	32	weight	weight	NOUN
fcis-28118	164	33	multiplied	multiply	VERB
fcis-28118	164	34	by	by	ADP
fcis-28118	164	35	a	a	DET
fcis-28118	164	36	28	28	NUM
fcis-28118	164	37	regularization	regularization	NOUN
fcis-28118	164	38	parameter	parameter	NOUN
fcis-28118	164	39	,	,	PUNCT
fcis-28118	164	40	the	the	DET
fcis-28118	164	41	specific	specific	ADJ
fcis-28118	164	42	formula	formula	NOUN
fcis-28118	164	43	is	be	AUX
fcis-28118	164	44	as	as	SCONJ
fcis-28118	164	45	follows	follow	VERB
fcis-28118	164	46	:	:	PUNCT
fcis-28118	164	47	2	2	NUM
fcis-28118	164	48	loss	loss	NOUN
fcis-28118	164	49	originalloss	originalloss	ADJ
fcis-28118	164	50	iw	iw	PROPN
fcis-28118	164	51			PROPN
fcis-28118	164	52			PROPN
fcis-28118	164	53	(	(	PUNCT
fcis-28118	164	54	11	11	NUM
fcis-28118	164	55	)	)	PUNCT
fcis-28118	164	56	where	where	SCONJ
fcis-28118	164	57	originall	originall	PROPN
fcis-28118	164	58	oss	oss	NOUN
fcis-28118	164	59	is	be	AUX
fcis-28118	164	60	the	the	DET
fcis-28118	164	61	original	original	ADJ
fcis-28118	164	62	loss	loss	NOUN
fcis-28118	164	63	function	function	NOUN
fcis-28118	164	64	,	,	PUNCT
fcis-28118	164	65	that	that	ADV
fcis-28118	164	66	is	is	ADV
fcis-28118	164	67	,	,	PUNCT
fcis-28118	164	68	the	the	DET
fcis-28118	164	69	model	model	NOUN
fcis-28118	164	70	objective	objective	ADJ
fcis-28118	164	71	function	function	NOUN
fcis-28118	164	72	,	,	PUNCT
fcis-28118	164	73	iw	iw	PROPN
fcis-28118	164	74	is	be	AUX
fcis-28118	164	75	the	the	DET
fcis-28118	164	76	weight	weight	NOUN
fcis-28118	164	77	parameter	parameter	NOUN
fcis-28118	164	78	of	of	ADP
fcis-28118	164	79	the	the	DET
fcis-28118	164	80	model	model	NOUN
fcis-28118	164	81	,	,	PUNCT
fcis-28118	164	82	and	and	CCONJ
fcis-28118	164	83			ADJ
fcis-28118	164	84	is	be	AUX
fcis-28118	164	85	the	the	DET
fcis-28118	164	86	regularization	regularization	NOUN
fcis-28118	164	87	parameter	parameter	NOUN
fcis-28118	164	88	,	,	PUNCT
fcis-28118	164	89	which	which	PRON
fcis-28118	164	90	is	be	AUX
fcis-28118	164	91	used	use	VERB
fcis-28118	164	92	to	to	PART
fcis-28118	164	93	control	control	VERB
fcis-28118	164	94	the	the	DET
fcis-28118	164	95	intensity	intensity	NOUN
fcis-28118	164	96	of	of	ADP
fcis-28118	164	97	regularization	regularization	NOUN
fcis-28118	164	98	.	.	PUNCT
fcis-28118	165	1	7	7	X
fcis-28118	165	2	.	.	NUM
fcis-28118	165	3	results	result	NOUN
fcis-28118	165	4	and	and	CCONJ
fcis-28118	165	5	discussion	discussion	NOUN
fcis-28118	165	6	in	in	ADP
fcis-28118	165	7	this	this	DET
fcis-28118	165	8	study	study	NOUN
fcis-28118	165	9	,	,	PUNCT
fcis-28118	165	10	the	the	DET
fcis-28118	165	11	model	model	NOUN
fcis-28118	165	12	is	be	AUX
fcis-28118	165	13	adjusted	adjust	VERB
fcis-28118	165	14	by	by	ADP
fcis-28118	165	15	finding	find	VERB
fcis-28118	165	16	the	the	DET
fcis-28118	165	17	best	good	ADJ
fcis-28118	165	18	combination	combination	NOUN
fcis-28118	165	19	of	of	ADP
fcis-28118	165	20	parameters	parameter	NOUN
fcis-28118	165	21	.	.	PUNCT
fcis-28118	166	1	for	for	ADP
fcis-28118	166	2	this	this	DET
fcis-28118	166	3	purpose	purpose	NOUN
fcis-28118	166	4	,	,	PUNCT
fcis-28118	166	5	this	this	DET
fcis-28118	166	6	paper	paper	NOUN
fcis-28118	166	7	explores	explore	VERB
fcis-28118	166	8	multiple	multiple	ADJ
fcis-28118	166	9	parameters	parameter	NOUN
fcis-28118	166	10	,	,	PUNCT
fcis-28118	166	11	including	include	VERB
fcis-28118	166	12	the	the	DET
fcis-28118	166	13	size	size	NOUN
fcis-28118	166	14	and	and	CCONJ
fcis-28118	166	15	number	number	NOUN
fcis-28118	166	16	of	of	ADP
fcis-28118	166	17	convolutional	convolutional	ADJ
fcis-28118	166	18	nuclei	nucleus	NOUN
fcis-28118	166	19	and	and	CCONJ
fcis-28118	166	20	the	the	DET
fcis-28118	166	21	number	number	NOUN
fcis-28118	166	22	of	of	ADP
fcis-28118	166	23	hidden	hidden	ADJ
fcis-28118	166	24	layers	layer	NOUN
fcis-28118	166	25	in	in	ADP
fcis-28118	166	26	the	the	DET
fcis-28118	166	27	convolutional	convolutional	ADJ
fcis-28118	166	28	layer	layer	NOUN
fcis-28118	166	29	,	,	PUNCT
fcis-28118	166	30	and	and	CCONJ
fcis-28118	166	31	investigates	investigate	VERB
fcis-28118	166	32	the	the	DET
fcis-28118	166	33	influence	influence	NOUN
fcis-28118	166	34	of	of	ADP
fcis-28118	166	35	learning	learn	VERB
fcis-28118	166	36	rate	rate	NOUN
fcis-28118	166	37	on	on	ADP
fcis-28118	166	38	accuracy	accuracy	NOUN
fcis-28118	166	39	.	.	PUNCT
fcis-28118	167	1	in	in	ADP
fcis-28118	167	2	this	this	DET
fcis-28118	167	3	experiment	experiment	NOUN
fcis-28118	167	4	,	,	PUNCT
fcis-28118	167	5	the	the	DET
fcis-28118	167	6	combination	combination	NOUN
fcis-28118	167	7	of	of	ADP
fcis-28118	167	8	ssc	ssc	NOUN
fcis-28118	167	9	layer	layer	NOUN
fcis-28118	167	10	and	and	CCONJ
fcis-28118	167	11	msc	msc	PROPN
fcis-28118	167	12	layer	layer	NOUN
fcis-28118	167	13	is	be	AUX
fcis-28118	167	14	called	call	VERB
fcis-28118	167	15	an	an	DET
fcis-28118	167	16	smc	smc	NOUN
fcis-28118	167	17	block	block	NOUN
fcis-28118	167	18	.	.	PUNCT
fcis-28118	168	1	the	the	DET
fcis-28118	168	2	number	number	NOUN
fcis-28118	168	3	of	of	ADP
fcis-28118	168	4	smc	smc	PROPN
fcis-28118	168	5	blocks	block	NOUN
fcis-28118	168	6	determines	determine	VERB
fcis-28118	168	7	the	the	DET
fcis-28118	168	8	number	number	NOUN
fcis-28118	168	9	of	of	ADP
fcis-28118	168	10	convolution	convolution	NOUN
fcis-28118	168	11	layers	layer	NOUN
fcis-28118	168	12	of	of	ADP
fcis-28118	168	13	the	the	DET
fcis-28118	168	14	model	model	NOUN
fcis-28118	168	15	,	,	PUNCT
fcis-28118	168	16	and	and	CCONJ
fcis-28118	168	17	the	the	DET
fcis-28118	168	18	convolution	convolution	NOUN
fcis-28118	168	19	kernel	kernel	NOUN
fcis-28118	168	20	along	along	ADP
fcis-28118	168	21	with	with	ADP
fcis-28118	168	22	the	the	DET
fcis-28118	168	23	number	number	NOUN
fcis-28118	168	24	of	of	ADP
fcis-28118	168	25	convolution	convolution	NOUN
fcis-28118	168	26	layers	layer	NOUN
fcis-28118	168	27	jointly	jointly	ADV
fcis-28118	168	28	determines	determine	VERB
fcis-28118	168	29	the	the	DET
fcis-28118	168	30	size	size	NOUN
fcis-28118	168	31	of	of	ADP
fcis-28118	168	32	the	the	DET
fcis-28118	168	33	final	final	ADJ
fcis-28118	168	34	receptive	receptive	ADJ
fcis-28118	168	35	field	field	NOUN
fcis-28118	168	36	.	.	PUNCT
fcis-28118	169	1	therefore	therefore	ADV
fcis-28118	169	2	,	,	PUNCT
fcis-28118	169	3	determining	determine	VERB
fcis-28118	169	4	the	the	DET
fcis-28118	169	5	number	number	NOUN
fcis-28118	169	6	of	of	ADP
fcis-28118	169	7	sms	sms	NOUN
fcis-28118	169	8	blocks	block	NOUN
fcis-28118	169	9	in	in	ADP
fcis-28118	169	10	the	the	DET
fcis-28118	169	11	model	model	NOUN
fcis-28118	169	12	and	and	CCONJ
fcis-28118	169	13	the	the	DET
fcis-28118	169	14	size	size	NOUN
fcis-28118	169	15	of	of	ADP
fcis-28118	169	16	the	the	DET
fcis-28118	169	17	convolution	convolution	NOUN
fcis-28118	169	18	kernel	kernel	NOUN
fcis-28118	169	19	have	have	VERB
fcis-28118	169	20	a	a	DET
fcis-28118	169	21	significant	significant	ADJ
fcis-28118	169	22	influence	influence	NOUN
fcis-28118	169	23	on	on	ADP
fcis-28118	169	24	the	the	DET
fcis-28118	169	25	final	final	ADJ
fcis-28118	169	26	prediction	prediction	NOUN
fcis-28118	169	27	result	result	NOUN
fcis-28118	169	28	.	.	PUNCT
fcis-28118	170	1	in	in	ADP
fcis-28118	170	2	order	order	NOUN
fcis-28118	170	3	to	to	PART
fcis-28118	170	4	explore	explore	VERB
fcis-28118	170	5	the	the	DET
fcis-28118	170	6	effect	effect	NOUN
fcis-28118	170	7	of	of	ADP
fcis-28118	170	8	the	the	DET
fcis-28118	170	9	convolution	convolution	NOUN
fcis-28118	170	10	layer	layer	NOUN
fcis-28118	170	11	on	on	ADP
fcis-28118	170	12	the	the	DET
fcis-28118	170	13	accuracy	accuracy	NOUN
fcis-28118	170	14	of	of	ADP
fcis-28118	170	15	the	the	DET
fcis-28118	170	16	model	model	NOUN
fcis-28118	170	17	,	,	PUNCT
fcis-28118	170	18	the	the	DET
fcis-28118	170	19	model	model	NOUN
fcis-28118	170	20	with	with	ADP
fcis-28118	170	21	the	the	DET
fcis-28118	170	22	number	number	NOUN
fcis-28118	170	23	of	of	ADP
fcis-28118	170	24	smc	smc	PROPN
fcis-28118	170	25	blocks	block	NOUN
fcis-28118	170	26	set	set	VERB
fcis-28118	170	27	at	at	ADP
fcis-28118	170	28	1	1	NUM
fcis-28118	170	29	,	,	PUNCT
fcis-28118	170	30	2	2	NUM
fcis-28118	170	31	,	,	PUNCT
fcis-28118	170	32	3	3	NUM
fcis-28118	170	33	,	,	PUNCT
fcis-28118	170	34	4	4	NUM
fcis-28118	170	35	,	,	PUNCT
fcis-28118	170	36	and	and	CCONJ
fcis-28118	170	37	5	5	NUM
fcis-28118	170	38	was	be	AUX
fcis-28118	170	39	tested	test	VERB
fcis-28118	170	40	while	while	SCONJ
fcis-28118	170	41	keeping	keep	VERB
fcis-28118	170	42	the	the	DET
fcis-28118	170	43	model	model	NOUN
fcis-28118	170	44	parameters	parameter	NOUN
fcis-28118	170	45	unchanged	unchanged	ADJ
fcis-28118	170	46	.	.	PUNCT
fcis-28118	171	1	as	as	SCONJ
fcis-28118	171	2	shown	show	VERB
fcis-28118	171	3	in	in	ADP
fcis-28118	171	4	table	table	NOUN
fcis-28118	171	5	2	2	NUM
fcis-28118	171	6	,	,	PUNCT
fcis-28118	171	7	the	the	DET
fcis-28118	171	8	model	model	NOUN
fcis-28118	171	9	achieves	achieve	VERB
fcis-28118	171	10	the	the	DET
fcis-28118	171	11	best	good	ADJ
fcis-28118	171	12	performance	performance	NOUN
fcis-28118	171	13	when	when	SCONJ
fcis-28118	171	14	the	the	DET
fcis-28118	171	15	number	number	NOUN
fcis-28118	171	16	of	of	ADP
fcis-28118	171	17	smc	smc	PROPN
fcis-28118	171	18	blocks	block	NOUN
fcis-28118	171	19	is	be	AUX
fcis-28118	171	20	3	3	NUM
fcis-28118	171	21	layers	layer	NOUN
fcis-28118	171	22	,	,	PUNCT
fcis-28118	171	23	with	with	SCONJ
fcis-28118	171	24	an	an	DET
fcis-28118	171	25	accuracy	accuracy	NOUN
fcis-28118	171	26	rate	rate	NOUN
fcis-28118	171	27	of	of	ADP
fcis-28118	171	28	72.4	72.4	NUM
fcis-28118	171	29	%	%	NOUN
fcis-28118	171	30	on	on	ADP
fcis-28118	171	31	the	the	DET
fcis-28118	171	32	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	171	33	test	test	NOUN
fcis-28118	171	34	set	set	VERB
fcis-28118	171	35	and68.5	and68.5	PROPN
fcis-28118	171	36	%	%	NOUN
fcis-28118	171	37	on	on	ADP
fcis-28118	171	38	the	the	DET
fcis-28118	171	39	cb513	cb513	PROPN
fcis-28118	171	40	test	test	NOUN
fcis-28118	171	41	set	set	VERB
fcis-28118	171	42	.	.	PUNCT
fcis-28118	172	1	table	table	NOUN
fcis-28118	172	2	2	2	NUM
fcis-28118	172	3	.	.	PUNCT
fcis-28118	172	4	effects	effect	NOUN
fcis-28118	172	5	of	of	ADP
fcis-28118	172	6	convolutional	convolutional	ADJ
fcis-28118	172	7	layers	layer	NOUN
fcis-28118	172	8	on	on	ADP
fcis-28118	172	9	model	model	NOUN
fcis-28118	172	10	performance	performance	NOUN
fcis-28118	172	11	.	.	PUNCT
fcis-28118	173	1	smcblocks	smcblock	NOUN
fcis-28118	173	2	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	173	3	accuracy	accuracy	NOUN
fcis-28118	173	4	(	(	PUNCT
fcis-28118	173	5	%	%	INTJ
fcis-28118	173	6	)	)	PUNCT
fcis-28118	173	7	cb513	cb513	NOUN
fcis-28118	173	8	accuracy	accuracy	NOUN
fcis-28118	173	9	(	(	PUNCT
fcis-28118	173	10	%	%	INTJ
fcis-28118	173	11	)	)	PUNCT
fcis-28118	173	12	w1	w1	NOUN
fcis-28118	173	13	70.5	70.5	NUM
fcis-28118	173	14	67.2	67.2	NUM
fcis-28118	173	15	w2	w2	NOUN
fcis-28118	173	16	72.0	72.0	NUM
fcis-28118	173	17	68.2	68.2	NUM
fcis-28118	173	18	w3	w3	PROPN
fcis-28118	173	19	72.4	72.4	NUM
fcis-28118	173	20	68.5	68.5	NUM
fcis-28118	173	21	w4	w4	NOUN
fcis-28118	173	22	70.3	70.3	NUM
fcis-28118	173	23	67.6	67.6	NUM
fcis-28118	173	24	w5	w5	PROPN
fcis-28118	173	25	71.9	71.9	NUM
fcis-28118	173	26	67.7	67.7	NUM
fcis-28118	173	27	after	after	ADP
fcis-28118	173	28	exploring	explore	VERB
fcis-28118	173	29	the	the	DET
fcis-28118	173	30	influence	influence	NOUN
fcis-28118	173	31	of	of	ADP
fcis-28118	173	32	smc	smc	PROPN
fcis-28118	173	33	blocks	block	NOUN
fcis-28118	173	34	on	on	ADP
fcis-28118	173	35	model	model	NOUN
fcis-28118	173	36	accuracy	accuracy	NOUN
fcis-28118	173	37	,	,	PUNCT
fcis-28118	173	38	the	the	DET
fcis-28118	173	39	influence	influence	NOUN
fcis-28118	173	40	of	of	ADP
fcis-28118	173	41	convolution	convolution	NOUN
fcis-28118	173	42	kernel	kernel	NOUN
fcis-28118	173	43	size	size	NOUN
fcis-28118	173	44	on	on	ADP
fcis-28118	173	45	model	model	NOUN
fcis-28118	173	46	performance	performance	NOUN
fcis-28118	173	47	was	be	AUX
fcis-28118	173	48	explored	explore	VERB
fcis-28118	173	49	with	with	ADP
fcis-28118	173	50	the	the	DET
fcis-28118	173	51	number	number	NOUN
fcis-28118	173	52	of	of	ADP
fcis-28118	173	53	smc	smc	PROPN
fcis-28118	173	54	blocks	block	NOUN
fcis-28118	173	55	guaranteed	guarantee	VERB
fcis-28118	173	56	to	to	PART
fcis-28118	173	57	be	be	AUX
fcis-28118	173	58	3	3	NUM
fcis-28118	173	59	,	,	PUNCT
fcis-28118	173	60	and	and	CCONJ
fcis-28118	173	61	the	the	DET
fcis-28118	173	62	model	model	NOUN
fcis-28118	173	63	accuracy	accuracy	NOUN
fcis-28118	173	64	under	under	ADP
fcis-28118	173	65	each	each	DET
fcis-28118	173	66	convolution	convolution	NOUN
fcis-28118	173	67	was	be	AUX
fcis-28118	173	68	analyzed	analyze	VERB
fcis-28118	173	69	while	while	SCONJ
fcis-28118	173	70	other	other	ADJ
fcis-28118	173	71	parameters	parameter	NOUN
fcis-28118	173	72	remained	remain	VERB
fcis-28118	173	73	unchanged	unchanged	ADJ
fcis-28118	173	74	.	.	PUNCT
fcis-28118	174	1	as	as	SCONJ
fcis-28118	174	2	can	can	AUX
fcis-28118	174	3	be	be	AUX
fcis-28118	174	4	seen	see	VERB
fcis-28118	174	5	from	from	ADP
fcis-28118	174	6	table	table	NOUN
fcis-28118	174	7	3	3	NUM
fcis-28118	174	8	,	,	PUNCT
fcis-28118	174	9	when	when	SCONJ
fcis-28118	174	10	the	the	DET
fcis-28118	174	11	convolution	convolution	NOUN
fcis-28118	174	12	kernel	kernel	NOUN
fcis-28118	174	13	of	of	ADP
fcis-28118	174	14	ssc	ssc	NOUN
fcis-28118	174	15	layer	layer	NOUN
fcis-28118	174	16	and	and	CCONJ
fcis-28118	174	17	msc	msc	PROPN
fcis-28118	174	18	layer	layer	NOUN
fcis-28118	174	19	is	be	AUX
fcis-28118	174	20	set	set	VERB
fcis-28118	174	21	to	to	ADP
fcis-28118	174	22	1	1	NUM
fcis-28118	174	23	layer	layer	NOUN
fcis-28118	174	24	,	,	PUNCT
fcis-28118	174	25	the	the	DET
fcis-28118	174	26	model	model	NOUN
fcis-28118	174	27	shows	show	VERB
fcis-28118	174	28	poor	poor	ADJ
fcis-28118	174	29	performance	performance	NOUN
fcis-28118	174	30	,	,	PUNCT
fcis-28118	174	31	and	and	CCONJ
fcis-28118	174	32	the	the	DET
fcis-28118	174	33	accuracy	accuracy	NOUN
fcis-28118	174	34	rate	rate	NOUN
fcis-28118	174	35	is	be	AUX
fcis-28118	174	36	64.3	64.3	NUM
fcis-28118	174	37	%	%	NOUN
fcis-28118	174	38	on	on	ADP
fcis-28118	174	39	the	the	DET
fcis-28118	174	40	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	174	41	test	test	NOUN
fcis-28118	174	42	set	set	VERB
fcis-28118	174	43	and	and	CCONJ
fcis-28118	174	44	59.8	59.8	NUM
fcis-28118	174	45	%	%	NOUN
fcis-28118	174	46	on	on	ADP
fcis-28118	174	47	the	the	DET
fcis-28118	174	48	cb513	cb513	PROPN
fcis-28118	174	49	test	test	NOUN
fcis-28118	174	50	set	set	VERB
fcis-28118	174	51	.	.	PUNCT
fcis-28118	175	1	when	when	SCONJ
fcis-28118	175	2	the	the	DET
fcis-28118	175	3	convolutional	convolutional	ADJ
fcis-28118	175	4	nuclei	nucleus	NOUN
fcis-28118	175	5	of	of	ADP
fcis-28118	175	6	the	the	DET
fcis-28118	175	7	ssc	ssc	NOUN
fcis-28118	175	8	layer	layer	NOUN
fcis-28118	175	9	are	be	AUX
fcis-28118	175	10	set	set	VERB
fcis-28118	175	11	to	to	ADP
fcis-28118	175	12	9	9	NUM
fcis-28118	175	13	and	and	CCONJ
fcis-28118	175	14	the	the	DET
fcis-28118	175	15	three	three	NUM
fcis-28118	175	16	convolutional	convolutional	ADJ
fcis-28118	175	17	nuclei	nucleus	NOUN
fcis-28118	175	18	of	of	ADP
fcis-28118	175	19	the	the	DET
fcis-28118	175	20	msc	msc	PROPN
fcis-28118	175	21	layer	layer	NOUN
fcis-28118	175	22	are	be	AUX
fcis-28118	175	23	set	set	VERB
fcis-28118	175	24	to	to	ADP
fcis-28118	175	25	3	3	NUM
fcis-28118	175	26	,	,	PUNCT
fcis-28118	175	27	5	5	NUM
fcis-28118	175	28	,	,	PUNCT
fcis-28118	175	29	and	and	CCONJ
fcis-28118	175	30	7	7	NUM
fcis-28118	175	31	respectively	respectively	ADV
fcis-28118	175	32	,	,	PUNCT
fcis-28118	175	33	the	the	DET
fcis-28118	175	34	model	model	NOUN
fcis-28118	175	35	performs	perform	VERB
fcis-28118	175	36	the	the	DET
fcis-28118	175	37	best	good	ADJ
fcis-28118	175	38	,	,	PUNCT
fcis-28118	175	39	with	with	ADP
fcis-28118	175	40	an	an	DET
fcis-28118	175	41	accuracy	accuracy	NOUN
fcis-28118	175	42	rate	rate	NOUN
fcis-28118	175	43	of	of	ADP
fcis-28118	175	44	72.5	72.5	NUM
fcis-28118	175	45	%	%	NOUN
fcis-28118	175	46	on	on	ADP
fcis-28118	175	47	the	the	DET
fcis-28118	175	48	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	175	49	test	test	NOUN
fcis-28118	175	50	set	set	VERB
fcis-28118	175	51	and	and	CCONJ
fcis-28118	175	52	68.8	68.8	NUM
fcis-28118	175	53	%	%	NOUN
fcis-28118	175	54	on	on	ADP
fcis-28118	175	55	the	the	DET
fcis-28118	175	56	cb513	cb513	PROPN
fcis-28118	175	57	test	test	NOUN
fcis-28118	175	58	set	set	VERB
fcis-28118	175	59	.	.	PUNCT
fcis-28118	176	1	after	after	ADP
fcis-28118	176	2	that	that	PRON
fcis-28118	176	3	,	,	PUNCT
fcis-28118	176	4	the	the	DET
fcis-28118	176	5	accuracy	accuracy	NOUN
fcis-28118	176	6	of	of	ADP
fcis-28118	176	7	q8	q8	PROPN
fcis-28118	176	8	was	be	AUX
fcis-28118	176	9	reduced	reduce	VERB
fcis-28118	176	10	by	by	ADP
fcis-28118	176	11	increasing	increase	VERB
fcis-28118	176	12	the	the	DET
fcis-28118	176	13	size	size	NOUN
fcis-28118	176	14	of	of	ADP
fcis-28118	176	15	convolutional	convolutional	ADJ
fcis-28118	176	16	nuclei	nucleus	NOUN
fcis-28118	176	17	.	.	PUNCT
fcis-28118	177	1	through	through	ADP
fcis-28118	177	2	experimental	experimental	ADJ
fcis-28118	177	3	comparison	comparison	NOUN
fcis-28118	177	4	,	,	PUNCT
fcis-28118	177	5	it	it	PRON
fcis-28118	177	6	was	be	AUX
fcis-28118	177	7	found	find	VERB
fcis-28118	177	8	that	that	SCONJ
fcis-28118	177	9	the	the	DET
fcis-28118	177	10	best	good	ADJ
fcis-28118	177	11	performance	performance	NOUN
fcis-28118	177	12	was	be	AUX
fcis-28118	177	13	achieved	achieve	VERB
fcis-28118	177	14	when	when	SCONJ
fcis-28118	177	15	the	the	DET
fcis-28118	177	16	convolutional	convolutional	ADJ
fcis-28118	177	17	nuclei	nucleus	NOUN
fcis-28118	177	18	of	of	ADP
fcis-28118	177	19	ssc	ssc	NOUN
fcis-28118	177	20	layer	layer	NOUN
fcis-28118	177	21	were	be	AUX
fcis-28118	177	22	9	9	NUM
fcis-28118	177	23	and	and	CCONJ
fcis-28118	177	24	the	the	DET
fcis-28118	177	25	three	three	NUM
fcis-28118	177	26	convolutional	convolutional	ADJ
fcis-28118	177	27	nuclei	nucleus	NOUN
fcis-28118	177	28	of	of	ADP
fcis-28118	177	29	msc	msc	PROPN
fcis-28118	177	30	layer	layer	NOUN
fcis-28118	177	31	were	be	AUX
fcis-28118	177	32	set	set	VERB
fcis-28118	177	33	to	to	ADP
fcis-28118	177	34	3	3	NUM
fcis-28118	177	35	,	,	PUNCT
fcis-28118	177	36	5	5	NUM
fcis-28118	177	37	,	,	PUNCT
fcis-28118	177	38	and	and	CCONJ
fcis-28118	177	39	7	7	NUM
fcis-28118	177	40	respectively	respectively	ADV
fcis-28118	177	41	.	.	PUNCT
fcis-28118	178	1	after	after	ADP
fcis-28118	178	2	determining	determine	VERB
fcis-28118	178	3	the	the	DET
fcis-28118	178	4	structure	structure	NOUN
fcis-28118	178	5	and	and	CCONJ
fcis-28118	178	6	parameters	parameter	NOUN
fcis-28118	178	7	of	of	ADP
fcis-28118	178	8	the	the	DET
fcis-28118	178	9	model	model	NOUN
fcis-28118	178	10	,	,	PUNCT
fcis-28118	178	11	we	we	PRON
fcis-28118	178	12	tested	test	VERB
fcis-28118	178	13	different	different	ADJ
fcis-28118	178	14	initial	initial	ADJ
fcis-28118	178	15	learning	learning	NOUN
fcis-28118	178	16	rates	rate	NOUN
fcis-28118	178	17	and	and	CCONJ
fcis-28118	178	18	set	set	VERB
fcis-28118	178	19	up	up	ADP
fcis-28118	178	20	a	a	DET
fcis-28118	178	21	learning	learning	NOUN
fcis-28118	178	22	rate	rate	NOUN
fcis-28118	178	23	attenuation	attenuation	NOUN
fcis-28118	178	24	mechanism	mechanism	NOUN
fcis-28118	178	25	for	for	ADP
fcis-28118	178	26	model	model	NOUN
fcis-28118	178	27	optimization	optimization	NOUN
fcis-28118	178	28	in	in	ADP
fcis-28118	178	29	this	this	DET
fcis-28118	178	30	experiment	experiment	NOUN
fcis-28118	178	31	.	.	PUNCT
fcis-28118	179	1	table	table	NOUN
fcis-28118	179	2	3	3	NUM
fcis-28118	179	3	.	.	X
fcis-28118	180	1	influence	influence	NOUN
fcis-28118	180	2	of	of	ADP
fcis-28118	180	3	convolution	convolution	NOUN
fcis-28118	180	4	check	check	NOUN
fcis-28118	180	5	on	on	ADP
fcis-28118	180	6	model	model	NOUN
fcis-28118	180	7	performance	performance	NOUN
fcis-28118	180	8	.	.	PUNCT
fcis-28118	181	1	ssc	ssc	NOUN
fcis-28118	181	2	blocks	block	NOUN
fcis-28118	181	3	smc	smc	PROPN
fcis-28118	181	4	blocks	block	NOUN
fcis-28118	181	5	cb6133	cb6133	X
fcis-28118	181	6	_	_	PUNCT
fcis-28118	181	7	filtered	filter	VERB
fcis-28118	181	8	accuracy(%	accuracy(%	ADJ
fcis-28118	181	9	)	)	PUNCT
fcis-28118	181	10	cb513	cb513	NOUN
fcis-28118	181	11	accuracy(%	accuracy(%	ADJ
fcis-28118	181	12	)	)	PUNCT
fcis-28118	181	13	1	1	NUM
fcis-28118	181	14	1,1,1	1,1,1	NUM
fcis-28118	181	15	64.3	64.3	NUM
fcis-28118	181	16	59.8	59.8	NUM
fcis-28118	181	17	3	3	NUM
fcis-28118	181	18	3,3,3	3,3,3	NUM
fcis-28118	181	19	71.7	71.7	NUM
fcis-28118	181	20	67.4	67.4	NUM
fcis-28118	181	21	5	5	NUM
fcis-28118	181	22	5,5,5	5,5,5	NUM
fcis-28118	181	23	72.1	72.1	NUM
fcis-28118	181	24	68.0	68.0	NUM
fcis-28118	181	25	7	7	NUM
fcis-28118	181	26	7,7,7	7,7,7	NUM
fcis-28118	181	27	71.5	71.5	NUM
fcis-28118	181	28	68.2	68.2	NUM
fcis-28118	181	29	9	9	NUM
fcis-28118	181	30	9,9,9	9,9,9	NUM
fcis-28118	181	31	71.6	71.6	NUM
fcis-28118	181	32	68.1	68.1	NUM
fcis-28118	181	33	11	11	NUM
fcis-28118	181	34	11,11,11	11,11,11	NUM
fcis-28118	181	35	72.3	72.3	NUM
fcis-28118	181	36	68.3	68.3	NUM
fcis-28118	181	37	13	13	NUM
fcis-28118	181	38	13,13,13	13,13,13	NUM
fcis-28118	181	39	72.1	72.1	NUM
fcis-28118	181	40	68.4	68.4	NUM
fcis-28118	181	41	15	15	NUM
fcis-28118	181	42	15,15,15	15,15,15	NUM
fcis-28118	181	43	72.0	72.0	NUM
fcis-28118	181	44	67.6	67.6	NUM
fcis-28118	181	45	1	1	NUM
fcis-28118	181	46	3,5,7	3,5,7	NUM
fcis-28118	181	47	70.9	70.9	NUM
fcis-28118	181	48	67.5	67.5	NUM
fcis-28118	181	49	3	3	NUM
fcis-28118	181	50	5,7,9	5,7,9	NUM
fcis-28118	181	51	70.6	70.6	NUM
fcis-28118	181	52	67.1	67.1	NUM
fcis-28118	181	53	5	5	NUM
fcis-28118	181	54	7,9,11	7,9,11	NUM
fcis-28118	181	55	72.4	72.4	NUM
fcis-28118	181	56	68.2	68.2	NUM
fcis-28118	181	57	7	7	NUM
fcis-28118	181	58	9.11.13	9.11.13	NUM
fcis-28118	181	59	71.7	71.7	NUM
fcis-28118	181	60	68.0	68.0	NUM
fcis-28118	181	61	7	7	NUM
fcis-28118	181	62	1,3,5	1,3,5	NUM
fcis-28118	181	63	70.6	70.6	NUM
fcis-28118	181	64	67.6	67.6	NUM
fcis-28118	181	65	9	9	NUM
fcis-28118	181	66	3,5,7	3,5,7	NUM
fcis-28118	181	67	72.8	72.8	NUM
fcis-28118	181	68	68.5	68.5	NUM
fcis-28118	181	69	11	11	NUM
fcis-28118	181	70	5,7,9	5,7,9	NUM
fcis-28118	181	71	72.6	72.6	NUM
fcis-28118	181	72	68.2	68.2	NUM
fcis-28118	181	73	13	13	NUM
fcis-28118	181	74	7,9,11	7,9,11	NUM
fcis-28118	181	75	71.6	71.6	NUM
fcis-28118	181	76	68.0	68.0	NUM
fcis-28118	181	77	15	15	NUM
fcis-28118	181	78	9,11,13	9,11,13	NUM
fcis-28118	181	79	70.0	70.0	NUM
fcis-28118	181	80	66.6	66.6	NUM
fcis-28118	181	81	the	the	DET
fcis-28118	181	82	optimizer	optimizer	NOUN
fcis-28118	181	83	we	we	PRON
fcis-28118	181	84	used	use	VERB
fcis-28118	181	85	was	be	AUX
fcis-28118	181	86	rmsprop	rmsprop	NOUN
fcis-28118	181	87	,	,	PUNCT
fcis-28118	181	88	and	and	CCONJ
fcis-28118	181	89	a	a	DET
fcis-28118	181	90	function	function	NOUN
fcis-28118	181	91	called	call	VERB
fcis-28118	181	92	`	`	PUNCT
fcis-28118	181	93	`	`	PUNCT
fcis-28118	181	94	scheduler	scheduler	NOUN
fcis-28118	181	95	''	''	PUNCT
fcis-28118	181	96	was	be	AUX
fcis-28118	181	97	designed	design	VERB
fcis-28118	181	98	to	to	PART
fcis-28118	181	99	adjust	adjust	VERB
fcis-28118	181	100	the	the	DET
fcis-28118	181	101	learning	learning	NOUN
fcis-28118	181	102	rate	rate	NOUN
fcis-28118	181	103	.	.	PUNCT
fcis-28118	182	1	it	it	PRON
fcis-28118	182	2	can	can	AUX
fcis-28118	182	3	decide	decide	VERB
fcis-28118	182	4	whether	whether	SCONJ
fcis-28118	182	5	to	to	PART
fcis-28118	182	6	adjust	adjust	VERB
fcis-28118	182	7	the	the	DET
fcis-28118	182	8	learning	learning	NOUN
fcis-28118	182	9	rate	rate	NOUN
fcis-28118	182	10	according	accord	VERB
fcis-28118	182	11	to	to	ADP
fcis-28118	182	12	the	the	DET
fcis-28118	182	13	current	current	ADJ
fcis-28118	182	14	number	number	NOUN
fcis-28118	182	15	of	of	ADP
fcis-28118	182	16	training	training	NOUN
fcis-28118	182	17	cycles	cycle	NOUN
fcis-28118	182	18	and	and	CCONJ
fcis-28118	182	19	return	return	VERB
fcis-28118	182	20	the	the	DET
fcis-28118	182	21	new	new	ADJ
fcis-28118	182	22	learning	learning	NOUN
fcis-28118	182	23	rate	rate	NOUN
fcis-28118	182	24	.	.	PUNCT
fcis-28118	183	1	we	we	PRON
fcis-28118	183	2	also	also	ADV
fcis-28118	183	3	defined	define	VERB
fcis-28118	183	4	a	a	DET
fcis-28118	183	5	learning	learning	NOUN
fcis-28118	183	6	rate	rate	NOUN
fcis-28118	183	7	scheduler	scheduler	NOUN
fcis-28118	183	8	,	,	PUNCT
fcis-28118	183	9	which	which	PRON
fcis-28118	183	10	is	be	AUX
fcis-28118	183	11	used	use	VERB
fcis-28118	183	12	to	to	PART
fcis-28118	183	13	call	call	VERB
fcis-28118	183	14	the	the	DET
fcis-28118	183	15	function	function	NOUN
fcis-28118	183	16	`	`	PUNCT
fcis-28118	183	17	`	`	PUNCT
fcis-28118	183	18	scheduler	scheduler	NOUN
fcis-28118	183	19	''	''	PUNCT
fcis-28118	183	20	.	.	PUNCT
fcis-28118	184	1	in	in	ADP
fcis-28118	184	2	this	this	DET
fcis-28118	184	3	experiment	experiment	NOUN
fcis-28118	184	4	,	,	PUNCT
fcis-28118	184	5	we	we	PRON
fcis-28118	184	6	adopted	adopt	VERB
fcis-28118	184	7	the	the	DET
fcis-28118	184	8	same	same	ADJ
fcis-28118	184	9	learning	learning	NOUN
fcis-28118	184	10	strategy	strategy	NOUN
fcis-28118	184	11	for	for	ADP
fcis-28118	184	12	all	all	DET
fcis-28118	184	13	initial	initial	ADJ
fcis-28118	184	14	learning	learning	NOUN
fcis-28118	184	15	rates	rate	NOUN
fcis-28118	184	16	.	.	PUNCT
fcis-28118	185	1	at	at	ADP
fcis-28118	185	2	the	the	DET
fcis-28118	185	3	end	end	NOUN
fcis-28118	185	4	of	of	ADP
fcis-28118	185	5	the	the	DET
fcis-28118	185	6	60th	60th	ADJ
fcis-28118	185	7	training	training	NOUN
fcis-28118	185	8	cycle	cycle	NOUN
fcis-28118	185	9	,	,	PUNCT
fcis-28118	185	10	the	the	DET
fcis-28118	185	11	learning	learn	VERB
fcis-28118	185	12	rate	rate	NOUN
fcis-28118	185	13	of	of	ADP
fcis-28118	185	14	the	the	DET
fcis-28118	185	15	model	model	NOUN
fcis-28118	185	16	is	be	AUX
fcis-28118	185	17	halved	halve	VERB
fcis-28118	185	18	.	.	PUNCT
fcis-28118	186	1	it	it	PRON
fcis-28118	186	2	can	can	AUX
fcis-28118	186	3	be	be	AUX
fcis-28118	186	4	seen	see	VERB
fcis-28118	186	5	from	from	ADP
fcis-28118	186	6	the	the	DET
fcis-28118	186	7	table	table	NOUN
fcis-28118	186	8	4	4	NUM
fcis-28118	186	9	that	that	SCONJ
fcis-28118	186	10	only	only	ADV
fcis-28118	186	11	62.2	62.2	NUM
fcis-28118	186	12	%	%	NOUN
fcis-28118	186	13	and	and	CCONJ
fcis-28118	186	14	58.9	58.9	NUM
fcis-28118	186	15	%	%	NOUN
fcis-28118	186	16	q8	q8	PROPN
fcis-28118	186	17	accuracies	accuracy	NOUN
fcis-28118	186	18	are	be	AUX
fcis-28118	186	19	obtained	obtain	VERB
fcis-28118	186	20	on	on	ADP
fcis-28118	186	21	the	the	DET
fcis-28118	186	22	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	186	23	dataset	dataset	NOUN
fcis-28118	186	24	and	and	CCONJ
fcis-28118	186	25	cb513	cb513	NOUN
fcis-28118	186	26	dataset	dataset	VERB
fcis-28118	186	27	when	when	SCONJ
fcis-28118	186	28	the	the	DET
fcis-28118	186	29	initial	initial	ADJ
fcis-28118	186	30	learning	learning	NOUN
fcis-28118	186	31	rate	rate	NOUN
fcis-28118	186	32	is	be	AUX
fcis-28118	186	33	0.00001	0.00001	NUM
fcis-28118	186	34	.	.	PUNCT
fcis-28118	187	1	with	with	ADP
fcis-28118	187	2	the	the	DET
fcis-28118	187	3	initial	initial	ADJ
fcis-28118	187	4	learning	learning	NOUN
fcis-28118	187	5	rate	rate	NOUN
fcis-28118	187	6	set	set	VERB
fcis-28118	187	7	to	to	ADP
fcis-28118	187	8	0.0001	0.0001	NUM
fcis-28118	187	9	,	,	PUNCT
fcis-28118	187	10	the	the	DET
fcis-28118	187	11	q8	q8	PROPN
fcis-28118	187	12	accuracy	accuracy	NOUN
fcis-28118	187	13	rates	rate	NOUN
fcis-28118	187	14	are	be	AUX
fcis-28118	187	15	68	68	NUM
fcis-28118	187	16	%	%	NOUN
fcis-28118	187	17	and	and	CCONJ
fcis-28118	187	18	65.7	65.7	NUM
fcis-28118	187	19	%	%	NOUN
fcis-28118	187	20	for	for	ADP
fcis-28118	187	21	the	the	DET
fcis-28118	187	22	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	187	23	dataset	dataset	NOUN
fcis-28118	187	24	and	and	CCONJ
fcis-28118	187	25	cb513	cb513	NOUN
fcis-28118	187	26	dataset	dataset	NOUN
fcis-28118	187	27	,	,	PUNCT
fcis-28118	187	28	respectively	respectively	ADV
fcis-28118	187	29	.	.	PUNCT
fcis-28118	188	1	through	through	ADP
fcis-28118	188	2	comparison	comparison	NOUN
fcis-28118	188	3	,	,	PUNCT
fcis-28118	188	4	it	it	PRON
fcis-28118	188	5	was	be	AUX
fcis-28118	188	6	found	find	VERB
fcis-28118	188	7	that	that	SCONJ
fcis-28118	188	8	increasing	increase	VERB
fcis-28118	188	9	the	the	DET
fcis-28118	188	10	learning	learning	NOUN
fcis-28118	188	11	rate	rate	NOUN
fcis-28118	188	12	significantly	significantly	ADV
fcis-28118	188	13	helped	help	VERB
fcis-28118	188	14	improve	improve	VERB
fcis-28118	188	15	the	the	DET
fcis-28118	188	16	accuracy	accuracy	NOUN
fcis-28118	188	17	rate	rate	NOUN
fcis-28118	188	18	.	.	PUNCT
fcis-28118	189	1	after	after	ADP
fcis-28118	189	2	adjusting	adjust	VERB
fcis-28118	189	3	the	the	DET
fcis-28118	189	4	learning	learning	NOUN
fcis-28118	189	5	rate	rate	NOUN
fcis-28118	189	6	,	,	PUNCT
fcis-28118	189	7	accuracy	accuracy	NOUN
fcis-28118	189	8	rates	rate	NOUN
fcis-28118	189	9	of	of	ADP
fcis-28118	189	10	72.5	72.5	NUM
fcis-28118	189	11	%	%	NOUN
fcis-28118	189	12	and	and	CCONJ
fcis-28118	189	13	68.8	68.8	NUM
fcis-28118	189	14	%	%	NOUN
fcis-28118	189	15	were	be	AUX
fcis-28118	189	16	achieved	achieve	VERB
fcis-28118	189	17	on	on	ADP
fcis-28118	189	18	the	the	DET
fcis-28118	189	19	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	189	20	dataset	dataset	NOUN
fcis-28118	189	21	and	and	CCONJ
fcis-28118	189	22	cb513	cb513	NOUN
fcis-28118	189	23	dataset	dataset	NOUN
fcis-28118	189	24	,	,	PUNCT
fcis-28118	189	25	respectively	respectively	ADV
fcis-28118	189	26	,	,	PUNCT
fcis-28118	189	27	when	when	SCONJ
fcis-28118	189	28	the	the	DET
fcis-28118	189	29	learning	learning	NOUN
fcis-28118	189	30	rate	rate	NOUN
fcis-28118	189	31	was	be	AUX
fcis-28118	189	32	set	set	VERB
fcis-28118	189	33	to	to	ADP
fcis-28118	189	34	0.0005	0.0005	NUM
fcis-28118	189	35	.	.	PUNCT
fcis-28118	190	1	after	after	ADP
fcis-28118	190	2	increasing	increase	VERB
fcis-28118	190	3	the	the	DET
fcis-28118	190	4	learning	learning	NOUN
fcis-28118	190	5	rate	rate	NOUN
fcis-28118	190	6	to	to	ADP
fcis-28118	190	7	0.005	0.005	NUM
fcis-28118	190	8	,	,	PUNCT
fcis-28118	190	9	it	it	PRON
fcis-28118	190	10	was	be	AUX
fcis-28118	190	11	found	find	VERB
fcis-28118	190	12	that	that	SCONJ
fcis-28118	190	13	the	the	DET
fcis-28118	190	14	accuracy	accuracy	NOUN
fcis-28118	190	15	of	of	ADP
fcis-28118	190	16	q8	q8	PROPN
fcis-28118	190	17	began	begin	VERB
fcis-28118	190	18	to	to	PART
fcis-28118	190	19	decline	decline	VERB
fcis-28118	190	20	.	.	PUNCT
fcis-28118	191	1	through	through	ADP
fcis-28118	191	2	experimental	experimental	ADJ
fcis-28118	191	3	comparison	comparison	NOUN
fcis-28118	191	4	,	,	PUNCT
fcis-28118	191	5	it	it	PRON
fcis-28118	191	6	was	be	AUX
fcis-28118	191	7	determined	determine	VERB
fcis-28118	191	8	that	that	SCONJ
fcis-28118	191	9	the	the	DET
fcis-28118	191	10	learning	learn	VERB
fcis-28118	191	11	rate	rate	NOUN
fcis-28118	191	12	of	of	ADP
fcis-28118	191	13	0.0005	0.0005	NUM
fcis-28118	191	14	yielded	yield	VERB
fcis-28118	191	15	the	the	DET
fcis-28118	191	16	best	good	ADJ
fcis-28118	191	17	results	result	NOUN
fcis-28118	191	18	.	.	PUNCT
fcis-28118	192	1	table	table	NOUN
fcis-28118	192	2	4	4	NUM
fcis-28118	192	3	.	.	PUNCT
fcis-28118	193	1	influence	influence	NOUN
fcis-28118	193	2	of	of	ADP
fcis-28118	193	3	learning	learn	VERB
fcis-28118	193	4	rate	rate	NOUN
fcis-28118	193	5	on	on	ADP
fcis-28118	193	6	model	model	NOUN
fcis-28118	193	7	performance	performance	NOUN
fcis-28118	193	8	.	.	PUNCT
fcis-28118	194	1	learning	learn	VERB
fcis-28118	194	2	rate	rate	NOUN
fcis-28118	194	3	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	194	4	accuracy	accuracy	NOUN
fcis-28118	194	5	(	(	PUNCT
fcis-28118	194	6	%	%	INTJ
fcis-28118	194	7	)	)	PUNCT
fcis-28118	194	8	cb513	cb513	NOUN
fcis-28118	194	9	accuracy	accuracy	NOUN
fcis-28118	194	10	(	(	PUNCT
fcis-28118	194	11	%	%	INTJ
fcis-28118	194	12	)	)	PUNCT
fcis-28118	194	13	0.00001	0.00001	NUM
fcis-28118	194	14	62.2	62.2	NUM
fcis-28118	194	15	58.9	58.9	NUM
fcis-28118	194	16	0.0001	0.0001	NUM
fcis-28118	194	17	72.2	72.2	NUM
fcis-28118	194	18	68.3	68.3	NUM
fcis-28118	194	19	0.0005	0.0005	NUM
fcis-28118	194	20	72.5	72.5	NUM
fcis-28118	194	21	68.8	68.8	NUM
fcis-28118	194	22	0.001	0.001	NUM
fcis-28118	194	23	71.0	71.0	NUM
fcis-28118	194	24	68.2	68.2	NUM
fcis-28118	194	25	0.005	0.005	NUM
fcis-28118	194	26	66.6	66.6	NUM
fcis-28118	194	27	63.3	63.3	NUM
fcis-28118	194	28	after	after	ADP
fcis-28118	194	29	adjusting	adjust	VERB
fcis-28118	194	30	the	the	DET
fcis-28118	194	31	parameters	parameter	NOUN
fcis-28118	194	32	,	,	PUNCT
fcis-28118	194	33	the	the	DET
fcis-28118	194	34	experiment	experiment	NOUN
fcis-28118	194	35	utilized	utilize	VERB
fcis-28118	194	36	multi	multi	ADJ
fcis-28118	194	37	-	-	ADJ
fcis-28118	194	38	feature	feature	ADJ
fcis-28118	194	39	fusion	fusion	NOUN
fcis-28118	194	40	to	to	PART
fcis-28118	194	41	process	process	VERB
fcis-28118	194	42	the	the	DET
fcis-28118	194	43	data	datum	NOUN
fcis-28118	194	44	.	.	PUNCT
fcis-28118	195	1	we	we	PRON
fcis-28118	195	2	tested	test	VERB
fcis-28118	195	3	the	the	DET
fcis-28118	195	4	model	model	NOUN
fcis-28118	195	5	's	's	PART
fcis-28118	195	6	accuracy	accuracy	NOUN
fcis-28118	195	7	under	under	ADP
fcis-28118	195	8	different	different	ADJ
fcis-28118	195	9	features	feature	NOUN
fcis-28118	195	10	,	,	PUNCT
fcis-28118	195	11	primarily	primarily	ADV
fcis-28118	195	12	pssm	pssm	ADJ
fcis-28118	195	13	spectral	spectral	ADJ
fcis-28118	195	14	coding	coding	NOUN
fcis-28118	195	15	and	and	CCONJ
fcis-28118	195	16	orthogonal	orthogonal	ADJ
fcis-28118	195	17	coding	coding	NOUN
fcis-28118	195	18	,	,	PUNCT
fcis-28118	195	19	and	and	CCONJ
fcis-28118	195	20	fused	fuse	VERB
fcis-28118	195	21	these	these	DET
fcis-28118	195	22	two	two	NUM
fcis-28118	195	23	features	feature	NOUN
fcis-28118	195	24	.	.	PUNCT
fcis-28118	196	1	as	as	SCONJ
fcis-28118	196	2	shown	show	VERB
fcis-28118	196	3	in	in	ADP
fcis-28118	196	4	table5	table5	PROPN
fcis-28118	196	5	,	,	PUNCT
fcis-28118	196	6	pssm	pssm	PROPN
fcis-28118	196	7	spectral	spectral	ADJ
fcis-28118	196	8	coding	coding	NOUN
fcis-28118	196	9	contributed	contribute	VERB
fcis-28118	196	10	more	more	ADV
fcis-28118	196	11	significantly	significantly	ADV
fcis-28118	196	12	and	and	CCONJ
fcis-28118	196	13	performed	perform	VERB
fcis-28118	196	14	more	more	ADV
fcis-28118	196	15	prominently	prominently	ADV
fcis-28118	196	16	compared	compare	VERB
fcis-28118	196	17	to	to	ADP
fcis-28118	196	18	orthogonal	orthogonal	ADJ
fcis-28118	196	19	coding	coding	NOUN
fcis-28118	196	20	.	.	PUNCT
fcis-28118	197	1	the	the	DET
fcis-28118	197	2	experiment	experiment	NOUN
fcis-28118	197	3	also	also	ADV
fcis-28118	197	4	demonstrates	demonstrate	VERB
fcis-28118	197	5	that	that	SCONJ
fcis-28118	197	6	the	the	DET
fcis-28118	197	7	performance	performance	NOUN
fcis-28118	197	8	of	of	ADP
fcis-28118	197	9	all	all	DET
fcis-28118	197	10	fusion	fusion	NOUN
fcis-28118	197	11	features	feature	VERB
fcis-28118	197	12	surpasses	surpass	VERB
fcis-28118	197	13	that	that	SCONJ
fcis-28118	197	14	of	of	ADP
fcis-28118	197	15	any	any	DET
fcis-28118	197	16	single	single	ADJ
fcis-28118	197	17	29	29	NUM
fcis-28118	197	18	feature	feature	NOUN
fcis-28118	197	19	,	,	PUNCT
fcis-28118	197	20	with	with	ADP
fcis-28118	197	21	the	the	DET
fcis-28118	197	22	q8	q8	PROPN
fcis-28118	197	23	accuracy	accuracy	NOUN
fcis-28118	197	24	of	of	ADP
fcis-28118	197	25	all	all	PRON
fcis-28118	197	26	features	feature	VERB
fcis-28118	197	27	post	post	ADJ
fcis-28118	197	28	-	-	ADJ
fcis-28118	197	29	fusion	fusion	ADJ
fcis-28118	197	30	being	be	AUX
fcis-28118	197	31	the	the	DET
fcis-28118	197	32	highest	high	ADJ
fcis-28118	197	33	.	.	PUNCT
fcis-28118	198	1	this	this	PRON
fcis-28118	198	2	indicates	indicate	VERB
fcis-28118	198	3	that	that	SCONJ
fcis-28118	198	4	feature	feature	NOUN
fcis-28118	198	5	fusion	fusion	NOUN
fcis-28118	198	6	positively	positively	ADV
fcis-28118	198	7	aids	aid	NOUN
fcis-28118	198	8	in	in	ADP
fcis-28118	198	9	improving	improve	VERB
fcis-28118	198	10	accuracy	accuracy	NOUN
fcis-28118	198	11	.	.	PUNCT
fcis-28118	199	1	table	table	NOUN
fcis-28118	199	2	5	5	NUM
fcis-28118	199	3	.	.	PUNCT
fcis-28118	200	1	q8	q8	PROPN
fcis-28118	200	2	accuracy	accuracy	NOUN
fcis-28118	200	3	rate	rate	NOUN
fcis-28118	200	4	under	under	ADP
fcis-28118	200	5	various	various	ADJ
fcis-28118	200	6	feature	feature	NOUN
fcis-28118	200	7	coding	code	VERB
fcis-28118	200	8	forms	form	NOUN
fcis-28118	200	9	.	.	PUNCT
fcis-28118	201	1	feature	feature	NOUN
fcis-28118	201	2	coding	code	VERB
fcis-28118	201	3	form	form	NOUN
fcis-28118	201	4	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	201	5	accuracy(%	accuracy(%	ADJ
fcis-28118	201	6	)	)	PUNCT
fcis-28118	202	1	cb613	cb613	PROPN
fcis-28118	202	2	accuracy(%	accuracy(%	ADJ
fcis-28118	202	3	)	)	PUNCT
fcis-28118	202	4	pssm	pssm	PROPN
fcis-28118	202	5	spectral	spectral	ADJ
fcis-28118	202	6	coding	coding	NOUN
fcis-28118	202	7	combines	combine	VERB
fcis-28118	202	8	71.6	71.6	NUM
fcis-28118	202	9	67.4	67.4	NUM
fcis-28118	202	10	quadrature	quadrature	NOUN
fcis-28118	202	11	encoding	encode	VERB
fcis-28118	202	12	59.6	59.6	NUM
fcis-28118	202	13	56.1	56.1	NUM
fcis-28118	202	14	pssm	pssm	ADJ
fcis-28118	202	15	spectral	spectral	ADJ
fcis-28118	202	16	coding	coding	NOUN
fcis-28118	202	17	combined	combine	VERB
fcis-28118	202	18	with	with	ADP
fcis-28118	202	19	orthogonal	orthogonal	ADJ
fcis-28118	202	20	coding	code	VERB
fcis-28118	202	21	71.7	71.7	NUM
fcis-28118	202	22	67.7	67.7	NUM
fcis-28118	202	23	pssm	pssm	ADJ
fcis-28118	202	24	spectral	spectral	ADJ
fcis-28118	202	25	coding	coding	NOUN
fcis-28118	202	26	combines	combine	VERB
fcis-28118	202	27	physicochemical	physicochemical	ADJ
fcis-28118	202	28	properties	property	NOUN
fcis-28118	202	29	with	with	ADP
fcis-28118	202	30	logarithmic	logarithmic	ADJ
fcis-28118	202	31	relative	relative	ADJ
fcis-28118	202	32	probability	probability	NOUN
fcis-28118	202	33	71.9	71.9	NUM
fcis-28118	202	34	68.1	68.1	NUM
fcis-28118	202	35	orthogonal	orthogonal	ADJ
fcis-28118	202	36	coding	coding	NOUN
fcis-28118	202	37	combines	combine	VERB
fcis-28118	202	38	physicochemical	physicochemical	ADJ
fcis-28118	202	39	properties	property	NOUN
fcis-28118	202	40	with	with	ADP
fcis-28118	202	41	logarithmic	logarithmic	ADJ
fcis-28118	202	42	relative	relative	ADJ
fcis-28118	202	43	probability	probability	NOUN
fcis-28118	202	44	59.7	59.7	NUM
fcis-28118	202	45	56.5	56.5	NUM
fcis-28118	202	46	all	all	DET
fcis-28118	202	47	characteristics	characteristic	NOUN
fcis-28118	202	48	72.5	72.5	NUM
fcis-28118	202	49	68.8	68.8	NUM
fcis-28118	202	50	finally	finally	ADV
fcis-28118	202	51	,	,	PUNCT
fcis-28118	202	52	the	the	DET
fcis-28118	202	53	accuracy	accuracy	NOUN
fcis-28118	202	54	of	of	ADP
fcis-28118	202	55	the	the	DET
fcis-28118	202	56	cb513	cb513	NOUN
fcis-28118	202	57	dataset	dataset	NOUN
fcis-28118	202	58	is	be	AUX
fcis-28118	202	59	compared	compare	VERB
fcis-28118	202	60	with	with	ADP
fcis-28118	202	61	other	other	ADJ
fcis-28118	202	62	similarity	similarity	NOUN
fcis-28118	202	63	models	model	NOUN
fcis-28118	202	64	proposed	propose	VERB
fcis-28118	202	65	by	by	ADP
fcis-28118	202	66	researchers(as	researchers(as	ADJ
fcis-28118	202	67	table	table	NOUN
fcis-28118	202	68	6	6	NUM
fcis-28118	202	69	)	)	PUNCT
fcis-28118	202	70	,	,	PUNCT
fcis-28118	202	71	as	as	SCONJ
fcis-28118	202	72	follows	follow	VERB
fcis-28118	202	73	:	:	PUNCT
fcis-28118	202	74	deepseqvec[18	deepseqvec[18	PROPN
fcis-28118	202	75	]	]	PUNCT
fcis-28118	202	76	,	,	PUNCT
fcis-28118	202	77	deepprof+seqvec[18	deepprof+seqvec[18	PROPN
fcis-28118	202	78	]	]	X
fcis-28118	202	79	,	,	PUNCT
fcis-28118	202	80	deep	deep	ADJ
fcis-28118	202	81	-	-	PUNCT
fcis-28118	202	82	cnf	cnf	NOUN
fcis-28118	203	1	[	[	X
fcis-28118	203	2	19	19	NUM
fcis-28118	203	3	]	]	PUNCT
fcis-28118	203	4	,	,	PUNCT
fcis-28118	203	5	multi	multi	ADJ
fcis-28118	203	6	-	-	ADJ
fcis-28118	203	7	scale	scale	ADJ
fcis-28118	203	8	cnn	cnn	PROPN
fcis-28118	203	9	one	one	NUM
fcis-28118	203	10	-	-	PUNCT
fcis-28118	203	11	hot	hot	ADJ
fcis-28118	203	12	encoded[8	encoded[8	PROPN
fcis-28118	203	13	]	]	PUNCT
fcis-28118	203	14	,	,	PUNCT
fcis-28118	203	15	must	must	AUX
fcis-28118	203	16	-	-	PUNCT
fcis-28118	203	17	cnn[20	cnn[20	NOUN
fcis-28118	203	18	]	]	PUNCT
fcis-28118	203	19	,	,	PUNCT
fcis-28118	203	20	bi	bi	NOUN
fcis-28118	203	21	-	-	ADJ
fcis-28118	203	22	rnn	rnn	ADJ
fcis-28118	203	23	single	single	PROPN
fcis-28118	203	24	model[21	model[21	PROPN
fcis-28118	203	25	]	]	PUNCT
fcis-28118	203	26	,	,	PUNCT
fcis-28118	203	27	and	and	CCONJ
fcis-28118	203	28	fine	fine	ADV
fcis-28118	203	29	-	-	PUNCT
fcis-28118	203	30	tuned	tune	VERB
fcis-28118	203	31	cnn[22	cnn[22	NOUN
fcis-28118	203	32	]	]	PUNCT
fcis-28118	203	33	with	with	ADP
fcis-28118	203	34	fine	fine	ADV
fcis-28118	203	35	-	-	PUNCT
fcis-28118	203	36	tuned	tune	VERB
fcis-28118	203	37	cnn	cnn	PROPN
fcis-28118	203	38	-	-	NOUN
fcis-28118	203	39	svm[22	svm[22	PROPN
fcis-28118	203	40	]	]	PUNCT
fcis-28118	203	41	.	.	PUNCT
fcis-28118	204	1	table	table	NOUN
fcis-28118	204	2	6	6	NUM
fcis-28118	204	3	.	.	PUNCT
fcis-28118	205	1	accuracy	accuracy	NOUN
fcis-28118	205	2	of	of	ADP
fcis-28118	205	3	similarity	similarity	NOUN
fcis-28118	205	4	model	model	NOUN
fcis-28118	205	5	on	on	ADP
fcis-28118	205	6	cb513	cb513	PROPN
fcis-28118	205	7	data	datum	NOUN
fcis-28118	205	8	set	set	VERB
fcis-28118	205	9	.	.	PUNCT
fcis-28118	206	1	model	model	PROPN
fcis-28118	206	2	q8	q8	PROPN
fcis-28118	206	3	(	(	PUNCT
fcis-28118	206	4	%	%	NOUN
fcis-28118	206	5	)	)	PUNCT
fcis-28118	206	6	deepseqvec	deepseqvec	PROPN
fcis-28118	206	7	[	[	PUNCT
fcis-28118	206	8	62.5	62.5	NUM
fcis-28118	206	9	±	±	NUM
fcis-28118	206	10	0.6	0.6	NUM
fcis-28118	206	11	deepprof+seqvec	deepprof+seqvec	PROPN
fcis-28118	206	12	66.0	66.0	NUM
fcis-28118	206	13	±	±	NUM
fcis-28118	206	14	0.5	0.5	NUM
fcis-28118	206	15	deep	deep	ADJ
fcis-28118	206	16	-	-	PUNCT
fcis-28118	206	17	cnf	cnf	NOUN
fcis-28118	206	18	68.3	68.3	NUM
fcis-28118	206	19	multi	multi	ADJ
fcis-28118	206	20	-	-	ADJ
fcis-28118	206	21	scale	scale	ADJ
fcis-28118	206	22	cnn	cnn	PROPN
fcis-28118	206	23	one	one	NUM
fcis-28118	206	24	-	-	PUNCT
fcis-28118	206	25	hot	hot	NOUN
fcis-28118	206	26	encoded	encode	VERB
fcis-28118	206	27	68.3	68.3	NUM
fcis-28118	206	28	must	must	AUX
fcis-28118	206	29	-	-	PUNCT
fcis-28118	206	30	cnn	cnn	PROPN
fcis-28118	206	31	68.4	68.4	NUM
fcis-28118	206	32	bi	bi	NOUN
fcis-28118	206	33	-	-	ADJ
fcis-28118	206	34	rnn	rnn	ADJ
fcis-28118	206	35	single	single	ADJ
fcis-28118	206	36	model	model	NOUN
fcis-28118	206	37	68.5	68.5	NUM
fcis-28118	206	38	fine	fine	ADV
fcis-28118	206	39	-	-	PUNCT
fcis-28118	206	40	tuned	tune	VERB
fcis-28118	206	41	cnn	cnn	NOUN
fcis-28118	206	42	68.711	68.711	NUM
fcis-28118	206	43	fine	fine	ADV
fcis-28118	206	44	-	-	PUNCT
fcis-28118	206	45	tuned	tune	VERB
fcis-28118	206	46	cnn	cnn	PROPN
fcis-28118	206	47	-	-	PUNCT
fcis-28118	206	48	svm	svm	PROPN
fcis-28118	206	49	68.735	68.735	NUM
fcis-28118	206	50	smc	smc	NOUN
fcis-28118	206	51	-	-	VERB
fcis-28118	206	52	bigru	bigru	ADJ
fcis-28118	206	53	68.86	68.86	NUM
fcis-28118	206	54	8	8	NUM
fcis-28118	206	55	.	.	PUNCT
fcis-28118	206	56	conclusion	conclusion	NOUN
fcis-28118	206	57	after	after	ADP
fcis-28118	206	58	experimental	experimental	ADJ
fcis-28118	206	59	analysis	analysis	NOUN
fcis-28118	206	60	and	and	CCONJ
fcis-28118	206	61	comparison	comparison	NOUN
fcis-28118	206	62	,	,	PUNCT
fcis-28118	206	63	the	the	DET
fcis-28118	206	64	smcbigru	smcbigru	ADJ
fcis-28118	206	65	model	model	NOUN
fcis-28118	206	66	we	we	PRON
fcis-28118	206	67	employed	employ	VERB
fcis-28118	206	68	proves	prove	VERB
fcis-28118	206	69	capable	capable	ADJ
fcis-28118	206	70	of	of	ADP
fcis-28118	206	71	predicting	predict	VERB
fcis-28118	206	72	the	the	DET
fcis-28118	206	73	secondary	secondary	ADJ
fcis-28118	206	74	structure	structure	NOUN
fcis-28118	206	75	of	of	ADP
fcis-28118	206	76	proteins	protein	NOUN
fcis-28118	206	77	with	with	ADP
fcis-28118	206	78	commendable	commendable	ADJ
fcis-28118	206	79	performance	performance	NOUN
fcis-28118	206	80	.	.	PUNCT
fcis-28118	207	1	additionally	additionally	ADV
fcis-28118	207	2	,	,	PUNCT
fcis-28118	207	3	the	the	DET
fcis-28118	207	4	multi	multi	ADJ
fcis-28118	207	5	-	-	ADJ
fcis-28118	207	6	feature	feature	ADJ
fcis-28118	207	7	fusion	fusion	NOUN
fcis-28118	207	8	method	method	NOUN
fcis-28118	207	9	we	we	PRON
fcis-28118	207	10	adopted	adopt	VERB
fcis-28118	207	11	effectively	effectively	ADV
fcis-28118	207	12	enhances	enhance	VERB
fcis-28118	207	13	the	the	DET
fcis-28118	207	14	accuracy	accuracy	NOUN
fcis-28118	207	15	of	of	ADP
fcis-28118	207	16	model	model	NOUN
fcis-28118	207	17	prediction	prediction	NOUN
fcis-28118	207	18	.	.	PUNCT
fcis-28118	208	1	we	we	PRON
fcis-28118	208	2	fine	fine	ADV
fcis-28118	208	3	-	-	PUNCT
fcis-28118	208	4	tuned	tune	VERB
fcis-28118	208	5	various	various	ADJ
fcis-28118	208	6	parameters	parameter	NOUN
fcis-28118	208	7	of	of	ADP
fcis-28118	208	8	the	the	DET
fcis-28118	208	9	convolutional	convolutional	ADJ
fcis-28118	208	10	block	block	NOUN
fcis-28118	208	11	and	and	CCONJ
fcis-28118	208	12	gru	gru	NOUN
fcis-28118	208	13	network	network	NOUN
fcis-28118	208	14	to	to	PART
fcis-28118	208	15	identify	identify	VERB
fcis-28118	208	16	the	the	DET
fcis-28118	208	17	combination	combination	NOUN
fcis-28118	208	18	yielding	yield	VERB
fcis-28118	208	19	the	the	DET
fcis-28118	208	20	highest	high	ADJ
fcis-28118	208	21	precision	precision	NOUN
fcis-28118	208	22	.	.	PUNCT
fcis-28118	209	1	the	the	DET
fcis-28118	209	2	convolutional	convolutional	ADJ
fcis-28118	209	3	block	block	NOUN
fcis-28118	209	4	was	be	AUX
fcis-28118	209	5	designed	design	VERB
fcis-28118	209	6	to	to	PART
fcis-28118	209	7	capture	capture	VERB
fcis-28118	209	8	dependencies	dependency	NOUN
fcis-28118	209	9	within	within	ADP
fcis-28118	209	10	each	each	DET
fcis-28118	209	11	residue	residue	NOUN
fcis-28118	209	12	in	in	ADP
fcis-28118	209	13	the	the	DET
fcis-28118	209	14	amino	amino	NOUN
fcis-28118	209	15	acid	acid	NOUN
fcis-28118	209	16	sequence	sequence	NOUN
fcis-28118	209	17	effectively	effectively	ADV
fcis-28118	209	18	,	,	PUNCT
fcis-28118	209	19	while	while	SCONJ
fcis-28118	209	20	the	the	DET
fcis-28118	209	21	bidirectional	bidirectional	PROPN
fcis-28118	209	22	gru	gru	PROPN
fcis-28118	209	23	network	network	NOUN
fcis-28118	209	24	further	far	ADV
fcis-28118	209	25	captures	capture	NOUN
fcis-28118	209	26	longterm	longterm	VERB
fcis-28118	209	27	dependencies	dependency	NOUN
fcis-28118	209	28	between	between	ADP
fcis-28118	209	29	sequences	sequence	NOUN
fcis-28118	209	30	.	.	PUNCT
fcis-28118	210	1	our	our	PRON
fcis-28118	210	2	model	model	NOUN
fcis-28118	210	3	achieved	achieve	VERB
fcis-28118	210	4	a	a	DET
fcis-28118	210	5	q8	q8	PROPN
fcis-28118	210	6	accuracy	accuracy	NOUN
fcis-28118	210	7	of	of	ADP
fcis-28118	210	8	72.5	72.5	NUM
fcis-28118	210	9	%	%	NOUN
fcis-28118	210	10	on	on	ADP
fcis-28118	210	11	the	the	DET
fcis-28118	210	12	cb6133_filtered	cb6133_filtere	VERB
fcis-28118	210	13	accuracy	accuracy	NOUN
fcis-28118	210	14	dataset	dataset	NOUN
fcis-28118	210	15	and	and	CCONJ
fcis-28118	210	16	68.86	68.86	NUM
fcis-28118	210	17	%	%	NOUN
fcis-28118	210	18	on	on	ADP
fcis-28118	210	19	the	the	DET
fcis-28118	210	20	cb513	cb513	PROPN
fcis-28118	210	21	dataset	dataset	NOUN
fcis-28118	210	22	.	.	PUNCT
fcis-28118	211	1	this	this	DET
fcis-28118	211	2	experiment	experiment	NOUN
fcis-28118	211	3	confirms	confirm	VERB
fcis-28118	211	4	the	the	DET
fcis-28118	211	5	effectiveness	effectiveness	NOUN
fcis-28118	211	6	of	of	ADP
fcis-28118	211	7	our	our	PRON
fcis-28118	211	8	model	model	NOUN
fcis-28118	211	9	.	.	PUNCT
fcis-28118	212	1	acknowledgments	acknowledgment	NOUN
fcis-28118	212	2	thanks	thank	NOUN
fcis-28118	212	3	to	to	ADP
fcis-28118	212	4	professor	professor	PROPN
fcis-28118	212	5	chen	chen	PROPN
fcis-28118	212	6	xiaozhou	xiaozhou	PROPN
fcis-28118	212	7	for	for	ADP
fcis-28118	212	8	his	his	PRON
fcis-28118	212	9	guidance	guidance	NOUN
fcis-28118	212	10	on	on	ADP
fcis-28118	212	11	the	the	DET
fcis-28118	212	12	direction	direction	NOUN
fcis-28118	212	13	of	of	ADP
fcis-28118	212	14	my	my	PRON
fcis-28118	212	15	research	research	NOUN
fcis-28118	212	16	and	and	CCONJ
fcis-28118	212	17	the	the	DET
fcis-28118	212	18	significance	significance	NOUN
fcis-28118	212	19	of	of	ADP
fcis-28118	212	20	the	the	DET
fcis-28118	212	21	research	research	NOUN
fcis-28118	212	22	results	result	NOUN
fcis-28118	212	23	,	,	PUNCT
fcis-28118	212	24	and	and	CCONJ
fcis-28118	212	25	the	the	DET
fcis-28118	212	26	national	national	ADJ
fcis-28118	212	27	natural	natural	PROPN
fcis-28118	212	28	science	science	PROPN
fcis-28118	212	29	foundation	foundation	PROPN
fcis-28118	212	30	of	of	ADP
fcis-28118	212	31	china	china	PROPN
fcis-28118	212	32	(	(	PUNCT
fcis-28118	212	33	approval	approval	NOUN
fcis-28118	212	34	number	number	NOUN
fcis-28118	212	35	:	:	PUNCT
fcis-28118	212	36	31460297	31460297	NUM
fcis-28118	212	37	)	)	PUNCT
fcis-28118	212	38	for	for	ADP
fcis-28118	212	39	the	the	DET
fcis-28118	212	40	funding	funding	NOUN
fcis-28118	212	41	.	.	PUNCT
fcis-28118	213	1	references	reference	NOUN
fcis-28118	213	2	[	[	X
fcis-28118	213	3	1	1	NUM
fcis-28118	213	4	]	]	PUNCT
fcis-28118	213	5	m.	m.	NOUN
fcis-28118	213	6	zamani	zamani	PROPN
fcis-28118	213	7	and	and	CCONJ
fcis-28118	213	8	s.	s.	PROPN
fcis-28118	213	9	c.	c.	PROPN
fcis-28118	213	10	kremer	kremer	PROPN
fcis-28118	213	11	,	,	PUNCT
fcis-28118	213	12	"	"	PUNCT
fcis-28118	213	13	protein	protein	NOUN
fcis-28118	213	14	secondary	secondary	ADJ
fcis-28118	213	15	structure	structure	NOUN
fcis-28118	213	16	prediction	prediction	NOUN
fcis-28118	213	17	through	through	ADP
fcis-28118	213	18	a	a	DET
fcis-28118	213	19	novel	novel	ADJ
fcis-28118	213	20	framework	framework	NOUN
fcis-28118	213	21	of	of	ADP
fcis-28118	213	22	secondary	secondary	ADJ
fcis-28118	213	23	structure	structure	NOUN
fcis-28118	213	24	transition	transition	NOUN
fcis-28118	213	25	sites	site	NOUN
fcis-28118	213	26	and	and	CCONJ
fcis-28118	213	27	new	new	ADJ
fcis-28118	213	28	encoding	encoding	NOUN
fcis-28118	213	29	schemes	scheme	NOUN
fcis-28118	213	30	,	,	PUNCT
fcis-28118	213	31	"	"	PUNCT
fcis-28118	213	32	2016	2016	NUM
fcis-28118	213	33	ieee	ieee	NOUN
fcis-28118	213	34	conference	conference	NOUN
fcis-28118	213	35	on	on	ADP
fcis-28118	213	36	computational	computational	ADJ
fcis-28118	213	37	intelligence	intelligence	NOUN
fcis-28118	213	38	in	in	ADP
fcis-28118	213	39	bioinformatics	bioinformatics	NOUN
fcis-28118	213	40	and	and	CCONJ
fcis-28118	213	41	computational	computational	ADJ
fcis-28118	213	42	biology	biology	NOUN
fcis-28118	213	43	(	(	PUNCT
fcis-28118	213	44	cibcb	cibcb	PROPN
fcis-28118	213	45	)	)	PUNCT
fcis-28118	213	46	,	,	PUNCT
fcis-28118	213	47	chiang	chiang	PROPN
fcis-28118	213	48	mai	mai	PROPN
fcis-28118	213	49	,	,	PUNCT
fcis-28118	213	50	thailand	thailand	PROPN
fcis-28118	213	51	,	,	PUNCT
fcis-28118	213	52	2016	2016	NUM
fcis-28118	213	53	,	,	PUNCT
fcis-28118	213	54	pp	pp	ADJ
fcis-28118	213	55	.	.	PUNCT
fcis-28118	214	1	1	1	NUM
fcis-28118	214	2	-	-	SYM
fcis-28118	214	3	7	7	NUM
fcis-28118	214	4	.	.	PUNCT
fcis-28118	215	1	[	[	X
fcis-28118	215	2	2	2	NUM
fcis-28118	215	3	]	]	SYM
fcis-28118	215	4	yuedong	yuedong	PROPN
fcis-28118	215	5	yang	yang	PROPN
fcis-28118	215	6	,	,	PUNCT
fcis-28118	215	7	jianzhao	jianzhao	PROPN
fcis-28118	215	8	gao	gao	PROPN
fcis-28118	215	9	,	,	PUNCT
fcis-28118	215	10	jihua	jihua	PROPN
fcis-28118	215	11	wang	wang	PROPN
fcis-28118	215	12	,	,	PUNCT
fcis-28118	215	13	rhys	rhys	PROPN
fcis-28118	215	14	heffernan	heffernan	PROPN
fcis-28118	215	15	,	,	PUNCT
fcis-28118	215	16	jack	jack	PROPN
fcis-28118	215	17	hanson	hanson	PROPN
fcis-28118	215	18	,	,	PUNCT
fcis-28118	215	19	kuldip	kuldip	ADJ
fcis-28118	215	20	paliwal	paliwal	NOUN
fcis-28118	215	21	,	,	PUNCT
fcis-28118	215	22	yaoqi	yaoqi	PROPN
fcis-28118	215	23	zhou	zhou	PROPN
fcis-28118	215	24	,	,	PUNCT
fcis-28118	215	25	sixty	sixty	NUM
fcis-28118	215	26	-	-	PUNCT
fcis-28118	215	27	five	five	NUM
fcis-28118	215	28	years	year	NOUN
fcis-28118	215	29	of	of	ADP
fcis-28118	215	30	the	the	DET
fcis-28118	215	31	long	long	ADJ
fcis-28118	215	32	march	march	NOUN
fcis-28118	215	33	in	in	ADP
fcis-28118	215	34	protein	protein	NOUN
fcis-28118	215	35	secondary	secondary	ADJ
fcis-28118	215	36	structure	structure	NOUN
fcis-28118	215	37	prediction	prediction	NOUN
fcis-28118	215	38	:	:	PUNCT
fcis-28118	215	39	the	the	DET
fcis-28118	215	40	final	final	ADJ
fcis-28118	215	41	stretch	stretch	NOUN
fcis-28118	215	42	?	?	PUNCT
fcis-28118	215	43	,	,	PUNCT
fcis-28118	215	44	briefings	briefing	NOUN
fcis-28118	215	45	in	in	ADP
fcis-28118	215	46	bioinformatics	bioinformatics	NOUN
fcis-28118	215	47	,	,	PUNCT
fcis-28118	215	48	volume	volume	NOUN
fcis-28118	215	49	19	19	NUM
fcis-28118	215	50	,	,	PUNCT
fcis-28118	215	51	issue	issue	NOUN
fcis-28118	215	52	3	3	NUM
fcis-28118	215	53	,	,	PUNCT
fcis-28118	215	54	may	may	PROPN
fcis-28118	215	55	2018	2018	NUM
fcis-28118	215	56	,	,	PUNCT
fcis-28118	215	57	pages	page	NOUN
fcis-28118	215	58	482–494	482–494	NUM
fcis-28118	215	59	.	.	PUNCT
fcis-28118	216	1	[	[	X
fcis-28118	216	2	3	3	NUM
fcis-28118	216	3	]	]	X
fcis-28118	216	4	y	y	PROPN
fcis-28118	216	5	,	,	PUNCT
fcis-28118	216	6	y.	y.	PROPN
fcis-28118	216	7	,	,	PUNCT
fcis-28118	216	8	j	j	PROPN
fcis-28118	216	9	,	,	PUNCT
fcis-28118	216	10	g.	g.	PROPN
fcis-28118	216	11	&	&	CCONJ
fcis-28118	216	12	j.	j.	PROPN
fcis-28118	216	13	,	,	PUNCT
fcis-28118	216	14	w.	w.	PROPN
fcis-28118	216	15	sixty	sixty	NUM
fcis-28118	216	16	-	-	PUNCT
fcis-28118	216	17	five	five	NUM
fcis-28118	216	18	years	year	NOUN
fcis-28118	216	19	of	of	ADP
fcis-28118	216	20	the	the	DET
fcis-28118	216	21	long	long	ADJ
fcis-28118	216	22	march	march	NOUN
fcis-28118	216	23	in	in	ADP
fcis-28118	216	24	protein	protein	NOUN
fcis-28118	216	25	secondary	secondary	ADJ
fcis-28118	216	26	structure	structure	NOUN
fcis-28118	216	27	prediction	prediction	NOUN
fcis-28118	216	28	:	:	PUNCT
fcis-28118	216	29	the	the	DET
fcis-28118	216	30	final	final	ADJ
fcis-28118	216	31	stretch	stretch	NOUN
fcis-28118	216	32	.	.	PUNCT
fcis-28118	217	1	briefings	briefing	NOUN
fcis-28118	217	2	bioinforma	bioinforma	NOUN
fcis-28118	217	3	.	.	PUNCT
fcis-28118	218	1	19	19	NUM
fcis-28118	218	2	,	,	PUNCT
fcis-28118	218	3	482–494	482–494	NUM
fcis-28118	218	4	.	.	PUNCT
fcis-28118	219	1	[	[	X
fcis-28118	219	2	4	4	NUM
fcis-28118	219	3	]	]	X
fcis-28118	219	4	martin	martin	PROPN
fcis-28118	219	5	e.	e.	PROPN
fcis-28118	219	6	m.	m.	PROPN
fcis-28118	219	7	noble	noble	PROPN
fcis-28118	219	8	et	et	PROPN
fcis-28118	219	9	al.protein	al.protein	X
fcis-28118	219	10	kinase	kinase	VERB
fcis-28118	219	11	inhibitors	inhibitor	NOUN
fcis-28118	219	12	:	:	PUNCT
fcis-28118	219	13	insights	insight	NOUN
fcis-28118	219	14	into	into	ADP
fcis-28118	219	15	drug	drug	NOUN
fcis-28118	219	16	design	design	NOUN
fcis-28118	219	17	from	from	ADP
fcis-28118	219	18	structure.science303,1800	structure.science303,1800	PROPN
fcis-28118	219	19	-	-	NOUN
fcis-28118	219	20	1805	1805	NUM
fcis-28118	219	21	(	(	PUNCT
fcis-28118	219	22	2004	2004	NUM
fcis-28118	219	23	)	)	PUNCT
fcis-28118	219	24	.	.	PUNCT
fcis-28118	220	1	[	[	X
fcis-28118	220	2	5	5	NUM
fcis-28118	220	3	]	]	X
fcis-28118	220	4	srinivasa	srinivasa	PROPN
fcis-28118	220	5	,	,	PUNCT
fcis-28118	220	6	k.	k.	PROPN
fcis-28118	220	7	g.	g.	PROPN
fcis-28118	220	8	,	,	PUNCT
fcis-28118	220	9	siddesh	siddesh	NOUN
fcis-28118	220	10	,	,	PUNCT
fcis-28118	220	11	g.	g.	PROPN
fcis-28118	220	12	m.	m.	PROPN
fcis-28118	220	13	&	&	CCONJ
fcis-28118	220	14	manisekhar	manisekhar	PROPN
fcis-28118	220	15	,	,	PUNCT
fcis-28118	220	16	s.	s.	PROPN
fcis-28118	220	17	statistical	statistical	ADJ
fcis-28118	220	18	modelling	modelling	NOUN
fcis-28118	220	19	and	and	CCONJ
fcis-28118	220	20	machine	machine	NOUN
fcis-28118	220	21	learning	learn	VERB
fcis-28118	220	22	principles	principle	NOUN
fcis-28118	220	23	for	for	ADP
fcis-28118	220	24	bioinformatics	bioinformatics	NOUN
fcis-28118	220	25	techniques	technique	NOUN
fcis-28118	220	26	,	,	PUNCT
fcis-28118	220	27	tools	tool	NOUN
fcis-28118	220	28	,	,	PUNCT
fcis-28118	220	29	and	and	CCONJ
fcis-28118	220	30	applications	application	NOUN
fcis-28118	220	31	(	(	PUNCT
fcis-28118	220	32	springer	springer	NOUN
fcis-28118	220	33	nature	nature	NOUN
fcis-28118	220	34	,	,	PUNCT
fcis-28118	220	35	2020	2020	NUM
fcis-28118	220	36	)	)	PUNCT
fcis-28118	220	37	.	.	PUNCT
fcis-28118	221	1	[	[	X
fcis-28118	221	2	6	6	NUM
fcis-28118	221	3	]	]	SYM
fcis-28118	221	4	kabsch	kabsch	PROPN
fcis-28118	221	5	,	,	PUNCT
fcis-28118	221	6	w.	w.	PROPN
fcis-28118	221	7	&	&	CCONJ
fcis-28118	221	8	sander	sander	PROPN
fcis-28118	221	9	,	,	PUNCT
fcis-28118	221	10	c.	c.	PROPN
fcis-28118	221	11	dictionary	dictionary	NOUN
fcis-28118	221	12	of	of	ADP
fcis-28118	221	13	protein	protein	NOUN
fcis-28118	221	14	secondary	secondary	ADJ
fcis-28118	221	15	structure	structure	NOUN
fcis-28118	221	16	:	:	PUNCT
fcis-28118	221	17	pattern	pattern	NOUN
fcis-28118	221	18	recognition	recognition	NOUN
fcis-28118	221	19	of	of	ADP
fcis-28118	221	20	hydrogen	hydrogen	NOUN
fcis-28118	221	21	-	-	PUNCT
fcis-28118	221	22	bonded	bond	VERB
fcis-28118	221	23	and	and	CCONJ
fcis-28118	221	24	geometrical	geometrical	ADJ
fcis-28118	221	25	features	feature	NOUN
fcis-28118	221	26	.	.	PUNCT
fcis-28118	222	1	biopolym	biopolym	PROPN
fcis-28118	222	2	.	.	PUNCT
fcis-28118	223	1	orig	orig	PROPN
fcis-28118	223	2	.	.	PUNCT
fcis-28118	224	1	res	re	NOUN
fcis-28118	224	2	.	.	PUNCT
fcis-28118	225	1	on	on	ADP
fcis-28118	225	2	biomol	biomol	PROPN
fcis-28118	225	3	.	.	PUNCT
fcis-28118	226	1	22	22	NUM
fcis-28118	226	2	,	,	PUNCT
fcis-28118	226	3	2577–2637	2577–2637	NUM
fcis-28118	226	4	,	,	PUNCT
fcis-28118	226	5	(	(	PUNCT
fcis-28118	226	6	1983	1983	NUM
fcis-28118	226	7	)	)	PUNCT
fcis-28118	226	8	.	.	PUNCT
fcis-28118	227	1	[	[	X
fcis-28118	227	2	7	7	X
fcis-28118	227	3	]	]	SYM
fcis-28118	227	4	shutong	shutong	PROPN
fcis-28118	227	5	yang	yang	PROPN
fcis-28118	227	6	,	,	PUNCT
fcis-28118	227	7	yuhong	yuhong	PROPN
fcis-28118	227	8	wang	wang	PROPN
fcis-28118	227	9	,	,	PUNCT
fcis-28118	227	10	kennie	kennie	PROPN
fcis-28118	227	11	cruz	cruz	PROPN
fcis-28118	227	12	-	-	PUNCT
fcis-28118	227	13	gutierrez	gutierrez	PROPN
fcis-28118	227	14	et	et	PROPN
fcis-28118	227	15	al	al	PROPN
fcis-28118	227	16	.	.	PROPN
fcis-28118	227	17	localnet	localnet	PROPN
fcis-28118	227	18	:	:	PUNCT
fcis-28118	227	19	a	a	DET
fcis-28118	227	20	simple	simple	ADJ
fcis-28118	227	21	recurrent	recurrent	ADJ
fcis-28118	227	22	neural	neural	ADJ
fcis-28118	227	23	network	network	NOUN
fcis-28118	227	24	model	model	NOUN
fcis-28118	227	25	for	for	ADP
fcis-28118	227	26	protein	protein	NOUN
fcis-28118	227	27	secondary	secondary	ADJ
fcis-28118	227	28	structure	structure	NOUN
fcis-28118	227	29	prediction	prediction	NOUN
fcis-28118	227	30	using	use	VERB
fcis-28118	227	31	local	local	ADJ
fcis-28118	227	32	amino	amino	NOUN
fcis-28118	227	33	acid	acid	NOUN
fcis-28118	227	34	sequences	sequence	NOUN
fcis-28118	227	35	only	only	ADV
fcis-28118	227	36	,	,	PUNCT
fcis-28118	227	37	07	07	NUM
fcis-28118	227	38	january	january	NOUN
fcis-28118	227	39	2021	2021	NUM
fcis-28118	227	40	,	,	PUNCT
fcis-28118	227	41	preprint	preprint	NOUN
fcis-28118	227	42	(	(	PUNCT
fcis-28118	227	43	version	version	NOUN
fcis-28118	227	44	1	1	NUM
fcis-28118	227	45	)	)	PUNCT
fcis-28118	227	46	available	available	ADJ
fcis-28118	227	47	at	at	ADP
fcis-28118	227	48	research	research	NOUN
fcis-28118	227	49	square	square	NOUN
fcis-28118	227	50	.	.	PUNCT
fcis-28118	228	1	[	[	X
fcis-28118	228	2	8	8	NUM
fcis-28118	228	3	]	]	PUNCT
fcis-28118	228	4	jiyun	jiyun	NOUN
fcis-28118	228	5	,	,	PUNCT
fcis-28118	228	6	z.	z.	PROPN
fcis-28118	228	7	,	,	PUNCT
fcis-28118	228	8	hongpeng	hongpeng	PROPN
fcis-28118	228	9	,	,	PUNCT
fcis-28118	228	10	w.	w.	PROPN
fcis-28118	228	11	&	&	CCONJ
fcis-28118	228	12	z.	z.	PROPN
fcis-28118	228	13	,	,	PUNCT
fcis-28118	228	14	z.	z.	PROPN
fcis-28118	228	15	cnnh_pss	cnnh_pss	PROPN
fcis-28118	228	16	:	:	PUNCT
fcis-28118	228	17	protein	protein	NOUN
fcis-28118	228	18	8	8	NUM
fcis-28118	228	19	-	-	PUNCT
fcis-28118	228	20	class	class	NOUN
fcis-28118	228	21	secondary	secondary	ADJ
fcis-28118	228	22	structure	structure	NOUN
fcis-28118	228	23	prediction	prediction	NOUN
fcis-28118	228	24	by	by	ADP
fcis-28118	228	25	convolutional	convolutional	ADJ
fcis-28118	228	26	neural	neural	ADJ
fcis-28118	228	27	network	network	NOUN
fcis-28118	228	28	with	with	ADP
fcis-28118	228	29	highway	highway	NOUN
fcis-28118	228	30	.	.	PUNCT
fcis-28118	229	1	bmc	bmc	ADJ
fcis-28118	229	2	bioinformatics	bioinformatics	PROPN
fcis-28118	229	3	19	19	NUM
fcis-28118	229	4	,	,	PUNCT
fcis-28118	229	5	99–109	99–109	NUM
fcis-28118	229	6	,	,	PUNCT
fcis-28118	229	7	(	(	PUNCT
fcis-28118	229	8	2018	2018	NUM
fcis-28118	229	9	)	)	PUNCT
fcis-28118	229	10	.	.	PUNCT
fcis-28118	230	1	[	[	X
fcis-28118	230	2	9	9	NUM
fcis-28118	230	3	]	]	X
fcis-28118	230	4	lina	lina	PROPN
fcis-28118	230	5	,	,	PUNCT
fcis-28118	230	6	y.	y.	PROPN
fcis-28118	230	7	,	,	PUNCT
fcis-28118	230	8	pu	pu	PROPN
fcis-28118	230	9	,	,	PUNCT
fcis-28118	230	10	w.	w.	PROPN
fcis-28118	230	11	,	,	PUNCT
fcis-28118	230	12	zhong	zhong	PROPN
fcis-28118	230	13	,	,	PUNCT
fcis-28118	230	14	x	x	PROPN
fcis-28118	230	15	,	,	PUNCT
fcis-28118	230	16	l.	l.	PROPN
fcis-28118	230	17	&	&	CCONJ
fcis-28118	230	18	y.	y.	PROPN
fcis-28118	230	19	,	,	PUNCT
fcis-28118	230	20	t.	t.	PROPN
fcis-28118	230	21	y.	y.	NOUN
fcis-28118	230	22	protein	protein	NOUN
fcis-28118	230	23	structure	structure	NOUN
fcis-28118	230	24	prediction	prediction	NOUN
fcis-28118	230	25	based	base	VERB
fcis-28118	230	26	on	on	ADP
fcis-28118	230	27	bn	bn	PROPN
fcis-28118	230	28	-	-	PUNCT
fcis-28118	230	29	gru	gru	NOUN
fcis-28118	230	30	method	method	NOUN
fcis-28118	230	31	.	.	PUNCT
fcis-28118	230	32	int	int	NOUN
fcis-28118	230	33	.	.	PUNCT
fcis-28118	231	1	j.	j.	PROPN
fcis-28118	231	2	wavelets	wavelets	PROPN
fcis-28118	231	3	,	,	PUNCT
fcis-28118	231	4	multiresolution	multiresolution	NOUN
fcis-28118	231	5	inf	inf	NOUN
fcis-28118	231	6	.	.	PUNCT
fcis-28118	231	7	process	process	NOUN
fcis-28118	231	8	.	.	PUNCT
fcis-28118	232	1	18	18	NUM
fcis-28118	232	2	,	,	PUNCT
fcis-28118	232	3	2050045	2050045	NUM
fcis-28118	232	4	,	,	PUNCT
fcis-28118	232	5	(	(	PUNCT
fcis-28118	232	6	2020	2020	NUM
fcis-28118	232	7	)	)	PUNCT
fcis-28118	232	8	.	.	PUNCT
fcis-28118	233	1	[	[	X
fcis-28118	233	2	10	10	NUM
fcis-28118	233	3	]	]	X
fcis-28118	233	4	l	l	NOUN
fcis-28118	233	5	,	,	PUNCT
fcis-28118	233	6	w.	w.	PROPN
fcis-28118	233	7	a.	a.	PROPN
fcis-28118	233	8	&	&	CCONJ
fcis-28118	233	9	l.	l.	PROPN
fcis-28118	233	10	,	,	PUNCT
fcis-28118	233	11	m.	m.	NOUN
fcis-28118	233	12	s.	s.	PROPN
fcis-28118	233	13	reasons	reason	NOUN
fcis-28118	233	14	for	for	ADP
fcis-28118	233	15	the	the	DET
fcis-28118	233	16	occurrence	occurrence	NOUN
fcis-28118	233	17	of	of	ADP
fcis-28118	233	18	the	the	DET
fcis-28118	233	19	twenty	twenty	NUM
fcis-28118	233	20	coded	code	VERB
fcis-28118	233	21	protein	protein	NOUN
fcis-28118	233	22	amino	amino	NOUN
fcis-28118	233	23	acids	acid	NOUN
fcis-28118	233	24	.	.	PUNCT
fcis-28118	234	1	j.	j.	PROPN
fcis-28118	234	2	mol	mol	PROPN
fcis-28118	234	3	.	.	PROPN
fcis-28118	234	4	evol	evol	NOUN
fcis-28118	234	5	.	.	PUNCT
fcis-28118	235	1	17	17	NUM
fcis-28118	235	2	,	,	PUNCT
fcis-28118	235	3	273–284	273–284	NUM
fcis-28118	235	4	,	,	PUNCT
fcis-28118	235	5	(	(	PUNCT
fcis-28118	235	6	1981	1981	NUM
fcis-28118	235	7	)	)	PUNCT
fcis-28118	235	8	.	.	PUNCT
fcis-28118	236	1	[	[	X
fcis-28118	236	2	11	11	NUM
fcis-28118	236	3	]	]	PUNCT
fcis-28118	236	4	ashraf	ashraf	NOUN
fcis-28118	236	5	,	,	PUNCT
fcis-28118	236	6	y.	y.	PROPN
fcis-28118	236	7	&	&	CCONJ
fcis-28118	236	8	li	li	PROPN
fcis-28118	236	9	,	,	PUNCT
fcis-28118	236	10	y.	y.	PROPN
fcis-28118	236	11	template	template	NOUN
fcis-28118	236	12	-	-	PUNCT
fcis-28118	236	13	based	base	VERB
fcis-28118	236	14	c8	c8	NOUN
fcis-28118	236	15	-	-	PUNCT
fcis-28118	236	16	scorpion	scorpion	NOUN
fcis-28118	236	17	:	:	PUNCT
fcis-28118	236	18	a	a	DET
fcis-28118	236	19	protein	protein	NOUN
fcis-28118	236	20	8state	8state	NUM
fcis-28118	236	21	secondary	secondary	ADJ
fcis-28118	236	22	structure	structure	NOUN
fcis-28118	236	23	prediction	prediction	NOUN
fcis-28118	236	24	method	method	NOUN
fcis-28118	236	25	using	use	VERB
fcis-28118	236	26	structural	structural	ADJ
fcis-28118	236	27	information	information	NOUN
fcis-28118	236	28	and	and	CCONJ
fcis-28118	236	29	context	context	NOUN
fcis-28118	236	30	-	-	PUNCT
fcis-28118	236	31	based	base	VERB
fcis-28118	236	32	features	feature	NOUN
fcis-28118	236	33	.	.	PUNCT
fcis-28118	237	1	bmc	bmc	ADJ
fcis-28118	237	2	bioinformatics	bioinformatic	NOUN
fcis-28118	237	3	15	15	NUM
fcis-28118	237	4	,	,	PUNCT
fcis-28118	237	5	1–8	1–8	NUM
fcis-28118	237	6	,	,	PUNCT
fcis-28118	237	7	(	(	PUNCT
fcis-28118	237	8	2014	2014	NUM
fcis-28118	237	9	)	)	PUNCT
fcis-28118	237	10	.	.	PUNCT
fcis-28118	238	1	[	[	X
fcis-28118	238	2	12	12	NUM
fcis-28118	238	3	]	]	X
fcis-28118	238	4	zhao	zhao	X
fcis-28118	238	5	,	,	PUNCT
fcis-28118	238	6	y.	y.	PROPN
fcis-28118	238	7	&	&	CCONJ
fcis-28118	238	8	liu	liu	PROPN
fcis-28118	238	9	,	,	PUNCT
fcis-28118	238	10	y.	y.	PROPN
fcis-28118	238	11	oclstm	oclstm	PROPN
fcis-28118	238	12	:	:	PUNCT
fcis-28118	238	13	optimized	optimize	VERB
fcis-28118	238	14	convolutional	convolutional	ADJ
fcis-28118	238	15	and	and	CCONJ
fcis-28118	238	16	long	long	ADJ
fcis-28118	238	17	short	short	ADJ
fcis-28118	238	18	-	-	PUNCT
fcis-28118	238	19	term	term	NOUN
fcis-28118	238	20	memory	memory	NOUN
fcis-28118	238	21	neural	neural	ADJ
fcis-28118	238	22	network	network	NOUN
fcis-28118	238	23	model	model	NOUN
fcis-28118	238	24	for	for	ADP
fcis-28118	238	25	protein	protein	NOUN
fcis-28118	238	26	secondary	secondary	ADJ
fcis-28118	238	27	structure	structure	NOUN
fcis-28118	238	28	prediction	prediction	NOUN
fcis-28118	238	29	.	.	PUNCT
fcis-28118	239	1	plos	plos	PROPN
fcis-28118	239	2	one	one	NUM
fcis-28118	239	3	16	16	NUM
fcis-28118	239	4	,	,	PUNCT
fcis-28118	239	5	e0245982	e0245982	NOUN
fcis-28118	239	6	,	,	PUNCT
fcis-28118	239	7	doi	doi	NOUN
fcis-28118	239	8	:	:	PUNCT
fcis-28118	239	9	https://doi.org/10.1371/journal.pone.0245982	https://doi.org/10.1371/journal.pone.0245982	PROPN
fcis-28118	239	10	(	(	PUNCT
fcis-28118	239	11	2021	2021	NUM
fcis-28118	239	12	)	)	PUNCT
fcis-28118	239	13	.	.	PUNCT
fcis-28118	240	1	30	30	NUM
fcis-28118	241	1	[	[	SYM
fcis-28118	241	2	13	13	NUM
fcis-28118	241	3	]	]	PUNCT
fcis-28118	241	4	cuff	cuff	NOUN
fcis-28118	241	5	,	,	PUNCT
fcis-28118	241	6	j.	j.	PROPN
fcis-28118	241	7	a.	a.	PROPN
fcis-28118	241	8	&	&	CCONJ
fcis-28118	241	9	barton	barton	PROPN
fcis-28118	241	10	,	,	PUNCT
fcis-28118	241	11	g.	g.	PROPN
fcis-28118	241	12	j.	j.	PROPN
fcis-28118	241	13	evaluation	evaluation	PROPN
fcis-28118	241	14	and	and	CCONJ
fcis-28118	241	15	improvement	improvement	NOUN
fcis-28118	241	16	of	of	ADP
fcis-28118	241	17	multiple	multiple	ADJ
fcis-28118	241	18	sequence	sequence	NOUN
fcis-28118	241	19	methods	method	NOUN
fcis-28118	241	20	for	for	ADP
fcis-28118	241	21	protein	protein	NOUN
fcis-28118	241	22	secondary	secondary	ADJ
fcis-28118	241	23	structure	structure	NOUN
fcis-28118	241	24	prediction	prediction	NOUN
fcis-28118	241	25	.	.	PUNCT
fcis-28118	242	1	proteins	protein	NOUN
fcis-28118	242	2	:	:	PUNCT
fcis-28118	242	3	struct	struct	NOUN
fcis-28118	242	4	.	.	PUNCT
fcis-28118	243	1	funct	funct	PROPN
fcis-28118	243	2	.	.	PUNCT
fcis-28118	243	3	bioinforma	bioinforma	NOUN
fcis-28118	243	4	.	.	PUNCT
fcis-28118	244	1	34	34	NUM
fcis-28118	244	2	,	,	PUNCT
fcis-28118	244	3	508–519	508–519	NUM
fcis-28118	244	4	,	,	PUNCT
fcis-28118	244	5	(	(	PUNCT
fcis-28118	244	6	1999	1999	NUM
fcis-28118	244	7	)	)	PUNCT
fcis-28118	244	8	.	.	PUNCT
fcis-28118	245	1	[	[	X
fcis-28118	245	2	14	14	NUM
fcis-28118	245	3	]	]	X
fcis-28118	245	4	tomasz	tomasz	NOUN
fcis-28118	245	5	,	,	PUNCT
fcis-28118	245	6	s.	s.	PROPN
fcis-28118	245	7	&	&	CCONJ
fcis-28118	245	8	k.	k.	PROPN
fcis-28118	245	9	,	,	PUNCT
fcis-28118	245	10	r.-k	r.-k	PROPN
fcis-28118	245	11	.	.	PUNCT
fcis-28118	246	1	i.	i.	PROPN
fcis-28118	246	2	s.	s.	PROPN
fcis-28118	246	3	protein	protein	NOUN
fcis-28118	246	4	secondary	secondary	ADJ
fcis-28118	246	5	structure	structure	NOUN
fcis-28118	246	6	prediction	prediction	NOUN
fcis-28118	246	7	:	:	PUNCT
fcis-28118	246	8	a	a	DET
fcis-28118	246	9	review	review	NOUN
fcis-28118	246	10	of	of	ADP
fcis-28118	246	11	progress	progress	NOUN
fcis-28118	246	12	and	and	CCONJ
fcis-28118	246	13	directions	direction	NOUN
fcis-28118	246	14	.	.	PUNCT
fcis-28118	247	1	curr	curr	PROPN
fcis-28118	247	2	.	.	PUNCT
fcis-28118	247	3	bioinforma	bioinforma	NOUN
fcis-28118	247	4	.	.	PUNCT
fcis-28118	248	1	15	15	NUM
fcis-28118	248	2	,	,	PUNCT
fcis-28118	248	3	90–107	90–107	NUM
fcis-28118	248	4	,	,	PUNCT
fcis-28118	248	5	(	(	PUNCT
fcis-28118	248	6	2020	2020	NUM
fcis-28118	248	7	)	)	PUNCT
fcis-28118	248	8	.	.	PUNCT
fcis-28118	249	1	[	[	X
fcis-28118	249	2	15	15	NUM
fcis-28118	249	3	]	]	X
fcis-28118	249	4	f	f	X
fcis-28118	249	5	,	,	PUNCT
fcis-28118	249	6	a.	a.	PROPN
fcis-28118	249	7	s.	s.	PROPN
fcis-28118	249	8	,	,	PUNCT
fcis-28118	249	9	m	m	PROPN
fcis-28118	249	10	,	,	PUNCT
fcis-28118	249	11	g.	g.	PROPN
fcis-28118	249	12	e.	e.	PROPN
fcis-28118	249	13	,	,	PUNCT
fcis-28118	249	14	r.	r.	PROPN
fcis-28118	249	15	,	,	PUNCT
fcis-28118	249	16	a.	a.	PROPN
fcis-28118	249	17	,	,	PUNCT
fcis-28118	249	18	a.	a.	PROPN
fcis-28118	249	19	,	,	PUNCT
fcis-28118	249	20	s.	s.	PROPN
fcis-28118	249	21	a.	a.	PROPN
fcis-28118	249	22	&	&	CCONJ
fcis-28118	249	23	yu	yu	PROPN
fcis-28118	249	24	,	,	PUNCT
fcis-28118	249	25	y.	y.	PROPN
fcis-28118	249	26	k.	k.	PROPN
fcis-28118	249	27	psi	psi	PROPN
fcis-28118	249	28	-	-	PUNCT
fcis-28118	249	29	blast	blast	NOUN
fcis-28118	249	30	pseudocounts	pseudocount	NOUN
fcis-28118	249	31	and	and	CCONJ
fcis-28118	249	32	the	the	DET
fcis-28118	249	33	minimum	minimum	ADJ
fcis-28118	249	34	description	description	NOUN
fcis-28118	249	35	length	length	NOUN
fcis-28118	249	36	principle	principle	NOUN
fcis-28118	249	37	.	.	PUNCT
fcis-28118	250	1	nucleic	nucleic	ADJ
fcis-28118	250	2	acids	acid	NOUN
fcis-28118	250	3	research	research	NOUN
fcis-28118	250	4	37	37	NUM
fcis-28118	250	5	,	,	PUNCT
fcis-28118	250	6	815–824	815–824	NUM
fcis-28118	250	7	,	,	PUNCT
fcis-28118	250	8	(	(	PUNCT
fcis-28118	250	9	2009	2009	NUM
fcis-28118	250	10	)	)	PUNCT
fcis-28118	250	11	.	.	PUNCT
fcis-28118	251	1	[	[	X
fcis-28118	251	2	16	16	NUM
fcis-28118	251	3	]	]	SYM
fcis-28118	251	4	f	f	X
fcis-28118	251	5	,	,	PUNCT
fcis-28118	251	6	a.	a.	PROPN
fcis-28118	251	7	s.	s.	PROPN
fcis-28118	251	8	et	et	PROPN
fcis-28118	251	9	al	al	PROPN
fcis-28118	251	10	.	.	PROPN
fcis-28118	251	11	gapped	gapped	NOUN
fcis-28118	251	12	blast	blast	NOUN
fcis-28118	251	13	and	and	CCONJ
fcis-28118	251	14	psi	psi	NOUN
fcis-28118	251	15	-	-	PUNCT
fcis-28118	251	16	blast	blast	NOUN
fcis-28118	251	17	:	:	PUNCT
fcis-28118	251	18	a	a	DET
fcis-28118	251	19	new	new	ADJ
fcis-28118	251	20	generation	generation	NOUN
fcis-28118	251	21	of	of	ADP
fcis-28118	251	22	protein	protein	NOUN
fcis-28118	251	23	database	database	NOUN
fcis-28118	251	24	search	search	NOUN
fcis-28118	251	25	programs	program	NOUN
fcis-28118	251	26	.	.	PUNCT
fcis-28118	252	1	nucleic	nucleic	ADJ
fcis-28118	252	2	acids	acid	NOUN
fcis-28118	252	3	research	research	NOUN
fcis-28118	252	4	25	25	NUM
fcis-28118	252	5	,	,	PUNCT
fcis-28118	252	6	3389–3402	3389–3402	NUM
fcis-28118	252	7	,	,	PUNCT
fcis-28118	252	8	(	(	PUNCT
fcis-28118	252	9	1997	1997	NUM
fcis-28118	252	10	)	)	PUNCT
fcis-28118	252	11	.	.	PUNCT
fcis-28118	253	1	[	[	X
fcis-28118	253	2	17	17	NUM
fcis-28118	253	3	]	]	SYM
fcis-28118	253	4	xiaoyang	xiaoyang	PROPN
fcis-28118	253	5	,	,	PUNCT
fcis-28118	253	6	j.	j.	PROPN
fcis-28118	253	7	,	,	PUNCT
fcis-28118	253	8	qiwen	qiwen	PROPN
fcis-28118	253	9	,	,	PUNCT
fcis-28118	253	10	d.	d.	PROPN
fcis-28118	253	11	,	,	PUNCT
fcis-28118	253	12	d	d	PROPN
fcis-28118	253	13	,	,	PUNCT
fcis-28118	253	14	h.	h.	PROPN
fcis-28118	253	15	&	&	CCONJ
fcis-28118	253	16	r.	r.	PROPN
fcis-28118	253	17	,	,	PUNCT
fcis-28118	253	18	l.	l.	PROPN
fcis-28118	253	19	amino	amino	PROPN
fcis-28118	253	20	acid	acid	PROPN
fcis-28118	253	21	encoding	encoding	NOUN
fcis-28118	253	22	methods	method	NOUN
fcis-28118	253	23	for	for	ADP
fcis-28118	253	24	protein	protein	NOUN
fcis-28118	253	25	sequences	sequence	NOUN
fcis-28118	253	26	:	:	PUNCT
fcis-28118	253	27	a	a	DET
fcis-28118	253	28	comprehensive	comprehensive	ADJ
fcis-28118	253	29	review	review	NOUN
fcis-28118	253	30	and	and	CCONJ
fcis-28118	253	31	assessment	assessment	NOUN
fcis-28118	253	32	.	.	PUNCT
fcis-28118	254	1	ieee	ieee	PROPN
fcis-28118	254	2	/	/	SYM
fcis-28118	254	3	acm	acm	PROPN
fcis-28118	254	4	transactions	transaction	NOUN
fcis-28118	254	5	on	on	ADP
fcis-28118	254	6	computational	computational	ADJ
fcis-28118	254	7	biology	biology	NOUN
fcis-28118	254	8	bioinformatics	bioinformatic	NOUN
fcis-28118	254	9	17	17	NUM
fcis-28118	254	10	,	,	PUNCT
fcis-28118	254	11	1918–1931	1918–1931	NUM
fcis-28118	254	12	,	,	PUNCT
fcis-28118	254	13	(	(	PUNCT
fcis-28118	254	14	2019	2019	NUM
fcis-28118	254	15	)	)	PUNCT
fcis-28118	254	16	.	.	PUNCT
fcis-28118	255	1	[	[	X
fcis-28118	255	2	18	18	NUM
fcis-28118	255	3	]	]	X
fcis-28118	255	4	michael	michael	PROPN
fcis-28118	255	5	,	,	PUNCT
fcis-28118	255	6	h.	h.	PROPN
fcis-28118	255	7	,	,	PUNCT
fcis-28118	255	8	a	a	PRON
fcis-28118	255	9	,	,	PUNCT
fcis-28118	255	10	e.	e.	PROPN
fcis-28118	255	11	&	&	CCONJ
fcis-28118	255	12	wang	wang	PROPN
fcis-28118	255	13	,	,	PUNCT
fcis-28118	255	14	y.	y.	PROPN
fcis-28118	255	15	modeling	model	VERB
fcis-28118	255	16	aspects	aspect	NOUN
fcis-28118	255	17	of	of	ADP
fcis-28118	255	18	the	the	DET
fcis-28118	255	19	language	language	NOUN
fcis-28118	255	20	of	of	ADP
fcis-28118	255	21	life	life	NOUN
fcis-28118	255	22	through	through	ADP
fcis-28118	255	23	transfer	transfer	NOUN
fcis-28118	255	24	-	-	PUNCT
fcis-28118	255	25	learning	learn	VERB
fcis-28118	255	26	protein	protein	NOUN
fcis-28118	255	27	sequences	sequence	NOUN
fcis-28118	255	28	.	.	PUNCT
fcis-28118	256	1	bmc	bmc	ADJ
fcis-28118	256	2	bioinformatics	bioinformatics	NOUN
fcis-28118	256	3	20	20	NUM
fcis-28118	256	4	,	,	PUNCT
fcis-28118	256	5	1–17	1–17	PROPN
fcis-28118	256	6	,	,	PUNCT
fcis-28118	256	7	(	(	PUNCT
fcis-28118	256	8	2019	2019	NUM
fcis-28118	256	9	)	)	PUNCT
fcis-28118	256	10	.	.	PUNCT
fcis-28118	257	1	[	[	X
fcis-28118	257	2	19	19	NUM
fcis-28118	257	3	]	]	X
fcis-28118	257	4	peng	peng	PROPN
fcis-28118	257	5	,	,	PUNCT
fcis-28118	257	6	w.	w.	PROPN
fcis-28118	257	7	s.	s.	PROPN
fcis-28118	257	8	,	,	PUNCT
fcis-28118	257	9	ma	ma	PROPN
fcis-28118	257	10	,	,	PUNCT
fcis-28118	257	11	j.	j.	PROPN
fcis-28118	257	12	,	,	PUNCT
fcis-28118	257	13	j	j	PROPN
fcis-28118	257	14	&	&	CCONJ
fcis-28118	257	15	j.	j.	PROPN
fcis-28118	257	16	,	,	PUNCT
fcis-28118	257	17	x.	x.	NOUN
fcis-28118	257	18	protein	protein	NOUN
fcis-28118	257	19	secondary	secondary	ADJ
fcis-28118	257	20	structure	structure	NOUN
fcis-28118	257	21	prediction	prediction	NOUN
fcis-28118	257	22	using	use	VERB
fcis-28118	257	23	deep	deep	ADJ
fcis-28118	257	24	convolutional	convolutional	ADJ
fcis-28118	257	25	neural	neural	ADJ
fcis-28118	257	26	fields	field	NOUN
fcis-28118	257	27	.	.	PUNCT
fcis-28118	258	1	sci	sci	PROPN
fcis-28118	258	2	.	.	PROPN
fcis-28118	258	3	reports	report	VERB
fcis-28118	258	4	6	6	NUM
fcis-28118	258	5	,	,	PUNCT
fcis-28118	258	6	1–11	1–11	PROPN
fcis-28118	258	7	,	,	PUNCT
fcis-28118	258	8	(	(	PUNCT
fcis-28118	258	9	2016	2016	NUM
fcis-28118	258	10	)	)	PUNCT
fcis-28118	258	11	.	.	PUNCT
fcis-28118	259	1	[	[	X
fcis-28118	259	2	20	20	NUM
fcis-28118	259	3	]	]	X
fcis-28118	259	4	zeming	zeming	PROPN
fcis-28118	259	5	,	,	PUNCT
fcis-28118	259	6	l.	l.	PROPN
fcis-28118	259	7	,	,	PUNCT
fcis-28118	259	8	j	j	PROPN
fcis-28118	259	9	,	,	PUNCT
fcis-28118	259	10	l.	l.	PROPN
fcis-28118	259	11	&	&	CCONJ
fcis-28118	259	12	y.	y.	PROPN
fcis-28118	259	13	,	,	PUNCT
fcis-28118	259	14	q.	q.	PROPN
fcis-28118	259	15	must	must	AUX
fcis-28118	259	16	-	-	PUNCT
fcis-28118	259	17	cnn	cnn	PROPN
fcis-28118	259	18	:	:	PUNCT
fcis-28118	259	19	a	a	DET
fcis-28118	259	20	multilayer	multilayer	ADJ
fcis-28118	259	21	shift	shift	NOUN
fcis-28118	259	22	-	-	PUNCT
fcis-28118	259	23	andstitch	andstitch	NOUN
fcis-28118	259	24	deep	deep	ADJ
fcis-28118	259	25	convolutional	convolutional	ADJ
fcis-28118	259	26	architecture	architecture	NOUN
fcis-28118	259	27	for	for	ADP
fcis-28118	259	28	sequence	sequence	NOUN
fcis-28118	259	29	-	-	PUNCT
fcis-28118	259	30	based	base	VERB
fcis-28118	259	31	protein	protein	NOUN
fcis-28118	259	32	structure	structure	NOUN
fcis-28118	259	33	prediction	prediction	NOUN
fcis-28118	259	34	.	.	PUNCT
fcis-28118	260	1	in	in	ADP
fcis-28118	260	2	proceedings	proceeding	NOUN
fcis-28118	260	3	of	of	ADP
fcis-28118	260	4	the	the	DET
fcis-28118	260	5	aaai	aaai	PROPN
fcis-28118	260	6	conference	conference	NOUN
fcis-28118	260	7	on	on	ADP
fcis-28118	260	8	artificial	artificial	ADJ
fcis-28118	260	9	intelligence	intelligence	NOUN
fcis-28118	260	10	,	,	PUNCT
fcis-28118	260	11	vol	vol	NOUN
fcis-28118	260	12	.	.	PROPN
fcis-28118	260	13	30	30	NUM
fcis-28118	260	14	,	,	PUNCT
fcis-28118	260	15	(	(	PUNCT
fcis-28118	260	16	2016	2016	NUM
fcis-28118	260	17	)	)	PUNCT
fcis-28118	260	18	.	.	PUNCT
fcis-28118	261	1	[	[	X
fcis-28118	261	2	21	21	NUM
fcis-28118	261	3	]	]	X
fcis-28118	261	4	rosenberg	rosenberg	PROPN
fcis-28118	261	5	,	,	PUNCT
fcis-28118	261	6	j.	j.	PROPN
fcis-28118	261	7	a.	a.	PROPN
fcis-28118	261	8	,	,	PUNCT
fcis-28118	261	9	sønderby	sønderby	PROPN
fcis-28118	261	10	,	,	PUNCT
fcis-28118	261	11	c.	c.	PROPN
fcis-28118	261	12	k.	k.	PROPN
fcis-28118	261	13	,	,	PUNCT
fcis-28118	261	14	sønderby	sønderby	PROPN
fcis-28118	261	15	,	,	PUNCT
fcis-28118	261	16	s.	s.	PROPN
fcis-28118	261	17	k.	k.	PROPN
fcis-28118	261	18	&	&	CCONJ
fcis-28118	261	19	winther	winther	PROPN
fcis-28118	261	20	,	,	PUNCT
fcis-28118	261	21	o.	o.	PROPN
fcis-28118	261	22	deep	deep	ADJ
fcis-28118	261	23	recurrent	recurrent	ADJ
fcis-28118	261	24	conditional	conditional	ADJ
fcis-28118	261	25	random	random	ADJ
fcis-28118	261	26	field	field	NOUN
fcis-28118	261	27	network	network	NOUN
fcis-28118	261	28	for	for	ADP
fcis-28118	261	29	protein	protein	NOUN
fcis-28118	261	30	secondary	secondary	ADJ
fcis-28118	261	31	prediction	prediction	NOUN
fcis-28118	261	32	.	.	PUNCT
fcis-28118	262	1	in	in	ADP
fcis-28118	262	2	proceedings	proceeding	NOUN
fcis-28118	262	3	of	of	ADP
fcis-28118	262	4	the	the	DET
fcis-28118	262	5	8th	8th	PROPN
fcis-28118	262	6	acm	acm	PROPN
fcis-28118	262	7	international	international	ADJ
fcis-28118	262	8	conference	conference	NOUN
fcis-28118	262	9	on	on	ADP
fcis-28118	262	10	bioinformatics	bioinformatics	NOUN
fcis-28118	262	11	,	,	PUNCT
fcis-28118	262	12	computational	computational	ADJ
fcis-28118	262	13	biology	biology	NOUN
fcis-28118	262	14	,	,	PUNCT
fcis-28118	262	15	and	and	CCONJ
fcis-28118	262	16	health	health	NOUN
fcis-28118	262	17	informatics	informatic	NOUN
fcis-28118	262	18	,	,	PUNCT
fcis-28118	262	19	73–78	73–78	NUM
fcis-28118	262	20	,	,	PUNCT
fcis-28118	262	21	(	(	PUNCT
fcis-28118	262	22	2017	2017	NUM
fcis-28118	262	23	)	)	PUNCT
fcis-28118	262	24	.	.	PUNCT
fcis-28118	263	1	[	[	X
fcis-28118	263	2	22	22	NUM
fcis-28118	263	3	]	]	X
fcis-28118	263	4	michael	michael	PROPN
fcis-28118	263	5	,	,	PUNCT
fcis-28118	263	6	s.	s.	PROPN
fcis-28118	263	7	v.	v.	PROPN
fcis-28118	263	8	,	,	PUNCT
fcis-28118	263	9	z.	z.	PROPN
fcis-28118	263	10	,	,	PUNCT
fcis-28118	263	11	s.	s.	PROPN
fcis-28118	263	12	&	&	CCONJ
fcis-28118	263	13	a.	a.	PROPN
fcis-28118	263	14	,	,	PUNCT
fcis-28118	263	15	a.	a.	NOUN
fcis-28118	263	16	predicting	predict	VERB
fcis-28118	263	17	secondary	secondary	ADJ
fcis-28118	263	18	structure	structure	NOUN
fcis-28118	263	19	of	of	ADP
fcis-28118	263	20	protein	protein	NOUN
fcis-28118	263	21	using	use	VERB
fcis-28118	263	22	hybrid	hybrid	NOUN
fcis-28118	263	23	of	of	ADP
fcis-28118	263	24	convolutional	convolutional	ADJ
fcis-28118	263	25	neural	neural	ADJ
fcis-28118	263	26	network	network	NOUN
fcis-28118	263	27	and	and	CCONJ
fcis-28118	263	28	support	support	VERB
fcis-28118	263	29	vector	vector	NOUN
fcis-28118	263	30	machine	machine	NOUN
fcis-28118	263	31	.	.	PUNCT
fcis-28118	264	1	int	int	NOUN
fcis-28118	264	2	.	.	PUNCT
fcis-28118	265	1	j.	j.	PROPN
fcis-28118	265	2	intell	intell	PROPN
fcis-28118	265	3	.	.	PUNCT
fcis-28118	266	1	eng	eng	PROPN
fcis-28118	266	2	.	.	PROPN
fcis-28118	266	3	&	&	CCONJ
fcis-28118	266	4	syst	syst	PROPN
fcis-28118	266	5	.	.	PUNCT
fcis-28118	267	1	14	14	NUM
fcis-28118	267	2	,	,	PUNCT
fcis-28118	267	3	(	(	PUNCT
fcis-28118	267	4	2022	2022	NUM
fcis-28118	267	5	)	)	PUNCT
fcis-28118	267	6	.	.	PUNCT