id	sid	tid	token	lemma	pos
ajst-16340	1	1	academic	academic	ADJ
ajst-16340	1	2	journal	journal	NOUN
ajst-16340	1	3	of	of	ADP
ajst-16340	1	4	science	science	NOUN
ajst-16340	1	5	and	and	CCONJ
ajst-16340	1	6	technology	technology	NOUN
ajst-16340	1	7	issn	issn	NOUN
ajst-16340	1	8	:	:	PUNCT
ajst-16340	1	9	2771	2771	NUM
ajst-16340	1	10	-	-	SYM
ajst-16340	1	11	3032	3032	NUM
ajst-16340	1	12	|	|	NOUN
ajst-16340	1	13	vol	vol	NOUN
ajst-16340	1	14	.	.	PROPN
ajst-16340	2	1	9	9	NUM
ajst-16340	2	2	,	,	PUNCT
ajst-16340	2	3	no	no	INTJ
ajst-16340	2	4	.	.	NOUN
ajst-16340	2	5	1	1	NUM
ajst-16340	2	6	,	,	PUNCT
ajst-16340	2	7	2024	2024	NUM
ajst-16340	2	8	150	150	NUM
ajst-16340	2	9	comprehensive	comprehensive	ADJ
ajst-16340	2	10	review	review	NOUN
ajst-16340	2	11	of	of	ADP
ajst-16340	2	12	backpropagation	backpropagation	NOUN
ajst-16340	2	13	neural	neural	PROPN
ajst-16340	2	14	networks	network	NOUN
ajst-16340	2	15	mingfeng	mingfeng	PROPN
ajst-16340	2	16	li	li	PROPN
ajst-16340	2	17	college	college	PROPN
ajst-16340	2	18	of	of	ADP
ajst-16340	2	19	mechanical	mechanical	ADJ
ajst-16340	2	20	engineering	engineering	NOUN
ajst-16340	2	21	,	,	PUNCT
ajst-16340	2	22	xi'an	xi'an	PROPN
ajst-16340	2	23	shiyou	shiyou	PROPN
ajst-16340	2	24	university	university	PROPN
ajst-16340	2	25	,	,	PUNCT
ajst-16340	2	26	xi’an	xi’an	PROPN
ajst-16340	2	27	,	,	PUNCT
ajst-16340	2	28	shaanxi	shaanxi	PROPN
ajst-16340	2	29	710065	710065	NUM
ajst-16340	2	30	,	,	PUNCT
ajst-16340	2	31	china	china	PROPN
ajst-16340	2	32	abstract	abstract	NOUN
ajst-16340	2	33	:	:	PUNCT
ajst-16340	2	34	the	the	DET
ajst-16340	2	35	backpropagation	backpropagation	NOUN
ajst-16340	2	36	neural	neural	ADJ
ajst-16340	2	37	network	network	NOUN
ajst-16340	2	38	(	(	PUNCT
ajst-16340	2	39	bpnn	bpnn	NOUN
ajst-16340	2	40	)	)	PUNCT
ajst-16340	2	41	is	be	AUX
ajst-16340	2	42	a	a	DET
ajst-16340	2	43	deep	deep	ADJ
ajst-16340	2	44	learning	learning	NOUN
ajst-16340	2	45	model	model	NOUN
ajst-16340	2	46	inspired	inspire	VERB
ajst-16340	2	47	by	by	ADP
ajst-16340	2	48	the	the	DET
ajst-16340	2	49	biological	biological	ADJ
ajst-16340	2	50	neural	neural	ADJ
ajst-16340	2	51	network	network	NOUN
ajst-16340	2	52	.	.	PUNCT
ajst-16340	3	1	introduced	introduce	VERB
ajst-16340	3	2	in	in	ADP
ajst-16340	3	3	the	the	DET
ajst-16340	3	4	1980s	1980	NOUN
ajst-16340	3	5	,	,	PUNCT
ajst-16340	3	6	the	the	DET
ajst-16340	3	7	bpnn	bpnn	NOUN
ajst-16340	3	8	quickly	quickly	ADV
ajst-16340	3	9	became	become	VERB
ajst-16340	3	10	a	a	DET
ajst-16340	3	11	focal	focal	ADJ
ajst-16340	3	12	point	point	NOUN
ajst-16340	3	13	in	in	ADP
ajst-16340	3	14	neural	neural	ADJ
ajst-16340	3	15	network	network	NOUN
ajst-16340	3	16	research	research	NOUN
ajst-16340	3	17	due	due	ADP
ajst-16340	3	18	to	to	ADP
ajst-16340	3	19	its	its	PRON
ajst-16340	3	20	outstanding	outstanding	ADJ
ajst-16340	3	21	learning	learning	NOUN
ajst-16340	3	22	capability	capability	NOUN
ajst-16340	3	23	and	and	CCONJ
ajst-16340	3	24	adaptability	adaptability	NOUN
ajst-16340	3	25	.	.	PUNCT
ajst-16340	4	1	the	the	DET
ajst-16340	4	2	network	network	NOUN
ajst-16340	4	3	structure	structure	NOUN
ajst-16340	4	4	consists	consist	VERB
ajst-16340	4	5	of	of	ADP
ajst-16340	4	6	input	input	NOUN
ajst-16340	4	7	,	,	PUNCT
ajst-16340	4	8	hidden	hide	VERB
ajst-16340	4	9	,	,	PUNCT
ajst-16340	4	10	and	and	CCONJ
ajst-16340	4	11	output	output	NOUN
ajst-16340	4	12	layers	layer	NOUN
ajst-16340	4	13	,	,	PUNCT
ajst-16340	4	14	and	and	CCONJ
ajst-16340	4	15	it	it	PRON
ajst-16340	4	16	optimizes	optimize	VERB
ajst-16340	4	17	weights	weight	NOUN
ajst-16340	4	18	through	through	ADP
ajst-16340	4	19	the	the	DET
ajst-16340	4	20	backpropagation	backpropagation	NOUN
ajst-16340	4	21	algorithm	algorithm	NOUN
ajst-16340	4	22	,	,	PUNCT
ajst-16340	4	23	widely	widely	ADV
ajst-16340	4	24	applied	apply	VERB
ajst-16340	4	25	in	in	ADP
ajst-16340	4	26	image	image	NOUN
ajst-16340	4	27	recognition	recognition	NOUN
ajst-16340	4	28	,	,	PUNCT
ajst-16340	4	29	speech	speech	NOUN
ajst-16340	4	30	processing	processing	NOUN
ajst-16340	4	31	,	,	PUNCT
ajst-16340	4	32	natural	natural	ADJ
ajst-16340	4	33	language	language	NOUN
ajst-16340	4	34	processing	processing	NOUN
ajst-16340	4	35	,	,	PUNCT
ajst-16340	4	36	and	and	CCONJ
ajst-16340	4	37	more	more	ADJ
ajst-16340	4	38	.	.	PUNCT
ajst-16340	5	1	the	the	DET
ajst-16340	5	2	mathematical	mathematical	ADJ
ajst-16340	5	3	model	model	NOUN
ajst-16340	5	4	of	of	ADP
ajst-16340	5	5	neurons	neuron	NOUN
ajst-16340	5	6	describes	describe	VERB
ajst-16340	5	7	the	the	DET
ajst-16340	5	8	relationship	relationship	NOUN
ajst-16340	5	9	between	between	ADP
ajst-16340	5	10	input	input	NOUN
ajst-16340	5	11	and	and	CCONJ
ajst-16340	5	12	output	output	NOUN
ajst-16340	5	13	,	,	PUNCT
ajst-16340	5	14	and	and	CCONJ
ajst-16340	5	15	the	the	DET
ajst-16340	5	16	training	training	NOUN
ajst-16340	5	17	process	process	NOUN
ajst-16340	5	18	involves	involve	VERB
ajst-16340	5	19	adjusting	adjust	VERB
ajst-16340	5	20	weights	weight	NOUN
ajst-16340	5	21	and	and	CCONJ
ajst-16340	5	22	biases	bias	NOUN
ajst-16340	5	23	using	use	VERB
ajst-16340	5	24	optimization	optimization	NOUN
ajst-16340	5	25	algorithms	algorithm	NOUN
ajst-16340	5	26	like	like	ADP
ajst-16340	5	27	gradient	gradient	ADJ
ajst-16340	5	28	descent	descent	NOUN
ajst-16340	5	29	.	.	PUNCT
ajst-16340	6	1	in	in	ADP
ajst-16340	6	2	applications	application	NOUN
ajst-16340	6	3	,	,	PUNCT
ajst-16340	6	4	bpnn	bpnn	NOUN
ajst-16340	6	5	excels	excel	NOUN
ajst-16340	6	6	in	in	ADP
ajst-16340	6	7	image	image	NOUN
ajst-16340	6	8	recognition	recognition	NOUN
ajst-16340	6	9	,	,	PUNCT
ajst-16340	6	10	speech	speech	NOUN
ajst-16340	6	11	processing	processing	NOUN
ajst-16340	6	12	,	,	PUNCT
ajst-16340	6	13	natural	natural	ADJ
ajst-16340	6	14	language	language	NOUN
ajst-16340	6	15	processing	processing	NOUN
ajst-16340	6	16	,	,	PUNCT
ajst-16340	6	17	and	and	CCONJ
ajst-16340	6	18	financial	financial	ADJ
ajst-16340	6	19	forecasting	forecasting	NOUN
ajst-16340	6	20	.	.	PUNCT
ajst-16340	7	1	researchers	researcher	NOUN
ajst-16340	7	2	continuously	continuously	ADV
ajst-16340	7	3	experiment	experiment	VERB
ajst-16340	7	4	with	with	ADP
ajst-16340	7	5	optimization	optimization	NOUN
ajst-16340	7	6	algorithms	algorithm	NOUN
ajst-16340	7	7	,	,	PUNCT
ajst-16340	7	8	including	include	VERB
ajst-16340	7	9	the	the	DET
ajst-16340	7	10	grey	grey	ADJ
ajst-16340	7	11	wolf	wolf	PROPN
ajst-16340	7	12	algorithm	algorithm	PROPN
ajst-16340	7	13	,	,	PUNCT
ajst-16340	7	14	genetic	genetic	ADJ
ajst-16340	7	15	algorithm	algorithm	NOUN
ajst-16340	7	16	,	,	PUNCT
ajst-16340	7	17	particle	particle	NOUN
ajst-16340	7	18	swarm	swarm	NOUN
ajst-16340	7	19	algorithm	algorithm	NOUN
ajst-16340	7	20	,	,	PUNCT
ajst-16340	7	21	simulated	simulate	VERB
ajst-16340	7	22	annealing	annealing	NOUN
ajst-16340	7	23	algorithm	algorithm	NOUN
ajst-16340	7	24	,	,	PUNCT
ajst-16340	7	25	as	as	ADV
ajst-16340	7	26	well	well	ADV
ajst-16340	7	27	as	as	ADP
ajst-16340	7	28	comprehensive	comprehensive	ADJ
ajst-16340	7	29	strategies	strategy	NOUN
ajst-16340	7	30	and	and	CCONJ
ajst-16340	7	31	improved	improve	VERB
ajst-16340	7	32	gradient	gradient	ADJ
ajst-16340	7	33	descent	descent	NOUN
ajst-16340	7	34	algorithms	algorithm	NOUN
ajst-16340	7	35	.	.	PUNCT
ajst-16340	8	1	in	in	ADP
ajst-16340	8	2	the	the	DET
ajst-16340	8	3	future	future	NOUN
ajst-16340	8	4	,	,	PUNCT
ajst-16340	8	5	with	with	ADP
ajst-16340	8	6	the	the	DET
ajst-16340	8	7	ongoing	ongoing	ADJ
ajst-16340	8	8	development	development	NOUN
ajst-16340	8	9	of	of	ADP
ajst-16340	8	10	deep	deep	ADJ
ajst-16340	8	11	learning	learning	NOUN
ajst-16340	8	12	,	,	PUNCT
ajst-16340	8	13	bpnn	bpnn	PROPN
ajst-16340	8	14	is	be	AUX
ajst-16340	8	15	poised	poise	VERB
ajst-16340	8	16	to	to	PART
ajst-16340	8	17	play	play	VERB
ajst-16340	8	18	a	a	DET
ajst-16340	8	19	crucial	crucial	ADJ
ajst-16340	8	20	role	role	NOUN
ajst-16340	8	21	in	in	ADP
ajst-16340	8	22	tasks	task	NOUN
ajst-16340	8	23	such	such	ADJ
ajst-16340	8	24	as	as	ADP
ajst-16340	8	25	image	image	NOUN
ajst-16340	8	26	recognition	recognition	NOUN
ajst-16340	8	27	and	and	CCONJ
ajst-16340	8	28	speech	speech	NOUN
ajst-16340	8	29	processing	processing	NOUN
ajst-16340	8	30	.	.	PUNCT
ajst-16340	9	1	keywords	keyword	NOUN
ajst-16340	9	2	:	:	PUNCT
ajst-16340	9	3	backpropagation	backpropagation	NOUN
ajst-16340	9	4	neural	neural	ADJ
ajst-16340	9	5	network	network	NOUN
ajst-16340	9	6	(	(	PUNCT
ajst-16340	9	7	bpnn	bpnn	NOUN
ajst-16340	9	8	)	)	PUNCT
ajst-16340	9	9	;	;	PUNCT
ajst-16340	9	10	deep	deep	ADJ
ajst-16340	9	11	learning	learning	NOUN
ajst-16340	9	12	;	;	PUNCT
ajst-16340	9	13	network	network	NOUN
ajst-16340	9	14	structure	structure	NOUN
ajst-16340	9	15	;	;	PUNCT
ajst-16340	9	16	optimization	optimization	NOUN
ajst-16340	9	17	algorithms	algorithm	NOUN
ajst-16340	9	18	.	.	PUNCT
ajst-16340	10	1	1	1	X
ajst-16340	10	2	.	.	X
ajst-16340	10	3	introduction	introduction	NOUN
ajst-16340	10	4	as	as	ADP
ajst-16340	10	5	technology	technology	NOUN
ajst-16340	10	6	continues	continue	VERB
ajst-16340	10	7	to	to	PART
ajst-16340	10	8	evolve	evolve	VERB
ajst-16340	10	9	,	,	PUNCT
ajst-16340	10	10	the	the	DET
ajst-16340	10	11	backpropagation	backpropagation	NOUN
ajst-16340	10	12	neural	neural	ADJ
ajst-16340	10	13	network	network	NOUN
ajst-16340	10	14	(	(	PUNCT
ajst-16340	10	15	bpnn	bpnn	NOUN
ajst-16340	10	16	)	)	PUNCT
ajst-16340	10	17	is	be	AUX
ajst-16340	10	18	emerging	emerge	VERB
ajst-16340	10	19	as	as	ADP
ajst-16340	10	20	a	a	DET
ajst-16340	10	21	key	key	ADJ
ajst-16340	10	22	driver	driver	NOUN
ajst-16340	10	23	in	in	ADP
ajst-16340	10	24	the	the	DET
ajst-16340	10	25	field	field	NOUN
ajst-16340	10	26	of	of	ADP
ajst-16340	10	27	deep	deep	ADJ
ajst-16340	10	28	learning	learning	NOUN
ajst-16340	10	29	,	,	PUNCT
ajst-16340	10	30	playing	play	VERB
ajst-16340	10	31	a	a	DET
ajst-16340	10	32	pivotal	pivotal	ADJ
ajst-16340	10	33	role	role	NOUN
ajst-16340	10	34	in	in	ADP
ajst-16340	10	35	the	the	DET
ajst-16340	10	36	development	development	NOUN
ajst-16340	10	37	of	of	ADP
ajst-16340	10	38	artificial	artificial	ADJ
ajst-16340	10	39	intelligence	intelligence	NOUN
ajst-16340	10	40	.	.	PUNCT
ajst-16340	11	1	since	since	SCONJ
ajst-16340	11	2	its	its	PRON
ajst-16340	11	3	introduction	introduction	NOUN
ajst-16340	11	4	in	in	ADP
ajst-16340	11	5	the	the	DET
ajst-16340	11	6	early	early	ADJ
ajst-16340	11	7	1980s	1980s	NUM
ajst-16340	11	8	,	,	PUNCT
ajst-16340	11	9	bpnn	bpnn	NOUN
ajst-16340	11	10	has	have	AUX
ajst-16340	11	11	gained	gain	VERB
ajst-16340	11	12	prominence	prominence	NOUN
ajst-16340	11	13	in	in	ADP
ajst-16340	11	14	both	both	DET
ajst-16340	11	15	research	research	NOUN
ajst-16340	11	16	and	and	CCONJ
ajst-16340	11	17	practical	practical	ADJ
ajst-16340	11	18	applications	application	NOUN
ajst-16340	11	19	,	,	PUNCT
ajst-16340	11	20	evolving	evolve	VERB
ajst-16340	11	21	from	from	ADP
ajst-16340	11	22	a	a	DET
ajst-16340	11	23	singlelayer	singlelayer	NOUN
ajst-16340	11	24	network	network	NOUN
ajst-16340	11	25	to	to	ADP
ajst-16340	11	26	a	a	DET
ajst-16340	11	27	deep	deep	ADJ
ajst-16340	11	28	neural	neural	ADJ
ajst-16340	11	29	network	network	NOUN
ajst-16340	11	30	with	with	ADP
ajst-16340	11	31	the	the	DET
ajst-16340	11	32	rise	rise	NOUN
ajst-16340	11	33	of	of	ADP
ajst-16340	11	34	computational	computational	ADJ
ajst-16340	11	35	capabilities	capability	NOUN
ajst-16340	11	36	and	and	CCONJ
ajst-16340	11	37	the	the	DET
ajst-16340	11	38	advent	advent	NOUN
ajst-16340	11	39	of	of	ADP
ajst-16340	11	40	deep	deep	ADJ
ajst-16340	11	41	learning	learning	NOUN
ajst-16340	11	42	.	.	PUNCT
ajst-16340	12	1	bpnn	bpnn	PROPN
ajst-16340	12	2	has	have	AUX
ajst-16340	12	3	demonstrated	demonstrate	VERB
ajst-16340	12	4	outstanding	outstanding	ADJ
ajst-16340	12	5	performance	performance	NOUN
ajst-16340	12	6	in	in	ADP
ajst-16340	12	7	various	various	ADJ
ajst-16340	12	8	domains	domain	NOUN
ajst-16340	12	9	such	such	ADJ
ajst-16340	12	10	as	as	ADP
ajst-16340	12	11	image	image	NOUN
ajst-16340	12	12	processing	processing	NOUN
ajst-16340	12	13	,	,	PUNCT
ajst-16340	12	14	speech	speech	NOUN
ajst-16340	12	15	recognition	recognition	NOUN
ajst-16340	12	16	,	,	PUNCT
ajst-16340	12	17	and	and	CCONJ
ajst-16340	12	18	natural	natural	ADJ
ajst-16340	12	19	language	language	NOUN
ajst-16340	12	20	processing	processing	NOUN
ajst-16340	12	21	.	.	PUNCT
ajst-16340	13	1	its	its	PRON
ajst-16340	13	2	flexibility	flexibility	NOUN
ajst-16340	13	3	and	and	CCONJ
ajst-16340	13	4	powerful	powerful	ADJ
ajst-16340	13	5	fitting	fitting	ADJ
ajst-16340	13	6	capabilities	capability	NOUN
ajst-16340	13	7	position	position	VERB
ajst-16340	13	8	it	it	PRON
ajst-16340	13	9	as	as	ADP
ajst-16340	13	10	a	a	DET
ajst-16340	13	11	robust	robust	ADJ
ajst-16340	13	12	tool	tool	NOUN
ajst-16340	13	13	for	for	ADP
ajst-16340	13	14	addressing	address	VERB
ajst-16340	13	15	complex	complex	ADJ
ajst-16340	13	16	problems	problem	NOUN
ajst-16340	13	17	.	.	PUNCT
ajst-16340	14	1	neurons	neuron	NOUN
ajst-16340	14	2	,	,	PUNCT
ajst-16340	14	3	serving	serve	VERB
ajst-16340	14	4	as	as	ADP
ajst-16340	14	5	the	the	DET
ajst-16340	14	6	fundamental	fundamental	ADJ
ajst-16340	14	7	units	unit	NOUN
ajst-16340	14	8	of	of	ADP
ajst-16340	14	9	the	the	DET
ajst-16340	14	10	network	network	NOUN
ajst-16340	14	11	,	,	PUNCT
ajst-16340	14	12	weight	weight	VERB
ajst-16340	14	13	the	the	DET
ajst-16340	14	14	outputs	output	NOUN
ajst-16340	14	15	of	of	ADP
ajst-16340	14	16	the	the	DET
ajst-16340	14	17	previous	previous	ADJ
ajst-16340	14	18	layer	layer	NOUN
ajst-16340	14	19	and	and	CCONJ
ajst-16340	14	20	produce	produce	VERB
ajst-16340	14	21	an	an	DET
ajst-16340	14	22	output	output	NOUN
ajst-16340	14	23	through	through	ADP
ajst-16340	14	24	activation	activation	NOUN
ajst-16340	14	25	functions	function	NOUN
ajst-16340	14	26	.	.	PUNCT
ajst-16340	15	1	the	the	DET
ajst-16340	15	2	network	network	NOUN
ajst-16340	15	3	structure	structure	NOUN
ajst-16340	15	4	comprises	comprise	VERB
ajst-16340	15	5	input	input	NOUN
ajst-16340	15	6	,	,	PUNCT
ajst-16340	15	7	hidden	hide	VERB
ajst-16340	15	8	,	,	PUNCT
ajst-16340	15	9	and	and	CCONJ
ajst-16340	15	10	output	output	NOUN
ajst-16340	15	11	layers	layer	NOUN
ajst-16340	15	12	,	,	PUNCT
ajst-16340	15	13	with	with	ADP
ajst-16340	15	14	collaborative	collaborative	ADJ
ajst-16340	15	15	efforts	effort	NOUN
ajst-16340	15	16	and	and	CCONJ
ajst-16340	15	17	the	the	DET
ajst-16340	15	18	backpropagation	backpropagation	NOUN
ajst-16340	15	19	algorithm	algorithm	NOUN
ajst-16340	15	20	adjusting	adjust	VERB
ajst-16340	15	21	weights	weight	NOUN
ajst-16340	15	22	to	to	PART
ajst-16340	15	23	optimize	optimize	VERB
ajst-16340	15	24	the	the	DET
ajst-16340	15	25	network	network	NOUN
ajst-16340	15	26	for	for	ADP
ajst-16340	15	27	specific	specific	ADJ
ajst-16340	15	28	tasks	task	NOUN
ajst-16340	15	29	.	.	PUNCT
ajst-16340	16	1	this	this	DET
ajst-16340	16	2	article	article	NOUN
ajst-16340	16	3	delves	delve	VERB
ajst-16340	16	4	into	into	ADP
ajst-16340	16	5	various	various	ADJ
ajst-16340	16	6	aspects	aspect	NOUN
ajst-16340	16	7	of	of	ADP
ajst-16340	16	8	bpnn	bpnn	NOUN
ajst-16340	16	9	,	,	PUNCT
ajst-16340	16	10	including	include	VERB
ajst-16340	16	11	its	its	PRON
ajst-16340	16	12	mathematical	mathematical	ADJ
ajst-16340	16	13	model	model	NOUN
ajst-16340	16	14	,	,	PUNCT
ajst-16340	16	15	network	network	NOUN
ajst-16340	16	16	structure	structure	NOUN
ajst-16340	16	17	,	,	PUNCT
ajst-16340	16	18	feedforward	feedforward	NOUN
ajst-16340	16	19	and	and	CCONJ
ajst-16340	16	20	backpropagation	backpropagation	NOUN
ajst-16340	16	21	algorithms	algorithm	NOUN
ajst-16340	16	22	,	,	PUNCT
ajst-16340	16	23	weight	weight	NOUN
ajst-16340	16	24	and	and	CCONJ
ajst-16340	16	25	bias	bias	NOUN
ajst-16340	16	26	updates	update	NOUN
ajst-16340	16	27	,	,	PUNCT
ajst-16340	16	28	as	as	ADV
ajst-16340	16	29	well	well	ADV
ajst-16340	16	30	as	as	ADP
ajst-16340	16	31	training	training	NOUN
ajst-16340	16	32	and	and	CCONJ
ajst-16340	16	33	optimization	optimization	NOUN
ajst-16340	16	34	.	.	PUNCT
ajst-16340	17	1	the	the	DET
ajst-16340	17	2	goal	goal	NOUN
ajst-16340	17	3	is	be	AUX
ajst-16340	17	4	to	to	PART
ajst-16340	17	5	provide	provide	VERB
ajst-16340	17	6	readers	reader	NOUN
ajst-16340	17	7	with	with	ADP
ajst-16340	17	8	a	a	DET
ajst-16340	17	9	clear	clear	ADJ
ajst-16340	17	10	and	and	CCONJ
ajst-16340	17	11	in	in	ADP
ajst-16340	17	12	-	-	PUNCT
ajst-16340	17	13	depth	depth	NOUN
ajst-16340	17	14	understanding	understanding	NOUN
ajst-16340	17	15	of	of	ADP
ajst-16340	17	16	bpnn	bpnn	NOUN
ajst-16340	17	17	's	's	PART
ajst-16340	17	18	core	core	NOUN
ajst-16340	17	19	elements	element	NOUN
ajst-16340	17	20	and	and	CCONJ
ajst-16340	17	21	offer	offer	VERB
ajst-16340	17	22	insights	insight	NOUN
ajst-16340	17	23	into	into	ADP
ajst-16340	17	24	its	its	PRON
ajst-16340	17	25	future	future	ADJ
ajst-16340	17	26	development	development	NOUN
ajst-16340	17	27	.	.	PUNCT
ajst-16340	18	1	in	in	ADP
ajst-16340	18	2	this	this	DET
ajst-16340	18	3	era	era	NOUN
ajst-16340	18	4	of	of	ADP
ajst-16340	18	5	information	information	NOUN
ajst-16340	18	6	explosion	explosion	NOUN
ajst-16340	18	7	,	,	PUNCT
ajst-16340	18	8	bpnn	bpnn	PROPN
ajst-16340	18	9	,	,	PUNCT
ajst-16340	18	10	with	with	ADP
ajst-16340	18	11	its	its	PRON
ajst-16340	18	12	learning	learn	VERB
ajst-16340	18	13	capabilities	capability	NOUN
ajst-16340	18	14	and	and	CCONJ
ajst-16340	18	15	extensive	extensive	ADJ
ajst-16340	18	16	application	application	NOUN
ajst-16340	18	17	areas	area	NOUN
ajst-16340	18	18	,	,	PUNCT
ajst-16340	18	19	is	be	AUX
ajst-16340	18	20	at	at	ADP
ajst-16340	18	21	the	the	DET
ajst-16340	18	22	forefront	forefront	NOUN
ajst-16340	18	23	of	of	ADP
ajst-16340	18	24	driving	drive	VERB
ajst-16340	18	25	innovations	innovation	NOUN
ajst-16340	18	26	in	in	ADP
ajst-16340	18	27	artificial	artificial	ADJ
ajst-16340	18	28	intelligence	intelligence	NOUN
ajst-16340	18	29	.	.	PUNCT
ajst-16340	19	1	2	2	X
ajst-16340	19	2	.	.	NUM
ajst-16340	19	3	basic	basic	ADJ
ajst-16340	19	4	concepts	concept	NOUN
ajst-16340	19	5	of	of	ADP
ajst-16340	19	6	backpropagation	backpropagation	NOUN
ajst-16340	19	7	(	(	PUNCT
ajst-16340	19	8	bp	bp	PROPN
ajst-16340	19	9	)	)	PUNCT
ajst-16340	19	10	neural	neural	ADJ
ajst-16340	19	11	networks	network	NOUN
ajst-16340	19	12	2.1	2.1	NUM
ajst-16340	19	13	.	.	PUNCT
ajst-16340	20	1	neurons	neuron	NOUN
ajst-16340	20	2	and	and	CCONJ
ajst-16340	20	3	network	network	NOUN
ajst-16340	20	4	structure	structure	NOUN
ajst-16340	20	5	the	the	DET
ajst-16340	20	6	bp	bp	PROPN
ajst-16340	20	7	neural	neural	PROPN
ajst-16340	20	8	network	network	NOUN
ajst-16340	20	9	,	,	PUNCT
ajst-16340	20	10	initially	initially	ADV
ajst-16340	20	11	proposed	propose	VERB
ajst-16340	20	12	in	in	ADP
ajst-16340	20	13	the	the	DET
ajst-16340	20	14	1980s[1	1980s[1	NOUN
ajst-16340	20	15	]	]	PUNCT
ajst-16340	20	16	,	,	PUNCT
ajst-16340	20	17	draws	draw	VERB
ajst-16340	20	18	inspiration	inspiration	NOUN
ajst-16340	20	19	from	from	ADP
ajst-16340	20	20	biological	biological	ADJ
ajst-16340	20	21	neural	neural	ADJ
ajst-16340	20	22	networks	network	NOUN
ajst-16340	20	23	.	.	PUNCT
ajst-16340	21	1	its	its	PRON
ajst-16340	21	2	superior	superior	ADJ
ajst-16340	21	3	learning	learning	NOUN
ajst-16340	21	4	ability	ability	NOUN
ajst-16340	21	5	and	and	CCONJ
ajst-16340	21	6	adaptability	adaptability	NOUN
ajst-16340	21	7	quickly	quickly	ADV
ajst-16340	21	8	made	make	VERB
ajst-16340	21	9	it	it	PRON
ajst-16340	21	10	a	a	DET
ajst-16340	21	11	focal	focal	ADJ
ajst-16340	21	12	point	point	NOUN
ajst-16340	21	13	in	in	ADP
ajst-16340	21	14	the	the	DET
ajst-16340	21	15	field	field	NOUN
ajst-16340	21	16	of	of	ADP
ajst-16340	21	17	neural	neural	ADJ
ajst-16340	21	18	network	network	NOUN
ajst-16340	21	19	research	research	NOUN
ajst-16340	21	20	.	.	PUNCT
ajst-16340	22	1	with	with	ADP
ajst-16340	22	2	the	the	DET
ajst-16340	22	3	improvement	improvement	NOUN
ajst-16340	22	4	of	of	ADP
ajst-16340	22	5	computing	compute	VERB
ajst-16340	22	6	power	power	NOUN
ajst-16340	22	7	and	and	CCONJ
ajst-16340	22	8	the	the	DET
ajst-16340	22	9	rise	rise	NOUN
ajst-16340	22	10	of	of	ADP
ajst-16340	22	11	the	the	DET
ajst-16340	22	12	deep	deep	ADJ
ajst-16340	22	13	learning	learning	NOUN
ajst-16340	22	14	trend	trend	NOUN
ajst-16340	22	15	,	,	PUNCT
ajst-16340	22	16	the	the	DET
ajst-16340	22	17	bp	bp	PROPN
ajst-16340	22	18	neural	neural	PROPN
ajst-16340	22	19	network	network	NOUN
ajst-16340	22	20	has	have	AUX
ajst-16340	22	21	evolved	evolve	VERB
ajst-16340	22	22	continuously	continuously	ADV
ajst-16340	22	23	.	.	PUNCT
ajst-16340	23	1	from	from	ADP
ajst-16340	23	2	the	the	DET
ajst-16340	23	3	initial	initial	ADJ
ajst-16340	23	4	single	single	ADJ
ajst-16340	23	5	-	-	PUNCT
ajst-16340	23	6	layer	layer	NOUN
ajst-16340	23	7	network	network	NOUN
ajst-16340	23	8	to	to	ADP
ajst-16340	23	9	the	the	DET
ajst-16340	23	10	emergence	emergence	NOUN
ajst-16340	23	11	of	of	ADP
ajst-16340	23	12	deep	deep	ADJ
ajst-16340	23	13	neural	neural	ADJ
ajst-16340	23	14	networks	network	NOUN
ajst-16340	23	15	,	,	PUNCT
ajst-16340	23	16	bp	bp	PROPN
ajst-16340	23	17	networks	network	NOUN
ajst-16340	23	18	have	have	AUX
ajst-16340	23	19	made	make	VERB
ajst-16340	23	20	significant	significant	ADJ
ajst-16340	23	21	progress	progress	NOUN
ajst-16340	23	22	in	in	ADP
ajst-16340	23	23	various	various	ADJ
ajst-16340	23	24	domains	domain	NOUN
ajst-16340	23	25	.	.	PUNCT
ajst-16340	24	1	its	its	PRON
ajst-16340	24	2	powerful	powerful	ADJ
ajst-16340	24	3	nonlinear	nonlinear	ADJ
ajst-16340	24	4	modeling	modeling	NOUN
ajst-16340	24	5	capability	capability	NOUN
ajst-16340	24	6	positions	position	NOUN
ajst-16340	24	7	the	the	DET
ajst-16340	24	8	bp	bp	PROPN
ajst-16340	24	9	neural	neural	PROPN
ajst-16340	24	10	network	network	NOUN
ajst-16340	24	11	as	as	ADP
ajst-16340	24	12	a	a	DET
ajst-16340	24	13	crucial	crucial	ADJ
ajst-16340	24	14	element	element	NOUN
ajst-16340	24	15	in	in	ADP
ajst-16340	24	16	machine	machine	NOUN
ajst-16340	24	17	learning	learning	NOUN
ajst-16340	24	18	.	.	PUNCT
ajst-16340	25	1	in	in	ADP
ajst-16340	25	2	numerous	numerous	ADJ
ajst-16340	25	3	fields	field	NOUN
ajst-16340	25	4	such	such	ADJ
ajst-16340	25	5	as	as	ADP
ajst-16340	25	6	image	image	NOUN
ajst-16340	25	7	processing	processing	NOUN
ajst-16340	25	8	,	,	PUNCT
ajst-16340	25	9	speech	speech	NOUN
ajst-16340	25	10	recognition	recognition	NOUN
ajst-16340	25	11	,	,	PUNCT
ajst-16340	25	12	and	and	CCONJ
ajst-16340	25	13	natural	natural	ADJ
ajst-16340	25	14	language	language	NOUN
ajst-16340	25	15	processing	processing	NOUN
ajst-16340	25	16	,	,	PUNCT
ajst-16340	25	17	bp	bp	PROPN
ajst-16340	25	18	neural	neural	PROPN
ajst-16340	25	19	networks	network	NOUN
ajst-16340	25	20	demonstrate	demonstrate	VERB
ajst-16340	25	21	outstanding	outstanding	ADJ
ajst-16340	25	22	performance	performance	NOUN
ajst-16340	25	23	.	.	PUNCT
ajst-16340	26	1	the	the	DET
ajst-16340	26	2	neurons	neuron	NOUN
ajst-16340	26	3	in	in	ADP
ajst-16340	26	4	the	the	DET
ajst-16340	26	5	network	network	NOUN
ajst-16340	26	6	are	be	AUX
ajst-16340	26	7	the	the	DET
ajst-16340	26	8	fundamental	fundamental	ADJ
ajst-16340	26	9	units	unit	NOUN
ajst-16340	26	10	that	that	PRON
ajst-16340	26	11	constitute	constitute	VERB
ajst-16340	26	12	the	the	DET
ajst-16340	26	13	entire	entire	ADJ
ajst-16340	26	14	system	system	NOUN
ajst-16340	26	15	.	.	PUNCT
ajst-16340	27	1	each	each	DET
ajst-16340	27	2	neuron	neuron	NOUN
ajst-16340	27	3	receives	receive	VERB
ajst-16340	27	4	the	the	DET
ajst-16340	27	5	output	output	NOUN
ajst-16340	27	6	from	from	ADP
ajst-16340	27	7	the	the	DET
ajst-16340	27	8	neurons	neuron	NOUN
ajst-16340	27	9	in	in	ADP
ajst-16340	27	10	the	the	DET
ajst-16340	27	11	previous	previous	ADJ
ajst-16340	27	12	layer	layer	NOUN
ajst-16340	27	13	,	,	PUNCT
ajst-16340	27	14	performs	perform	VERB
ajst-16340	27	15	a	a	DET
ajst-16340	27	16	weighted	weight	VERB
ajst-16340	27	17	sum	sum	NOUN
ajst-16340	27	18	through	through	ADP
ajst-16340	27	19	the	the	DET
ajst-16340	27	20	assigned	assign	VERB
ajst-16340	27	21	weights	weight	NOUN
ajst-16340	27	22	,	,	PUNCT
ajst-16340	27	23	and	and	CCONJ
ajst-16340	27	24	generates	generate	VERB
ajst-16340	27	25	an	an	DET
ajst-16340	27	26	output	output	NOUN
ajst-16340	27	27	through	through	ADP
ajst-16340	27	28	an	an	DET
ajst-16340	27	29	activation	activation	NOUN
ajst-16340	27	30	function	function	NOUN
ajst-16340	27	31	.	.	PUNCT
ajst-16340	28	1	the	the	DET
ajst-16340	28	2	entire	entire	ADJ
ajst-16340	28	3	bp	bp	PROPN
ajst-16340	28	4	neural	neural	ADJ
ajst-16340	28	5	network	network	NOUN
ajst-16340	28	6	consists	consist	VERB
ajst-16340	28	7	of	of	ADP
ajst-16340	28	8	multiple	multiple	ADJ
ajst-16340	28	9	layers	layer	NOUN
ajst-16340	28	10	of	of	ADP
ajst-16340	28	11	neurons	neuron	NOUN
ajst-16340	28	12	,	,	PUNCT
ajst-16340	28	13	including	include	VERB
ajst-16340	28	14	the	the	DET
ajst-16340	28	15	input	input	NOUN
ajst-16340	28	16	layer	layer	NOUN
ajst-16340	28	17	,	,	PUNCT
ajst-16340	28	18	hidden	hidden	ADJ
ajst-16340	28	19	layers	layer	NOUN
ajst-16340	28	20	,	,	PUNCT
ajst-16340	28	21	and	and	CCONJ
ajst-16340	28	22	output	output	NOUN
ajst-16340	28	23	layer	layer	NOUN
ajst-16340	28	24	.	.	PUNCT
ajst-16340	29	1	these	these	DET
ajst-16340	29	2	layers	layer	NOUN
ajst-16340	29	3	work	work	VERB
ajst-16340	29	4	collaboratively	collaboratively	ADV
ajst-16340	29	5	,	,	PUNCT
ajst-16340	29	6	adjusting	adjust	VERB
ajst-16340	29	7	weights	weight	NOUN
ajst-16340	29	8	continuously	continuously	ADV
ajst-16340	29	9	through	through	ADP
ajst-16340	29	10	the	the	DET
ajst-16340	29	11	backpropagation	backpropagation	NOUN
ajst-16340	29	12	algorithm	algorithm	NOUN
ajst-16340	29	13	to	to	PART
ajst-16340	29	14	optimize	optimize	VERB
ajst-16340	29	15	the	the	DET
ajst-16340	29	16	network	network	NOUN
ajst-16340	29	17	for	for	ADP
ajst-16340	29	18	specific	specific	ADJ
ajst-16340	29	19	tasks	task	NOUN
ajst-16340	29	20	.	.	PUNCT
ajst-16340	30	1	with	with	ADP
ajst-16340	30	2	its	its	PRON
ajst-16340	30	3	profound	profound	ADJ
ajst-16340	30	4	research	research	NOUN
ajst-16340	30	5	foundation	foundation	NOUN
ajst-16340	30	6	and	and	CCONJ
ajst-16340	30	7	successful	successful	ADJ
ajst-16340	30	8	applications	application	NOUN
ajst-16340	30	9	,	,	PUNCT
ajst-16340	30	10	the	the	DET
ajst-16340	30	11	bp	bp	PROPN
ajst-16340	30	12	neural	neural	PROPN
ajst-16340	30	13	network	network	NOUN
ajst-16340	30	14	has	have	AUX
ajst-16340	30	15	become	become	VERB
ajst-16340	30	16	a	a	DET
ajst-16340	30	17	crucial	crucial	ADJ
ajst-16340	30	18	pillar	pillar	NOUN
ajst-16340	30	19	in	in	ADP
ajst-16340	30	20	the	the	DET
ajst-16340	30	21	field	field	NOUN
ajst-16340	30	22	of	of	ADP
ajst-16340	30	23	deep	deep	ADJ
ajst-16340	30	24	learning	learning	NOUN
ajst-16340	30	25	.	.	PUNCT
ajst-16340	31	1	in	in	ADP
ajst-16340	31	2	the	the	DET
ajst-16340	31	3	future	future	NOUN
ajst-16340	31	4	,	,	PUNCT
ajst-16340	31	5	as	as	ADP
ajst-16340	31	6	technology	technology	NOUN
ajst-16340	31	7	advances	advance	NOUN
ajst-16340	31	8	and	and	CCONJ
ajst-16340	31	9	application	application	NOUN
ajst-16340	31	10	scenarios	scenario	NOUN
ajst-16340	31	11	expand	expand	VERB
ajst-16340	31	12	,	,	PUNCT
ajst-16340	31	13	the	the	DET
ajst-16340	31	14	bp	bp	PROPN
ajst-16340	31	15	neural	neural	PROPN
ajst-16340	31	16	network	network	NOUN
ajst-16340	31	17	will	will	AUX
ajst-16340	31	18	continue	continue	VERB
ajst-16340	31	19	to	to	PART
ajst-16340	31	20	play	play	VERB
ajst-16340	31	21	a	a	DET
ajst-16340	31	22	key	key	ADJ
ajst-16340	31	23	role	role	NOUN
ajst-16340	31	24	in	in	ADP
ajst-16340	31	25	the	the	DET
ajst-16340	31	26	field	field	NOUN
ajst-16340	31	27	of	of	ADP
ajst-16340	31	28	machine	machine	NOUN
ajst-16340	31	29	learning	learning	NOUN
ajst-16340	31	30	.	.	PUNCT
ajst-16340	32	1	2.1.1	2.1.1	X
ajst-16340	32	2	.	.	PUNCT
ajst-16340	32	3	mathematical	mathematical	ADJ
ajst-16340	32	4	model	model	NOUN
ajst-16340	32	5	of	of	ADP
ajst-16340	32	6	neurons	neuron	NOUN
ajst-16340	32	7	the	the	DET
ajst-16340	32	8	mathematical	mathematical	ADJ
ajst-16340	32	9	model	model	NOUN
ajst-16340	32	10	of	of	ADP
ajst-16340	32	11	neurons	neuron	NOUN
ajst-16340	32	12	describes	describe	VERB
ajst-16340	32	13	the	the	DET
ajst-16340	32	14	relationship	relationship	NOUN
ajst-16340	32	15	between	between	ADP
ajst-16340	32	16	their	their	PRON
ajst-16340	32	17	inputs	input	NOUN
ajst-16340	32	18	and	and	CCONJ
ajst-16340	32	19	outputs	output	NOUN
ajst-16340	32	20	:	:	PUNCT
ajst-16340	32	21	1	1	NUM
ajst-16340	32	22	(	(	PUNCT
ajst-16340	32	23	)	)	PUNCT
ajst-16340	33	1	i	i	PRON
ajst-16340	33	2	i	i	VERB
ajst-16340	34	1	i	i	VERB
ajst-16340	34	2	n	n	VERB
ajst-16340	35	1	y	y	NOUN
ajst-16340	35	2	f	f	PROPN
ajst-16340	35	3	w	w	PROPN
ajst-16340	35	4	x	x	PROPN
ajst-16340	35	5	b	b	X
ajst-16340	35	6			NUM
ajst-16340	35	7			NOUN
ajst-16340	35	8			NUM
ajst-16340	36	1			NUM
ajst-16340	36	2			X
ajst-16340	36	3	(	(	PUNCT
ajst-16340	36	4	1	1	NUM
ajst-16340	36	5	)	)	PUNCT
ajst-16340	36	6	as	as	SCONJ
ajst-16340	36	7	shown	show	VERB
ajst-16340	36	8	in	in	ADP
ajst-16340	36	9	equation	equation	NOUN
ajst-16340	36	10	(	(	PUNCT
ajst-16340	36	11	1	1	NUM
ajst-16340	36	12	):	):	PUNCT
ajst-16340	36	13	ix	ix	ADV
ajst-16340	36	14	,	,	PUNCT
ajst-16340	36	15	represents	represent	VERB
ajst-16340	36	16	the	the	DET
ajst-16340	36	17	input	input	NOUN
ajst-16340	36	18	,	,	PUNCT
ajst-16340	36	19	iw	iw	NOUN
ajst-16340	36	20	corresponds	correspond	VERB
ajst-16340	36	21	to	to	ADP
ajst-16340	36	22	the	the	DET
ajst-16340	36	23	weights	weight	NOUN
ajst-16340	36	24	,	,	PUNCT
ajst-16340	36	25	b	b	PROPN
ajst-16340	36	26	is	be	AUX
ajst-16340	36	27	the	the	DET
ajst-16340	36	28	bias	bias	NOUN
ajst-16340	36	29	,	,	PUNCT
ajst-16340	36	30	f	f	PROPN
ajst-16340	36	31	is	be	AUX
ajst-16340	36	32	the	the	DET
ajst-16340	36	33	activation	activation	NOUN
ajst-16340	36	34	function	function	NOUN
ajst-16340	36	35	,	,	PUNCT
ajst-16340	36	36	and	and	CCONJ
ajst-16340	36	37	y	y	PROPN
ajst-16340	36	38	is	be	AUX
ajst-16340	36	39	the	the	DET
ajst-16340	36	40	output	output	NOUN
ajst-16340	36	41	of	of	ADP
ajst-16340	36	42	the	the	DET
ajst-16340	36	43	neuron	neuron	NOUN
ajst-16340	36	44	.	.	PUNCT
ajst-16340	37	1	151	151	NUM
ajst-16340	37	2	2.1.2	2.1.2	NUM
ajst-16340	37	3	.	.	PUNCT
ajst-16340	37	4	network	network	NOUN
ajst-16340	37	5	structure	structure	NOUN
ajst-16340	37	6	bp	bp	PROPN
ajst-16340	37	7	neural	neural	PROPN
ajst-16340	37	8	networks	network	NOUN
ajst-16340	37	9	generally	generally	ADV
ajst-16340	37	10	consist	consist	VERB
ajst-16340	37	11	of	of	ADP
ajst-16340	37	12	three	three	NUM
ajst-16340	37	13	main	main	ADJ
ajst-16340	37	14	layers[2	layers[2	NOUN
ajst-16340	37	15	]	]	PUNCT
ajst-16340	37	16	:	:	PUNCT
ajst-16340	37	17	1.input	1.input	NUM
ajst-16340	37	18	layer	layer	NOUN
ajst-16340	37	19	:	:	PUNCT
ajst-16340	37	20	the	the	DET
ajst-16340	37	21	input	input	NOUN
ajst-16340	37	22	layer	layer	NOUN
ajst-16340	37	23	receives	receive	VERB
ajst-16340	37	24	external	external	ADJ
ajst-16340	37	25	input	input	NOUN
ajst-16340	37	26	data	datum	NOUN
ajst-16340	37	27	and	and	CCONJ
ajst-16340	37	28	passes	pass	VERB
ajst-16340	37	29	it	it	PRON
ajst-16340	37	30	to	to	ADP
ajst-16340	37	31	the	the	DET
ajst-16340	37	32	next	next	ADJ
ajst-16340	37	33	layer	layer	NOUN
ajst-16340	37	34	of	of	ADP
ajst-16340	37	35	the	the	DET
ajst-16340	37	36	network	network	NOUN
ajst-16340	37	37	.	.	PUNCT
ajst-16340	38	1	the	the	DET
ajst-16340	38	2	neurons	neuron	NOUN
ajst-16340	38	3	in	in	ADP
ajst-16340	38	4	this	this	DET
ajst-16340	38	5	layer	layer	NOUN
ajst-16340	38	6	are	be	AUX
ajst-16340	38	7	responsible	responsible	ADJ
ajst-16340	38	8	for	for	ADP
ajst-16340	38	9	receiving	receive	VERB
ajst-16340	38	10	and	and	CCONJ
ajst-16340	38	11	transmitting	transmit	VERB
ajst-16340	38	12	the	the	DET
ajst-16340	38	13	raw	raw	ADJ
ajst-16340	38	14	input	input	NOUN
ajst-16340	38	15	information	information	NOUN
ajst-16340	38	16	.	.	PUNCT
ajst-16340	39	1	2.hidden	2.hidden	NUM
ajst-16340	39	2	layer	layer	NOUN
ajst-16340	39	3	:	:	PUNCT
ajst-16340	39	4	the	the	DET
ajst-16340	39	5	hidden	hide	VERB
ajst-16340	39	6	layer	layer	NOUN
ajst-16340	39	7	is	be	AUX
ajst-16340	39	8	a	a	DET
ajst-16340	39	9	core	core	NOUN
ajst-16340	39	10	component	component	NOUN
ajst-16340	39	11	of	of	ADP
ajst-16340	39	12	the	the	DET
ajst-16340	39	13	neural	neural	ADJ
ajst-16340	39	14	network	network	NOUN
ajst-16340	39	15	,	,	PUNCT
ajst-16340	39	16	responsible	responsible	ADJ
ajst-16340	39	17	for	for	ADP
ajst-16340	39	18	processing	process	VERB
ajst-16340	39	19	input	input	NOUN
ajst-16340	39	20	data	datum	NOUN
ajst-16340	39	21	and	and	CCONJ
ajst-16340	39	22	extracting	extract	VERB
ajst-16340	39	23	features	feature	NOUN
ajst-16340	39	24	.	.	PUNCT
ajst-16340	40	1	each	each	DET
ajst-16340	40	2	neuron	neuron	NOUN
ajst-16340	40	3	in	in	ADP
ajst-16340	40	4	the	the	DET
ajst-16340	40	5	hidden	hide	VERB
ajst-16340	40	6	layer	layer	NOUN
ajst-16340	40	7	gradually	gradually	ADV
ajst-16340	40	8	adjusts	adjust	VERB
ajst-16340	40	9	its	its	PRON
ajst-16340	40	10	weights	weight	NOUN
ajst-16340	40	11	through	through	ADP
ajst-16340	40	12	the	the	DET
ajst-16340	40	13	learning	learning	NOUN
ajst-16340	40	14	process	process	NOUN
ajst-16340	40	15	,	,	PUNCT
ajst-16340	40	16	capturing	capture	VERB
ajst-16340	40	17	patterns	pattern	NOUN
ajst-16340	40	18	and	and	CCONJ
ajst-16340	40	19	correlations	correlation	NOUN
ajst-16340	40	20	in	in	ADP
ajst-16340	40	21	the	the	DET
ajst-16340	40	22	input	input	NOUN
ajst-16340	40	23	data	datum	NOUN
ajst-16340	40	24	.	.	PUNCT
ajst-16340	41	1	the	the	DET
ajst-16340	41	2	number	number	NOUN
ajst-16340	41	3	of	of	ADP
ajst-16340	41	4	hidden	hide	VERB
ajst-16340	41	5	layers	layer	NOUN
ajst-16340	41	6	and	and	CCONJ
ajst-16340	41	7	the	the	DET
ajst-16340	41	8	activation	activation	NOUN
ajst-16340	41	9	function	function	NOUN
ajst-16340	41	10	type	type	NOUN
ajst-16340	41	11	for	for	ADP
ajst-16340	41	12	each	each	DET
ajst-16340	41	13	neuron	neuron	NOUN
ajst-16340	41	14	can	can	AUX
ajst-16340	41	15	be	be	AUX
ajst-16340	41	16	adjusted	adjust	VERB
ajst-16340	41	17	based	base	VERB
ajst-16340	41	18	on	on	ADP
ajst-16340	41	19	the	the	DET
ajst-16340	41	20	specific	specific	ADJ
ajst-16340	41	21	task	task	NOUN
ajst-16340	41	22	and	and	CCONJ
ajst-16340	41	23	network	network	NOUN
ajst-16340	41	24	design	design	NOUN
ajst-16340	41	25	.	.	PUNCT
ajst-16340	42	1	3.output	3.output	NUM
ajst-16340	42	2	layer	layer	NOUN
ajst-16340	42	3	:	:	PUNCT
ajst-16340	42	4	the	the	DET
ajst-16340	42	5	output	output	NOUN
ajst-16340	42	6	layer	layer	NOUN
ajst-16340	42	7	generates	generate	VERB
ajst-16340	42	8	the	the	DET
ajst-16340	42	9	final	final	ADJ
ajst-16340	42	10	output	output	NOUN
ajst-16340	42	11	result	result	NOUN
ajst-16340	42	12	of	of	ADP
ajst-16340	42	13	the	the	DET
ajst-16340	42	14	neural	neural	ADJ
ajst-16340	42	15	network	network	NOUN
ajst-16340	42	16	.	.	PUNCT
ajst-16340	43	1	neurons	neuron	NOUN
ajst-16340	43	2	in	in	ADP
ajst-16340	43	3	this	this	DET
ajst-16340	43	4	layer	layer	NOUN
ajst-16340	43	5	integrate	integrate	VERB
ajst-16340	43	6	the	the	DET
ajst-16340	43	7	features	feature	NOUN
ajst-16340	43	8	passed	pass	VERB
ajst-16340	43	9	from	from	ADP
ajst-16340	43	10	the	the	DET
ajst-16340	43	11	hidden	hide	VERB
ajst-16340	43	12	layer	layer	NOUN
ajst-16340	43	13	,	,	PUNCT
ajst-16340	43	14	forming	form	VERB
ajst-16340	43	15	the	the	DET
ajst-16340	43	16	network	network	NOUN
ajst-16340	43	17	's	's	PART
ajst-16340	43	18	overall	overall	ADJ
ajst-16340	43	19	understanding	understanding	NOUN
ajst-16340	43	20	of	of	ADP
ajst-16340	43	21	the	the	DET
ajst-16340	43	22	input	input	NOUN
ajst-16340	43	23	data	datum	NOUN
ajst-16340	43	24	.	.	PUNCT
ajst-16340	44	1	the	the	DET
ajst-16340	44	2	choice	choice	NOUN
ajst-16340	44	3	of	of	ADP
ajst-16340	44	4	the	the	DET
ajst-16340	44	5	activation	activation	NOUN
ajst-16340	44	6	function	function	NOUN
ajst-16340	44	7	in	in	ADP
ajst-16340	44	8	the	the	DET
ajst-16340	44	9	output	output	NOUN
ajst-16340	44	10	layer	layer	NOUN
ajst-16340	44	11	often	often	ADV
ajst-16340	44	12	depends	depend	VERB
ajst-16340	44	13	on	on	ADP
ajst-16340	44	14	the	the	DET
ajst-16340	44	15	nature	nature	NOUN
ajst-16340	44	16	of	of	ADP
ajst-16340	44	17	the	the	DET
ajst-16340	44	18	problem	problem	NOUN
ajst-16340	44	19	;	;	PUNCT
ajst-16340	44	20	for	for	ADP
ajst-16340	44	21	example	example	NOUN
ajst-16340	44	22	,	,	PUNCT
ajst-16340	44	23	the	the	DET
ajst-16340	44	24	sigmoid	sigmoid	NOUN
ajst-16340	44	25	function	function	NOUN
ajst-16340	44	26	may	may	AUX
ajst-16340	44	27	be	be	AUX
ajst-16340	44	28	used	use	VERB
ajst-16340	44	29	in	in	ADP
ajst-16340	44	30	binary	binary	ADJ
ajst-16340	44	31	classification	classification	NOUN
ajst-16340	44	32	problems	problem	NOUN
ajst-16340	44	33	,	,	PUNCT
ajst-16340	44	34	while	while	SCONJ
ajst-16340	44	35	the	the	DET
ajst-16340	44	36	softmax	softmax	NOUN
ajst-16340	44	37	function	function	NOUN
ajst-16340	44	38	might	might	AUX
ajst-16340	44	39	be	be	AUX
ajst-16340	44	40	employed	employ	VERB
ajst-16340	44	41	in	in	ADP
ajst-16340	44	42	multi	multi	ADJ
ajst-16340	44	43	-	-	ADJ
ajst-16340	44	44	class	class	ADJ
ajst-16340	44	45	classification	classification	NOUN
ajst-16340	44	46	problems	problem	NOUN
ajst-16340	44	47	.	.	PUNCT
ajst-16340	45	1	the	the	DET
ajst-16340	45	2	structural	structural	ADJ
ajst-16340	45	3	diagram	diagram	NOUN
ajst-16340	45	4	is	be	AUX
ajst-16340	45	5	shown	show	VERB
ajst-16340	45	6	in	in	ADP
ajst-16340	45	7	figure	figure	NOUN
ajst-16340	45	8	1	1	NUM
ajst-16340	45	9	.	.	PUNCT
ajst-16340	46	1	x1	x1	NUM
ajst-16340	47	1	x2	x2	PROPN
ajst-16340	47	2	h1	h1	PROPN
ajst-16340	47	3	h2	h2	PROPN
ajst-16340	47	4	.	.	PUNCT
ajst-16340	47	5	.	.	PUNCT
ajst-16340	47	6	.	.	PUNCT
ajst-16340	47	7	.	.	PUNCT
ajst-16340	47	8	.	.	PUNCT
ajst-16340	47	9	.	.	PUNCT
ajst-16340	47	10	.	.	PUNCT
ajst-16340	48	1	hq	hq	PROPN
ajst-16340	48	2	hidden	hide	VERB
ajst-16340	48	3	layer	layer	NOUN
ajst-16340	48	4	output	output	NOUN
ajst-16340	48	5	layerinput	layerinput	ADJ
ajst-16340	48	6	layer	layer	NOUN
ajst-16340	48	7	y2	y2	PROPN
ajst-16340	48	8	xw	xw	PROPN
ajst-16340	48	9	.	.	PUNCT
ajst-16340	48	10	.	.	PUNCT
ajst-16340	48	11	.	.	PUNCT
ajst-16340	48	12	.	.	PUNCT
ajst-16340	48	13	.	.	PUNCT
ajst-16340	48	14	.	.	PUNCT
ajst-16340	49	1	.	.	PUNCT
ajst-16340	50	1	y1	y1	INTJ
ajst-16340	50	2	.	.	PUNCT
ajst-16340	50	3	.	.	PUNCT
ajst-16340	50	4	.	.	PUNCT
ajst-16340	50	5	.	.	PUNCT
ajst-16340	50	6	.	.	PUNCT
ajst-16340	50	7	.	.	PUNCT
ajst-16340	50	8	.	.	PUNCT
ajst-16340	51	1	ye	ye	PRON
ajst-16340	51	2	figure	figure	NOUN
ajst-16340	51	3	1	1	NUM
ajst-16340	51	4	.	.	PUNCT
ajst-16340	51	5	schematic	schematic	ADJ
ajst-16340	51	6	diagram	diagram	NOUN
ajst-16340	51	7	of	of	ADP
ajst-16340	51	8	bp	bp	PROPN
ajst-16340	51	9	neural	neural	ADJ
ajst-16340	51	10	network	network	NOUN
ajst-16340	51	11	structure	structure	NOUN
ajst-16340	51	12	2.2	2.2	NUM
ajst-16340	51	13	.	.	PUNCT
ajst-16340	52	1	feedforward	feedforward	NOUN
ajst-16340	52	2	and	and	CCONJ
ajst-16340	52	3	backpropagation	backpropagation	NOUN
ajst-16340	52	4	algorithms	algorithm	NOUN
ajst-16340	52	5	feedforward	feedforward	NOUN
ajst-16340	52	6	propagation	propagation	NOUN
ajst-16340	52	7	is	be	AUX
ajst-16340	52	8	the	the	DET
ajst-16340	52	9	process	process	NOUN
ajst-16340	52	10	of	of	ADP
ajst-16340	52	11	information	information	NOUN
ajst-16340	52	12	transmission	transmission	NOUN
ajst-16340	52	13	in	in	ADP
ajst-16340	52	14	a	a	DET
ajst-16340	52	15	neural	neural	ADJ
ajst-16340	52	16	network	network	NOUN
ajst-16340	52	17	from	from	ADP
ajst-16340	52	18	the	the	DET
ajst-16340	52	19	input	input	NOUN
ajst-16340	52	20	layer	layer	NOUN
ajst-16340	52	21	to	to	ADP
ajst-16340	52	22	the	the	DET
ajst-16340	52	23	output	output	NOUN
ajst-16340	52	24	layer	layer	NOUN
ajst-16340	52	25	.	.	PUNCT
ajst-16340	53	1	for	for	ADP
ajst-16340	53	2	a	a	DET
ajst-16340	53	3	neuron	neuron	PROPN
ajst-16340	53	4	j	j	PROPN
ajst-16340	53	5	in	in	ADP
ajst-16340	53	6	the	the	DET
ajst-16340	53	7	l	l	NOUN
ajst-16340	53	8	-	-	PUNCT
ajst-16340	53	9	th	th	X
ajst-16340	53	10	layer，the	layer，the	DET
ajst-16340	53	11	calculation	calculation	NOUN
ajst-16340	53	12	formula	formula	NOUN
ajst-16340	53	13	for	for	ADP
ajst-16340	53	14	its	its	PRON
ajst-16340	53	15	output	output	NOUN
ajst-16340	53	16	(	(	PUNCT
ajst-16340	53	17	)	)	PUNCT
ajst-16340	53	18	l	l	NOUN
ajst-16340	53	19	ja	ja	PROPN
ajst-16340	53	20	is	be	AUX
ajst-16340	53	21	given	give	VERB
ajst-16340	53	22	by	by	ADP
ajst-16340	53	23	:	:	PUNCT
ajst-16340	53	24	1	1	NUM
ajst-16340	53	25	(	(	PUNCT
ajst-16340	53	26	)	)	PUNCT
ajst-16340	53	27	(	(	PUNCT
ajst-16340	53	28	)	)	PUNCT
ajst-16340	53	29	(	(	PUNCT
ajst-16340	53	30	1	1	X
ajst-16340	53	31	)	)	PUNCT
ajst-16340	53	32	(	(	PUNCT
ajst-16340	53	33	)	)	PUNCT
ajst-16340	54	1	i	i	PRON
ajst-16340	54	2	l	l	NOUN
ajst-16340	54	3	l	l	X
ajst-16340	54	4	l	l	X
ajst-16340	54	5	l	l	X
ajst-16340	54	6	j	j	NOUN
ajst-16340	55	1	ij	ij	INTJ
ajst-16340	55	2	i	i	PRON
ajst-16340	55	3	j	j	PROPN
ajst-16340	55	4	n	n	CCONJ
ajst-16340	55	5	a	a	DET
ajst-16340	55	6	f	f	X
ajst-16340	55	7	w	w	PROPN
ajst-16340	55	8	a	a	DET
ajst-16340	55	9	b	b	NOUN
ajst-16340	55	10			PROPN
ajst-16340	55	11			PROPN
ajst-16340	55	12			X
ajst-16340	55	13			NUM
ajst-16340	55	14			ADJ
ajst-16340	55	15			PROPN
ajst-16340	55	16			CCONJ
ajst-16340	55	17			PROPN
ajst-16340	55	18			NOUN
ajst-16340	55	19	(	(	PUNCT
ajst-16340	55	20	2	2	NUM
ajst-16340	55	21	)	)	PUNCT
ajst-16340	55	22	as	as	SCONJ
ajst-16340	55	23	shown	show	VERB
ajst-16340	55	24	in	in	ADP
ajst-16340	55	25	equation	equation	NOUN
ajst-16340	55	26	(	(	PUNCT
ajst-16340	55	27	2	2	NUM
ajst-16340	55	28	)	)	PUNCT
ajst-16340	55	29	:	:	PUNCT
ajst-16340	55	30	(	(	PUNCT
ajst-16340	55	31	)	)	PUNCT
ajst-16340	55	32	l	l	NOUN
ajst-16340	55	33	ijw	ijw	NOUN
ajst-16340	55	34	is	be	AUX
ajst-16340	55	35	the	the	DET
ajst-16340	55	36	weight	weight	NOUN
ajst-16340	55	37	connecting	connect	VERB
ajst-16340	55	38	neuron	neuron	NOUN
ajst-16340	55	39	i	i	PRON
ajst-16340	55	40	and	and	CCONJ
ajst-16340	55	41	j	j	PROPN
ajst-16340	56	1	，	，	PROPN
ajst-16340	56	2	(	(	PUNCT
ajst-16340	56	3	1)l	1)l	NUM
ajst-16340	56	4	ia	ia	PROPN
ajst-16340	56	5			PROPN
ajst-16340	56	6	is	be	AUX
ajst-16340	56	7	the	the	DET
ajst-16340	56	8	output	output	NOUN
ajst-16340	56	9	of	of	ADP
ajst-16340	56	10	neuron	neuron	NOUN
ajst-16340	56	11	i	i	PRON
ajst-16340	56	12	in	in	ADP
ajst-16340	56	13	the	the	DET
ajst-16340	56	14	previous	previous	ADJ
ajst-16340	56	15	layer	layer	NOUN
ajst-16340	56	16	,	,	PUNCT
ajst-16340	56	17	(	(	PUNCT
ajst-16340	56	18	)	)	PUNCT
ajst-16340	56	19	l	l	NOUN
ajst-16340	56	20	jb	jb	PROPN
ajst-16340	56	21	is	be	AUX
ajst-16340	56	22	the	the	DET
ajst-16340	56	23	bias	bias	NOUN
ajst-16340	56	24	of	of	ADP
ajst-16340	56	25	neuron	neuron	PROPN
ajst-16340	56	26	j	j	PROPN
ajst-16340	56	27	。	。	PROPN
ajst-16340	56	28	backpropagation	backpropagation	NOUN
ajst-16340	56	29	optimizes	optimize	VERB
ajst-16340	56	30	the	the	DET
ajst-16340	56	31	network	network	NOUN
ajst-16340	56	32	weights	weight	NOUN
ajst-16340	56	33	and	and	CCONJ
ajst-16340	56	34	biases	bias	NOUN
ajst-16340	56	35	by	by	ADP
ajst-16340	56	36	computing	compute	VERB
ajst-16340	56	37	the	the	DET
ajst-16340	56	38	gradient	gradient	NOUN
ajst-16340	56	39	of	of	ADP
ajst-16340	56	40	the	the	DET
ajst-16340	56	41	loss	loss	NOUN
ajst-16340	56	42	function	function	NOUN
ajst-16340	56	43	with	with	ADP
ajst-16340	56	44	respect	respect	NOUN
ajst-16340	56	45	to	to	ADP
ajst-16340	56	46	the	the	DET
ajst-16340	56	47	network	network	NOUN
ajst-16340	56	48	parameters	parameter	NOUN
ajst-16340	56	49	.	.	PUNCT
ajst-16340	57	1	the	the	DET
ajst-16340	57	2	gradient	gradient	NOUN
ajst-16340	57	3	formulas	formula	NOUN
ajst-16340	57	4	for	for	ADP
ajst-16340	57	5	weights	weight	NOUN
ajst-16340	57	6	and	and	CCONJ
ajst-16340	57	7	biases	bias	NOUN
ajst-16340	57	8	are	be	AUX
ajst-16340	57	9	given	give	VERB
ajst-16340	57	10	by	by	ADP
ajst-16340	57	11	:	:	PUNCT
ajst-16340	57	12	(	(	PUNCT
ajst-16340	57	13	)	)	PUNCT
ajst-16340	57	14	(	(	PUNCT
ajst-16340	57	15	)	)	PUNCT
ajst-16340	57	16	δ	δ	X
ajst-16340	57	17	l	l	NOUN
ajst-16340	58	1	ij	ij	X
ajst-16340	58	2	l	l	NOUN
ajst-16340	59	1	ij	ij	X
ajst-16340	59	2	e	e	X
ajst-16340	59	3	w	w	PROPN
ajst-16340	59	4	w	w	PROPN
ajst-16340	59	5			PROPN
ajst-16340	59	6			ADJ
ajst-16340	59	7			PROPN
ajst-16340	59	8			NOUN
ajst-16340	59	9	(	(	PUNCT
ajst-16340	59	10	3	3	NUM
ajst-16340	59	11	)	)	PUNCT
ajst-16340	59	12	(	(	PUNCT
ajst-16340	59	13	)	)	PUNCT
ajst-16340	59	14	(	(	PUNCT
ajst-16340	59	15	)	)	PUNCT
ajst-16340	59	16	δ	δ	X
ajst-16340	59	17	l	l	NOUN
ajst-16340	59	18	j	j	PROPN
ajst-16340	59	19	l	l	X
ajst-16340	59	20	j	j	PROPN
ajst-16340	59	21	e	e	PROPN
ajst-16340	59	22	b	b	PROPN
ajst-16340	59	23	b	b	PROPN
ajst-16340	59	24			PROPN
ajst-16340	59	25			PROPN
ajst-16340	59	26			PROPN
ajst-16340	59	27			NOUN
ajst-16340	59	28	(	(	PUNCT
ajst-16340	59	29	4	4	NUM
ajst-16340	59	30	)	)	PUNCT
ajst-16340	59	31	as	as	SCONJ
ajst-16340	59	32	shown	show	VERB
ajst-16340	59	33	in	in	ADP
ajst-16340	59	34	equations	equation	NOUN
ajst-16340	59	35	(	(	PUNCT
ajst-16340	59	36	3	3	NUM
ajst-16340	59	37	)	)	PUNCT
ajst-16340	59	38	and	and	CCONJ
ajst-16340	59	39	(	(	PUNCT
ajst-16340	59	40	4):	4):	PROPN
ajst-16340	59	41	s	s	VERB
ajst-16340	59	42	the	the	DET
ajst-16340	59	43	learning	learning	NOUN
ajst-16340	59	44	rate	rate	NOUN
ajst-16340	59	45	,	,	PUNCT
ajst-16340	59	46	and	and	CCONJ
ajst-16340	59	47	e	e	NOUN
ajst-16340	59	48	represents	represent	VERB
ajst-16340	59	49	the	the	DET
ajst-16340	59	50	loss	loss	NOUN
ajst-16340	59	51	function	function	NOUN
ajst-16340	59	52	.	.	PUNCT
ajst-16340	60	1	2.3	2.3	NUM
ajst-16340	60	2	.	.	PUNCT
ajst-16340	60	3	weight	weight	NOUN
ajst-16340	60	4	and	and	CCONJ
ajst-16340	60	5	bias	bias	NOUN
ajst-16340	60	6	updates	update	VERB
ajst-16340	60	7	the	the	DET
ajst-16340	60	8	learning	learning	NOUN
ajst-16340	60	9	process	process	NOUN
ajst-16340	60	10	involves	involve	VERB
ajst-16340	60	11	adjusting	adjust	VERB
ajst-16340	60	12	weights	weight	NOUN
ajst-16340	60	13	and	and	CCONJ
ajst-16340	60	14	biases	bias	NOUN
ajst-16340	60	15	using	use	VERB
ajst-16340	60	16	optimization	optimization	NOUN
ajst-16340	60	17	algorithms	algorithm	NOUN
ajst-16340	60	18	such	such	ADJ
ajst-16340	60	19	as	as	ADP
ajst-16340	60	20	gradient	gradient	ADJ
ajst-16340	60	21	descent	descent	NOUN
ajst-16340	60	22	,	,	PUNCT
ajst-16340	60	23	where	where	SCONJ
ajst-16340	60	24	the	the	DET
ajst-16340	60	25	learning	learning	NOUN
ajst-16340	60	26	rate	rate	NOUN
ajst-16340	60	27	(	(	PUNCT
ajst-16340	60	28	\(\eta\	\(\eta\	NOUN
ajst-16340	60	29	)	)	PUNCT
ajst-16340	60	30	)	)	PUNCT
ajst-16340	60	31	determines	determine	VERB
ajst-16340	60	32	the	the	DET
ajst-16340	60	33	step	step	NOUN
ajst-16340	60	34	size	size	NOUN
ajst-16340	60	35	of	of	ADP
ajst-16340	60	36	parameter	parameter	NOUN
ajst-16340	60	37	updates	update	NOUN
ajst-16340	60	38	.	.	PUNCT
ajst-16340	61	1	(	(	PUNCT
ajst-16340	61	2	)	)	PUNCT
ajst-16340	61	3	(	(	PUNCT
ajst-16340	61	4	)	)	PUNCT
ajst-16340	61	5	(	(	PUNCT
ajst-16340	61	6	)	)	PUNCT
ajst-16340	61	7	δl	δl	PROPN
ajst-16340	61	8	l	l	NOUN
ajst-16340	61	9	l	l	NOUN
ajst-16340	61	10	ij	ij	INTJ
ajst-16340	61	11	ij	ij	INTJ
ajst-16340	61	12	ijw	ijw	PROPN
ajst-16340	61	13	w	w	NOUN
ajst-16340	61	14	w	w	PROPN
ajst-16340	61	15			PROPN
ajst-16340	61	16	(	(	PUNCT
ajst-16340	61	17	5	5	NUM
ajst-16340	61	18	)	)	PUNCT
ajst-16340	61	19	(	(	PUNCT
ajst-16340	61	20	)	)	PUNCT
ajst-16340	61	21	(	(	PUNCT
ajst-16340	61	22	)	)	PUNCT
ajst-16340	61	23	(	(	PUNCT
ajst-16340	61	24	)	)	PUNCT
ajst-16340	61	25	δl	δl	PROPN
ajst-16340	61	26	l	l	PROPN
ajst-16340	61	27	l	l	PROPN
ajst-16340	62	1	j	j	PROPN
ajst-16340	62	2	j	j	PROPN
ajst-16340	62	3	jb	jb	PROPN
ajst-16340	62	4	b	b	PROPN
ajst-16340	62	5	b	b	PROPN
ajst-16340	62	6			X
ajst-16340	62	7	(	(	PUNCT
ajst-16340	62	8	6	6	NUM
ajst-16340	62	9	)	)	PUNCT
ajst-16340	62	10	2.4	2.4	NUM
ajst-16340	62	11	.	.	PUNCT
ajst-16340	63	1	training	training	NOUN
ajst-16340	63	2	and	and	CCONJ
ajst-16340	63	3	optimization	optimization	NOUN
ajst-16340	63	4	of	of	ADP
ajst-16340	63	5	bp	bp	PROPN
ajst-16340	63	6	neural	neural	ADJ
ajst-16340	63	7	networks	network	NOUN
ajst-16340	63	8	in	in	ADP
ajst-16340	63	9	the	the	DET
ajst-16340	63	10	training	training	NOUN
ajst-16340	63	11	process	process	NOUN
ajst-16340	63	12	of	of	ADP
ajst-16340	63	13	bp	bp	PROPN
ajst-16340	63	14	neural	neural	PROPN
ajst-16340	63	15	networks	network	NOUN
ajst-16340	63	16	,	,	PUNCT
ajst-16340	63	17	the	the	DET
ajst-16340	63	18	loss	loss	NOUN
ajst-16340	63	19	function	function	NOUN
ajst-16340	63	20	is	be	AUX
ajst-16340	63	21	used	use	VERB
ajst-16340	63	22	to	to	PART
ajst-16340	63	23	evaluate	evaluate	VERB
ajst-16340	63	24	the	the	DET
ajst-16340	63	25	difference	difference	NOUN
ajst-16340	63	26	between	between	ADP
ajst-16340	63	27	the	the	DET
ajst-16340	63	28	model	model	NOUN
ajst-16340	63	29	's	's	PART
ajst-16340	63	30	output	output	NOUN
ajst-16340	63	31	and	and	CCONJ
ajst-16340	63	32	the	the	DET
ajst-16340	63	33	actual	actual	ADJ
ajst-16340	63	34	labels	label	NOUN
ajst-16340	63	35	.	.	PUNCT
ajst-16340	64	1	commonly	commonly	ADV
ajst-16340	64	2	used	use	VERB
ajst-16340	64	3	loss	loss	NOUN
ajst-16340	64	4	functions	function	NOUN
ajst-16340	64	5	include	include	VERB
ajst-16340	64	6	mean	mean	VERB
ajst-16340	64	7	squared	square	VERB
ajst-16340	64	8	error	error	NOUN
ajst-16340	64	9	(	(	PUNCT
ajst-16340	64	10	mse	mse	NOUN
ajst-16340	64	11	)	)	PUNCT
ajst-16340	64	12	and	and	CCONJ
ajst-16340	64	13	crossentropy	crossentropy	NOUN
ajst-16340	64	14	loss	loss	NOUN
ajst-16340	64	15	.	.	PUNCT
ajst-16340	65	1	mse	mse	PROPN
ajst-16340	65	2	is	be	AUX
ajst-16340	65	3	suitable	suitable	ADJ
ajst-16340	65	4	for	for	ADP
ajst-16340	65	5	regression	regression	NOUN
ajst-16340	65	6	problems	problem	NOUN
ajst-16340	65	7	,	,	PUNCT
ajst-16340	65	8	while	while	SCONJ
ajst-16340	65	9	cross	cross	ADJ
ajst-16340	65	10	-	-	NOUN
ajst-16340	65	11	entropy	entropy	NOUN
ajst-16340	65	12	is	be	AUX
ajst-16340	65	13	typically	typically	ADV
ajst-16340	65	14	used	use	VERB
ajst-16340	65	15	for	for	ADP
ajst-16340	65	16	classification	classification	NOUN
ajst-16340	65	17	problems	problem	NOUN
ajst-16340	65	18	.	.	PUNCT
ajst-16340	66	1	the	the	DET
ajst-16340	66	2	formulas	formula	NOUN
ajst-16340	66	3	for	for	ADP
ajst-16340	66	4	mean	mean	ADJ
ajst-16340	66	5	squared	square	VERB
ajst-16340	66	6	error	error	NOUN
ajst-16340	66	7	(	(	PUNCT
ajst-16340	66	8	mse	mse	NOUN
ajst-16340	66	9	)	)	PUNCT
ajst-16340	66	10	and	and	CCONJ
ajst-16340	66	11	cross	cross	VERB
ajst-16340	66	12	152	152	NUM
ajst-16340	66	13	entropy	entropy	NOUN
ajst-16340	66	14	loss	loss	NOUN
ajst-16340	66	15	are	be	AUX
ajst-16340	66	16	as	as	SCONJ
ajst-16340	66	17	follows	follow	VERB
ajst-16340	66	18	:	:	PUNCT
ajst-16340	66	19	1	1	NUM
ajst-16340	66	20	21	21	NUM
ajst-16340	66	21	(	(	PUNCT
ajst-16340	66	22	)	)	PUNCT
ajst-16340	66	23	2	2	NUM
ajst-16340	66	24	ˆ	ˆ	NOUN
ajst-16340	67	1	i	i	PRON
ajst-16340	67	2	i	i	PRON
ajst-16340	67	3	i	i	PRON
ajst-16340	67	4	n	n	VERB
ajst-16340	67	5	e	e	X
ajst-16340	67	6	y	y	PROPN
ajst-16340	67	7	y	y	PROPN
ajst-16340	68	1	n	n	CCONJ
ajst-16340	68	2			NUM
ajst-16340	69	1			NUM
ajst-16340	69	2			NOUN
ajst-16340	69	3			NOUN
ajst-16340	69	4	(	(	PUNCT
ajst-16340	69	5	7	7	NUM
ajst-16340	69	6	)	)	PUNCT
ajst-16340	69	7			NOUN
ajst-16340	69	8			PROPN
ajst-16340	69	9	11	11	NUM
ajst-16340	69	10	log	log	NOUN
ajst-16340	69	11	(	(	PUNCT
ajst-16340	69	12	)	)	PUNCT
ajst-16340	69	13	(	(	PUNCT
ajst-16340	69	14	1	1	X
ajst-16340	69	15	)	)	PUNCT
ajst-16340	69	16	log	log	NOUN
ajst-16340	69	17	(	(	PUNCT
ajst-16340	69	18	ˆ	ˆ	NOUN
ajst-16340	69	19	)	)	PUNCT
ajst-16340	69	20	ˆ	ˆ	ADP
ajst-16340	69	21	1	1	NUM
ajst-16340	70	1	i	i	PRON
ajst-16340	71	1	i	i	PRON
ajst-16340	72	1	i	i	PRON
ajst-16340	73	1	i	i	VERB
ajst-16340	74	1	i	i	VERB
ajst-16340	74	2	n	n	VERB
ajst-16340	74	3	e	e	X
ajst-16340	74	4	y	y	PROPN
ajst-16340	74	5	y	y	PROPN
ajst-16340	74	6	y	y	PROPN
ajst-16340	74	7	y	y	PROPN
ajst-16340	75	1	n	n	CCONJ
ajst-16340	75	2			PROPN
ajst-16340	75	3			PROPN
ajst-16340	75	4			PROPN
ajst-16340	75	5			X
ajst-16340	75	6			VERB
ajst-16340	75	7			PROPN
ajst-16340	75	8			PROPN
ajst-16340	75	9	(	(	PUNCT
ajst-16340	75	10	8)	8)	NUM
ajst-16340	75	11	as	as	SCONJ
ajst-16340	75	12	shown	show	VERB
ajst-16340	75	13	in	in	ADP
ajst-16340	75	14	equations	equation	NOUN
ajst-16340	75	15	(	(	PUNCT
ajst-16340	75	16	7	7	NUM
ajst-16340	75	17	)	)	PUNCT
ajst-16340	75	18	and	and	CCONJ
ajst-16340	75	19	(	(	PUNCT
ajst-16340	75	20	8)	8)	NUM
ajst-16340	75	21	:	:	PUNCT
ajst-16340	75	22	n	n	PRON
ajst-16340	75	23	is	be	AUX
ajst-16340	75	24	the	the	DET
ajst-16340	75	25	number	number	NOUN
ajst-16340	75	26	of	of	ADP
ajst-16340	75	27	samples	sample	NOUN
ajst-16340	75	28	,	,	PUNCT
ajst-16340	75	29	iy	iy	PROPN
ajst-16340	75	30	is	be	AUX
ajst-16340	75	31	the	the	DET
ajst-16340	75	32	actual	actual	ADJ
ajst-16340	75	33	label	label	NOUN
ajst-16340	75	34	,	,	PUNCT
ajst-16340	75	35	and	and	CCONJ
ajst-16340	75	36	îy	îy	ADV
ajst-16340	75	37	is	be	AUX
ajst-16340	75	38	the	the	DET
ajst-16340	75	39	model	model	NOUN
ajst-16340	75	40	's	's	PART
ajst-16340	75	41	predicted	predict	VERB
ajst-16340	75	42	output	output	NOUN
ajst-16340	75	43	.	.	PUNCT
ajst-16340	76	1	gradient	gradient	ADJ
ajst-16340	76	2	descent	descent	NOUN
ajst-16340	76	3	is	be	AUX
ajst-16340	76	4	a	a	DET
ajst-16340	76	5	common	common	ADJ
ajst-16340	76	6	method	method	NOUN
ajst-16340	76	7	for	for	ADP
ajst-16340	76	8	adjusting	adjust	VERB
ajst-16340	76	9	network	network	NOUN
ajst-16340	76	10	parameters[3	parameters[3	PROPN
ajst-16340	76	11	]	]	X
ajst-16340	76	12	,	,	PUNCT
ajst-16340	76	13	but	but	CCONJ
ajst-16340	76	14	there	there	PRON
ajst-16340	76	15	are	be	VERB
ajst-16340	76	16	some	some	DET
ajst-16340	76	17	variants	variant	NOUN
ajst-16340	76	18	that	that	PRON
ajst-16340	76	19	can	can	AUX
ajst-16340	76	20	improve	improve	VERB
ajst-16340	76	21	performance	performance	NOUN
ajst-16340	76	22	.	.	PUNCT
ajst-16340	77	1	among	among	ADP
ajst-16340	77	2	them	they	PRON
ajst-16340	77	3	,	,	PUNCT
ajst-16340	77	4	stochastic	stochastic	ADJ
ajst-16340	77	5	gradient	gradient	ADJ
ajst-16340	77	6	descent	descent	NOUN
ajst-16340	77	7	(	(	PUNCT
ajst-16340	77	8	sgd	sgd	NOUN
ajst-16340	77	9	)	)	PUNCT
ajst-16340	77	10	,	,	PUNCT
ajst-16340	77	11	batch	batch	VERB
ajst-16340	77	12	gradient	gradient	ADJ
ajst-16340	77	13	descent	descent	NOUN
ajst-16340	77	14	,	,	PUNCT
ajst-16340	77	15	and	and	CCONJ
ajst-16340	77	16	mini	mini	ADJ
ajst-16340	77	17	-	-	NOUN
ajst-16340	77	18	batch	batch	ADJ
ajst-16340	77	19	gradient	gradient	ADJ
ajst-16340	77	20	descent	descent	NOUN
ajst-16340	77	21	are	be	AUX
ajst-16340	77	22	the	the	DET
ajst-16340	77	23	three	three	NUM
ajst-16340	77	24	most	most	ADV
ajst-16340	77	25	common	common	ADJ
ajst-16340	77	26	.	.	PUNCT
ajst-16340	78	1	1	1	X
ajst-16340	78	2	.	.	X
ajst-16340	78	3	stochastic	stochastic	ADJ
ajst-16340	78	4	gradient	gradient	ADJ
ajst-16340	78	5	descent	descent	NOUN
ajst-16340	78	6	(	(	PUNCT
ajst-16340	78	7	sgd	sgd	PROPN
ajst-16340	78	8	):	):	PUNCT
ajst-16340	78	9	in	in	ADP
ajst-16340	78	10	each	each	DET
ajst-16340	78	11	iteration	iteration	NOUN
ajst-16340	78	12	,	,	PUNCT
ajst-16340	78	13	sgd	sgd	PROPN
ajst-16340	78	14	updates	update	VERB
ajst-16340	78	15	parameters	parameter	NOUN
ajst-16340	78	16	using	use	VERB
ajst-16340	78	17	a	a	DET
ajst-16340	78	18	single	single	ADJ
ajst-16340	78	19	sample	sample	NOUN
ajst-16340	78	20	.	.	PUNCT
ajst-16340	79	1	this	this	DET
ajst-16340	79	2	method	method	NOUN
ajst-16340	79	3	has	have	VERB
ajst-16340	79	4	a	a	DET
ajst-16340	79	5	lower	low	ADJ
ajst-16340	79	6	computational	computational	ADJ
ajst-16340	79	7	cost	cost	NOUN
ajst-16340	79	8	,	,	PUNCT
ajst-16340	79	9	but	but	CCONJ
ajst-16340	79	10	the	the	DET
ajst-16340	79	11	noise	noise	NOUN
ajst-16340	79	12	from	from	ADP
ajst-16340	79	13	individual	individual	ADJ
ajst-16340	79	14	samples	sample	NOUN
ajst-16340	79	15	may	may	AUX
ajst-16340	79	16	lead	lead	VERB
ajst-16340	79	17	to	to	ADP
ajst-16340	79	18	unstable	unstable	ADJ
ajst-16340	79	19	parameter	parameter	NOUN
ajst-16340	79	20	updates	update	NOUN
ajst-16340	79	21	.	.	PUNCT
ajst-16340	80	1	despite	despite	SCONJ
ajst-16340	80	2	this	this	PRON
ajst-16340	80	3	,	,	PUNCT
ajst-16340	80	4	sgd	sgd	PROPN
ajst-16340	80	5	is	be	AUX
ajst-16340	80	6	often	often	ADV
ajst-16340	80	7	widely	widely	ADV
ajst-16340	80	8	applied	apply	VERB
ajst-16340	80	9	,	,	PUNCT
ajst-16340	80	10	especially	especially	ADV
ajst-16340	80	11	on	on	ADP
ajst-16340	80	12	large	large	ADJ
ajst-16340	80	13	datasets	dataset	NOUN
ajst-16340	80	14	.	.	PUNCT
ajst-16340	81	1	2	2	X
ajst-16340	81	2	.	.	NOUN
ajst-16340	81	3	batch	batch	NOUN
ajst-16340	81	4	gradient	gradient	ADJ
ajst-16340	81	5	descent	descent	NOUN
ajst-16340	81	6	:	:	PUNCT
ajst-16340	81	7	batch	batch	VERB
ajst-16340	81	8	gradient	gradient	ADJ
ajst-16340	81	9	descent	descent	NOUN
ajst-16340	81	10	updates	update	VERB
ajst-16340	81	11	parameters	parameter	NOUN
ajst-16340	81	12	using	use	VERB
ajst-16340	81	13	the	the	DET
ajst-16340	81	14	entire	entire	ADJ
ajst-16340	81	15	training	training	NOUN
ajst-16340	81	16	set	set	NOUN
ajst-16340	81	17	,	,	PUNCT
ajst-16340	81	18	calculating	calculate	VERB
ajst-16340	81	19	the	the	DET
ajst-16340	81	20	average	average	ADJ
ajst-16340	81	21	gradient	gradient	NOUN
ajst-16340	81	22	.	.	PUNCT
ajst-16340	82	1	the	the	DET
ajst-16340	82	2	advantage	advantage	NOUN
ajst-16340	82	3	of	of	ADP
ajst-16340	82	4	this	this	DET
ajst-16340	82	5	method	method	NOUN
ajst-16340	82	6	is	be	AUX
ajst-16340	82	7	that	that	SCONJ
ajst-16340	82	8	the	the	DET
ajst-16340	82	9	gradient	gradient	ADJ
ajst-16340	82	10	calculation	calculation	NOUN
ajst-16340	82	11	is	be	AUX
ajst-16340	82	12	relatively	relatively	ADV
ajst-16340	82	13	stable	stable	ADJ
ajst-16340	82	14	,	,	PUNCT
ajst-16340	82	15	but	but	CCONJ
ajst-16340	82	16	it	it	PRON
ajst-16340	82	17	comes	come	VERB
ajst-16340	82	18	with	with	ADP
ajst-16340	82	19	a	a	DET
ajst-16340	82	20	higher	high	ADJ
ajst-16340	82	21	computational	computational	ADJ
ajst-16340	82	22	cost	cost	NOUN
ajst-16340	82	23	,	,	PUNCT
ajst-16340	82	24	especially	especially	ADV
ajst-16340	82	25	for	for	ADP
ajst-16340	82	26	large	large	ADJ
ajst-16340	82	27	datasets	dataset	NOUN
ajst-16340	82	28	.	.	PUNCT
ajst-16340	83	1	batch	batch	NOUN
ajst-16340	83	2	gradient	gradient	ADJ
ajst-16340	83	3	descent	descent	NOUN
ajst-16340	83	4	is	be	AUX
ajst-16340	83	5	typically	typically	ADV
ajst-16340	83	6	used	use	VERB
ajst-16340	83	7	for	for	ADP
ajst-16340	83	8	small	small	ADJ
ajst-16340	83	9	datasets	dataset	NOUN
ajst-16340	83	10	or	or	CCONJ
ajst-16340	83	11	situations	situation	NOUN
ajst-16340	83	12	where	where	SCONJ
ajst-16340	83	13	computational	computational	ADJ
ajst-16340	83	14	resources	resource	NOUN
ajst-16340	83	15	are	be	AUX
ajst-16340	83	16	abundant	abundant	ADJ
ajst-16340	83	17	.	.	PUNCT
ajst-16340	84	1	3	3	X
ajst-16340	84	2	.	.	X
ajst-16340	84	3	mini	mini	ADJ
ajst-16340	84	4	-	-	NOUN
ajst-16340	84	5	batch	batch	ADJ
ajst-16340	84	6	gradient	gradient	ADJ
ajst-16340	84	7	descent	descent	NOUN
ajst-16340	84	8	:	:	PUNCT
ajst-16340	84	9	mini	mini	ADJ
ajst-16340	84	10	-	-	NOUN
ajst-16340	84	11	batch	batch	ADJ
ajst-16340	84	12	gradient	gradient	ADJ
ajst-16340	84	13	descent	descent	NOUN
ajst-16340	84	14	is	be	AUX
ajst-16340	84	15	a	a	DET
ajst-16340	84	16	compromise	compromise	NOUN
ajst-16340	84	17	between	between	ADP
ajst-16340	84	18	the	the	DET
ajst-16340	84	19	above	above	ADJ
ajst-16340	84	20	two	two	NUM
ajst-16340	84	21	methods	method	NOUN
ajst-16340	84	22	,	,	PUNCT
ajst-16340	84	23	updating	update	VERB
ajst-16340	84	24	parameters	parameter	NOUN
ajst-16340	84	25	using	use	VERB
ajst-16340	84	26	a	a	DET
ajst-16340	84	27	small	small	ADJ
ajst-16340	84	28	subset	subset	NOUN
ajst-16340	84	29	of	of	ADP
ajst-16340	84	30	samples	sample	NOUN
ajst-16340	84	31	in	in	ADP
ajst-16340	84	32	each	each	DET
ajst-16340	84	33	iteration	iteration	NOUN
ajst-16340	84	34	.	.	PUNCT
ajst-16340	85	1	this	this	DET
ajst-16340	85	2	approach	approach	NOUN
ajst-16340	85	3	balances	balance	VERB
ajst-16340	85	4	computational	computational	ADJ
ajst-16340	85	5	efficiency	efficiency	NOUN
ajst-16340	85	6	and	and	CCONJ
ajst-16340	85	7	relatively	relatively	ADV
ajst-16340	85	8	stable	stable	ADJ
ajst-16340	85	9	parameter	parameter	NOUN
ajst-16340	85	10	updates	update	NOUN
ajst-16340	85	11	,	,	PUNCT
ajst-16340	85	12	making	make	VERB
ajst-16340	85	13	it	it	PRON
ajst-16340	85	14	the	the	DET
ajst-16340	85	15	preferred	preferred	ADJ
ajst-16340	85	16	method	method	NOUN
ajst-16340	85	17	for	for	ADP
ajst-16340	85	18	most	most	ADJ
ajst-16340	85	19	deep	deep	ADJ
ajst-16340	85	20	learning	learning	NOUN
ajst-16340	85	21	tasks	task	NOUN
ajst-16340	85	22	.	.	PUNCT
ajst-16340	86	1	mini	mini	ADJ
ajst-16340	86	2	-	-	NOUN
ajst-16340	86	3	batch	batch	ADJ
ajst-16340	86	4	gradient	gradient	ADJ
ajst-16340	86	5	descent	descent	NOUN
ajst-16340	86	6	often	often	ADV
ajst-16340	86	7	exhibits	exhibit	VERB
ajst-16340	86	8	good	good	ADJ
ajst-16340	86	9	convergence	convergence	NOUN
ajst-16340	86	10	performance	performance	NOUN
ajst-16340	86	11	,	,	PUNCT
ajst-16340	86	12	especially	especially	ADV
ajst-16340	86	13	in	in	ADP
ajst-16340	86	14	the	the	DET
ajst-16340	86	15	case	case	NOUN
ajst-16340	86	16	of	of	ADP
ajst-16340	86	17	large	large	ADJ
ajst-16340	86	18	datasets	dataset	NOUN
ajst-16340	86	19	and	and	CCONJ
ajst-16340	86	20	deep	deep	ADJ
ajst-16340	86	21	networks	network	NOUN
ajst-16340	86	22	.	.	PUNCT
ajst-16340	87	1	the	the	DET
ajst-16340	87	2	choice	choice	NOUN
ajst-16340	87	3	among	among	ADP
ajst-16340	87	4	these	these	DET
ajst-16340	87	5	three	three	NUM
ajst-16340	87	6	variants	variant	NOUN
ajst-16340	87	7	of	of	ADP
ajst-16340	87	8	gradient	gradient	ADJ
ajst-16340	87	9	descent	descent	NOUN
ajst-16340	87	10	depends	depend	VERB
ajst-16340	87	11	on	on	ADP
ajst-16340	87	12	task	task	NOUN
ajst-16340	87	13	requirements	requirement	NOUN
ajst-16340	87	14	and	and	CCONJ
ajst-16340	87	15	dataset	dataset	ADJ
ajst-16340	87	16	sizes	size	NOUN
ajst-16340	87	17	in	in	ADP
ajst-16340	87	18	practical	practical	ADJ
ajst-16340	87	19	applications	application	NOUN
ajst-16340	87	20	.	.	PUNCT
ajst-16340	88	1	in	in	ADP
ajst-16340	88	2	the	the	DET
ajst-16340	88	3	field	field	NOUN
ajst-16340	88	4	of	of	ADP
ajst-16340	88	5	deep	deep	ADJ
ajst-16340	88	6	learning	learning	NOUN
ajst-16340	88	7	,	,	PUNCT
ajst-16340	88	8	mini	mini	ADJ
ajst-16340	88	9	-	-	NOUN
ajst-16340	88	10	batch	batch	ADJ
ajst-16340	88	11	gradient	gradient	ADJ
ajst-16340	88	12	descent	descent	NOUN
ajst-16340	88	13	is	be	AUX
ajst-16340	88	14	a	a	DET
ajst-16340	88	15	common	common	ADJ
ajst-16340	88	16	and	and	CCONJ
ajst-16340	88	17	effective	effective	ADJ
ajst-16340	88	18	optimization	optimization	NOUN
ajst-16340	88	19	method	method	NOUN
ajst-16340	88	20	,	,	PUNCT
ajst-16340	88	21	often	often	ADV
ajst-16340	88	22	leading	lead	VERB
ajst-16340	88	23	to	to	ADP
ajst-16340	88	24	good	good	ADJ
ajst-16340	88	25	training	training	NOUN
ajst-16340	88	26	results	result	NOUN
ajst-16340	88	27	by	by	ADP
ajst-16340	88	28	combining	combine	VERB
ajst-16340	88	29	the	the	DET
ajst-16340	88	30	advantages	advantage	NOUN
ajst-16340	88	31	of	of	ADP
ajst-16340	88	32	the	the	DET
ajst-16340	88	33	previous	previous	ADJ
ajst-16340	88	34	two	two	NUM
ajst-16340	88	35	approaches	approach	NOUN
ajst-16340	88	36	.	.	PUNCT
ajst-16340	89	1	2.5	2.5	NUM
ajst-16340	89	2	.	.	PUNCT
ajst-16340	90	1	applications	application	NOUN
ajst-16340	90	2	of	of	ADP
ajst-16340	90	3	bp	bp	PROPN
ajst-16340	90	4	neural	neural	PROPN
ajst-16340	90	5	networks	network	NOUN
ajst-16340	90	6	bp	bp	PROPN
ajst-16340	90	7	neural	neural	PROPN
ajst-16340	90	8	networks	network	NOUN
ajst-16340	90	9	find	find	VERB
ajst-16340	90	10	wide	wide	ADJ
ajst-16340	90	11	applications	application	NOUN
ajst-16340	90	12	in	in	ADP
ajst-16340	90	13	various	various	ADJ
ajst-16340	90	14	fields	field	NOUN
ajst-16340	90	15	,	,	PUNCT
ajst-16340	90	16	including	include	VERB
ajst-16340	90	17	image	image	NOUN
ajst-16340	90	18	recognition	recognition	NOUN
ajst-16340	90	19	,	,	PUNCT
ajst-16340	90	20	speech	speech	NOUN
ajst-16340	90	21	processing	processing	NOUN
ajst-16340	90	22	,	,	PUNCT
ajst-16340	90	23	natural	natural	ADJ
ajst-16340	90	24	language	language	NOUN
ajst-16340	90	25	processing	processing	NOUN
ajst-16340	90	26	,	,	PUNCT
ajst-16340	90	27	and	and	CCONJ
ajst-16340	90	28	more	more	ADJ
ajst-16340	90	29	.	.	PUNCT
ajst-16340	91	1	their	their	PRON
ajst-16340	91	2	flexibility	flexibility	NOUN
ajst-16340	91	3	and	and	CCONJ
ajst-16340	91	4	powerful	powerful	ADJ
ajst-16340	91	5	fitting	fitting	ADJ
ajst-16340	91	6	capabilities	capability	NOUN
ajst-16340	91	7	make	make	VERB
ajst-16340	91	8	them	they	PRON
ajst-16340	91	9	essential	essential	ADJ
ajst-16340	91	10	tools	tool	NOUN
ajst-16340	91	11	for	for	ADP
ajst-16340	91	12	solving	solve	VERB
ajst-16340	91	13	complex	complex	ADJ
ajst-16340	91	14	problems	problem	NOUN
ajst-16340	91	15	.	.	PUNCT
ajst-16340	92	1	3	3	X
ajst-16340	92	2	.	.	X
ajst-16340	92	3	applications	application	NOUN
ajst-16340	92	4	and	and	CCONJ
ajst-16340	92	5	performance	performance	NOUN
ajst-16340	92	6	optimization	optimization	NOUN
ajst-16340	92	7	of	of	ADP
ajst-16340	92	8	bp	bp	PROPN
ajst-16340	92	9	neural	neural	PROPN
ajst-16340	92	10	networks	network	NOUN
ajst-16340	92	11	3.1	3.1	NUM
ajst-16340	92	12	.	.	PUNCT
ajst-16340	92	13	image	image	NOUN
ajst-16340	92	14	recognition	recognition	NOUN
ajst-16340	92	15	and	and	CCONJ
ajst-16340	92	16	classification	classification	NOUN
ajst-16340	92	17	in	in	ADP
ajst-16340	92	18	the	the	DET
ajst-16340	92	19	field	field	NOUN
ajst-16340	92	20	of	of	ADP
ajst-16340	92	21	image	image	NOUN
ajst-16340	92	22	recognition	recognition	NOUN
ajst-16340	92	23	,	,	PUNCT
ajst-16340	92	24	bp	bp	PROPN
ajst-16340	92	25	neural	neural	PROPN
ajst-16340	92	26	networks	network	NOUN
ajst-16340	92	27	have	have	AUX
ajst-16340	92	28	achieved	achieve	VERB
ajst-16340	92	29	significant	significant	ADJ
ajst-16340	92	30	success	success	NOUN
ajst-16340	92	31	.	.	PUNCT
ajst-16340	93	1	the	the	DET
ajst-16340	93	2	introduction	introduction	NOUN
ajst-16340	93	3	of	of	ADP
ajst-16340	93	4	convolutional	convolutional	ADJ
ajst-16340	93	5	neural	neural	ADJ
ajst-16340	93	6	networks	network	NOUN
ajst-16340	93	7	(	(	PUNCT
ajst-16340	93	8	cnns	cnns	PROPN
ajst-16340	93	9	)	)	PUNCT
ajst-16340	93	10	enables	enable	VERB
ajst-16340	93	11	the	the	DET
ajst-16340	93	12	network	network	NOUN
ajst-16340	93	13	to	to	PART
ajst-16340	93	14	effectively	effectively	ADV
ajst-16340	93	15	capture	capture	VERB
ajst-16340	93	16	spatial	spatial	ADJ
ajst-16340	93	17	features	feature	NOUN
ajst-16340	93	18	in	in	ADP
ajst-16340	93	19	images	image	NOUN
ajst-16340	93	20	,	,	PUNCT
ajst-16340	93	21	leading	lead	VERB
ajst-16340	93	22	to	to	ADP
ajst-16340	93	23	excellent	excellent	ADJ
ajst-16340	93	24	performance	performance	NOUN
ajst-16340	93	25	in	in	ADP
ajst-16340	93	26	tasks	task	NOUN
ajst-16340	93	27	such	such	ADJ
ajst-16340	93	28	as	as	ADP
ajst-16340	93	29	image	image	NOUN
ajst-16340	93	30	classification	classification	NOUN
ajst-16340	93	31	and	and	CCONJ
ajst-16340	93	32	object	object	NOUN
ajst-16340	93	33	detection	detection	NOUN
ajst-16340	93	34	.	.	PUNCT
ajst-16340	94	1	typical	typical	ADJ
ajst-16340	94	2	applications	application	NOUN
ajst-16340	94	3	in	in	ADP
ajst-16340	94	4	image	image	NOUN
ajst-16340	94	5	recognition	recognition	NOUN
ajst-16340	94	6	include	include	VERB
ajst-16340	94	7	face	face	NOUN
ajst-16340	94	8	recognition	recognition	NOUN
ajst-16340	94	9	,	,	PUNCT
ajst-16340	94	10	object	object	NOUN
ajst-16340	94	11	recognition	recognition	NOUN
ajst-16340	94	12	,	,	PUNCT
ajst-16340	94	13	and	and	CCONJ
ajst-16340	94	14	the	the	DET
ajst-16340	94	15	hierarchical	hierarchical	ADJ
ajst-16340	94	16	structure	structure	NOUN
ajst-16340	94	17	of	of	ADP
ajst-16340	94	18	deep	deep	ADJ
ajst-16340	94	19	neural	neural	ADJ
ajst-16340	94	20	networks	network	NOUN
ajst-16340	94	21	automatically	automatically	ADV
ajst-16340	94	22	learns	learn	VERB
ajst-16340	94	23	abstract	abstract	ADJ
ajst-16340	94	24	features	feature	NOUN
ajst-16340	94	25	from	from	ADP
ajst-16340	94	26	images	image	NOUN
ajst-16340	94	27	,	,	PUNCT
ajst-16340	94	28	thereby	thereby	ADV
ajst-16340	94	29	improving	improve	VERB
ajst-16340	94	30	classification	classification	NOUN
ajst-16340	94	31	accuracy	accuracy	NOUN
ajst-16340	94	32	.	.	PUNCT
ajst-16340	95	1	3.2	3.2	NUM
ajst-16340	95	2	.	.	PUNCT
ajst-16340	95	3	speech	speech	NOUN
ajst-16340	95	4	processing	processing	NOUN
ajst-16340	95	5	and	and	CCONJ
ajst-16340	95	6	speech	speech	NOUN
ajst-16340	95	7	recognition	recognition	NOUN
ajst-16340	95	8	bp	bp	PROPN
ajst-16340	95	9	neural	neural	PROPN
ajst-16340	95	10	networks	network	NOUN
ajst-16340	95	11	play	play	VERB
ajst-16340	95	12	a	a	DET
ajst-16340	95	13	crucial	crucial	ADJ
ajst-16340	95	14	role	role	NOUN
ajst-16340	95	15	in	in	ADP
ajst-16340	95	16	the	the	DET
ajst-16340	95	17	field	field	NOUN
ajst-16340	95	18	of	of	ADP
ajst-16340	95	19	speech	speech	NOUN
ajst-16340	95	20	processing	processing	NOUN
ajst-16340	95	21	.	.	PUNCT
ajst-16340	96	1	the	the	DET
ajst-16340	96	2	temporal	temporal	ADJ
ajst-16340	96	3	nature	nature	NOUN
ajst-16340	96	4	and	and	CCONJ
ajst-16340	96	5	complexity	complexity	NOUN
ajst-16340	96	6	of	of	ADP
ajst-16340	96	7	speech	speech	NOUN
ajst-16340	96	8	signals	signal	NOUN
ajst-16340	96	9	make	make	VERB
ajst-16340	96	10	traditional	traditional	ADJ
ajst-16340	96	11	methods	method	NOUN
ajst-16340	96	12	challenging	challenge	VERB
ajst-16340	96	13	,	,	PUNCT
ajst-16340	96	14	but	but	CCONJ
ajst-16340	96	15	bp	bp	PROPN
ajst-16340	96	16	neural	neural	PROPN
ajst-16340	96	17	networks	network	NOUN
ajst-16340	96	18	,	,	PUNCT
ajst-16340	96	19	especially	especially	ADV
ajst-16340	96	20	those	those	PRON
ajst-16340	96	21	with	with	ADP
ajst-16340	96	22	long	long	ADJ
ajst-16340	96	23	short	short	ADJ
ajst-16340	96	24	-	-	PUNCT
ajst-16340	96	25	term	term	NOUN
ajst-16340	96	26	memory	memory	NOUN
ajst-16340	96	27	(	(	PUNCT
ajst-16340	96	28	lstm	lstm	ADJ
ajst-16340	96	29	)	)	PUNCT
ajst-16340	96	30	structures	structure	NOUN
ajst-16340	96	31	,	,	PUNCT
ajst-16340	96	32	can	can	AUX
ajst-16340	96	33	better	well	ADV
ajst-16340	96	34	capture	capture	VERB
ajst-16340	96	35	the	the	DET
ajst-16340	96	36	temporal	temporal	ADJ
ajst-16340	96	37	information	information	NOUN
ajst-16340	96	38	in	in	ADP
ajst-16340	96	39	speech	speech	NOUN
ajst-16340	96	40	.	.	PUNCT
ajst-16340	97	1	they	they	PRON
ajst-16340	97	2	are	be	AUX
ajst-16340	97	3	widely	widely	ADV
ajst-16340	97	4	used	use	VERB
ajst-16340	97	5	in	in	ADP
ajst-16340	97	6	tasks	task	NOUN
ajst-16340	97	7	such	such	ADJ
ajst-16340	97	8	as	as	ADP
ajst-16340	97	9	speech	speech	NOUN
ajst-16340	97	10	recognition	recognition	NOUN
ajst-16340	97	11	and	and	CCONJ
ajst-16340	97	12	speaker	speaker	NOUN
ajst-16340	97	13	identification	identification	NOUN
ajst-16340	97	14	.	.	PUNCT
ajst-16340	98	1	their	their	PRON
ajst-16340	98	2	successful	successful	ADJ
ajst-16340	98	3	applications	application	NOUN
ajst-16340	98	4	have	have	VERB
ajst-16340	98	5	greatly	greatly	ADV
ajst-16340	98	6	advanced	advanced	ADJ
ajst-16340	98	7	speech	speech	NOUN
ajst-16340	98	8	processing	processing	NOUN
ajst-16340	98	9	technology	technology	NOUN
ajst-16340	98	10	in	in	ADP
ajst-16340	98	11	areas	area	NOUN
ajst-16340	98	12	such	such	ADJ
ajst-16340	98	13	as	as	ADP
ajst-16340	98	14	intelligent	intelligent	ADJ
ajst-16340	98	15	assistants	assistant	NOUN
ajst-16340	98	16	and	and	CCONJ
ajst-16340	98	17	voice	voice	NOUN
ajst-16340	98	18	search	search	NOUN
ajst-16340	98	19	.	.	PUNCT
ajst-16340	99	1	in	in	ADP
ajst-16340	99	2	speech	speech	NOUN
ajst-16340	99	3	recognition	recognition	NOUN
ajst-16340	99	4	tasks	task	NOUN
ajst-16340	99	5	,	,	PUNCT
ajst-16340	99	6	bp	bp	PROPN
ajst-16340	99	7	neural	neural	ADJ
ajst-16340	99	8	networks	network	NOUN
ajst-16340	99	9	can	can	AUX
ajst-16340	99	10	accurately	accurately	ADV
ajst-16340	99	11	identify	identify	VERB
ajst-16340	99	12	words	word	NOUN
ajst-16340	99	13	and	and	CCONJ
ajst-16340	99	14	speech	speech	NOUN
ajst-16340	99	15	features	feature	NOUN
ajst-16340	99	16	by	by	ADP
ajst-16340	99	17	learning	learn	VERB
ajst-16340	99	18	the	the	DET
ajst-16340	99	19	temporal	temporal	ADJ
ajst-16340	99	20	patterns	pattern	NOUN
ajst-16340	99	21	in	in	ADP
ajst-16340	99	22	speech	speech	NOUN
ajst-16340	99	23	signals	signal	NOUN
ajst-16340	99	24	.	.	PUNCT
ajst-16340	100	1	the	the	DET
ajst-16340	100	2	lstm	lstm	PROPN
ajst-16340	100	3	structure	structure	NOUN
ajst-16340	100	4	enables	enable	VERB
ajst-16340	100	5	the	the	DET
ajst-16340	100	6	network	network	NOUN
ajst-16340	100	7	to	to	PART
ajst-16340	100	8	handle	handle	VERB
ajst-16340	100	9	long	long	ADJ
ajst-16340	100	10	-	-	PUNCT
ajst-16340	100	11	term	term	NOUN
ajst-16340	100	12	dependencies	dependency	NOUN
ajst-16340	100	13	in	in	ADP
ajst-16340	100	14	speech	speech	NOUN
ajst-16340	100	15	signals	signal	NOUN
ajst-16340	100	16	,	,	PUNCT
ajst-16340	100	17	effectively	effectively	ADV
ajst-16340	100	18	capturing	capture	VERB
ajst-16340	100	19	contextual	contextual	ADJ
ajst-16340	100	20	information	information	NOUN
ajst-16340	100	21	in	in	ADP
ajst-16340	100	22	speech	speech	NOUN
ajst-16340	100	23	signals	signal	NOUN
ajst-16340	100	24	.	.	PUNCT
ajst-16340	101	1	for	for	ADP
ajst-16340	101	2	speaker	speaker	NOUN
ajst-16340	101	3	identification	identification	NOUN
ajst-16340	101	4	,	,	PUNCT
ajst-16340	101	5	bp	bp	PROPN
ajst-16340	101	6	neural	neural	PROPN
ajst-16340	101	7	networks	network	NOUN
ajst-16340	101	8	model	model	VERB
ajst-16340	101	9	the	the	DET
ajst-16340	101	10	speech	speech	NOUN
ajst-16340	101	11	features	feature	NOUN
ajst-16340	101	12	of	of	ADP
ajst-16340	101	13	speakers	speaker	NOUN
ajst-16340	101	14	and	and	CCONJ
ajst-16340	101	15	distinguish	distinguish	VERB
ajst-16340	101	16	between	between	ADP
ajst-16340	101	17	different	different	ADJ
ajst-16340	101	18	speakers	speaker	NOUN
ajst-16340	101	19	through	through	ADP
ajst-16340	101	20	weight	weight	NOUN
ajst-16340	101	21	adjustments	adjustment	NOUN
ajst-16340	101	22	during	during	ADP
ajst-16340	101	23	the	the	DET
ajst-16340	101	24	learning	learning	NOUN
ajst-16340	101	25	process	process	NOUN
ajst-16340	101	26	.	.	PUNCT
ajst-16340	102	1	this	this	PRON
ajst-16340	102	2	is	be	AUX
ajst-16340	102	3	valuable	valuable	ADJ
ajst-16340	102	4	in	in	ADP
ajst-16340	102	5	applications	application	NOUN
ajst-16340	102	6	such	such	ADJ
ajst-16340	102	7	as	as	ADP
ajst-16340	102	8	voice	voice	NOUN
ajst-16340	102	9	assistants	assistant	NOUN
ajst-16340	102	10	,	,	PUNCT
ajst-16340	102	11	voice	voice	NOUN
ajst-16340	102	12	search	search	NOUN
ajst-16340	102	13	,	,	PUNCT
ajst-16340	102	14	and	and	CCONJ
ajst-16340	102	15	secure	secure	ADJ
ajst-16340	102	16	authentication	authentication	NOUN
ajst-16340	102	17	.	.	PUNCT
ajst-16340	103	1	these	these	DET
ajst-16340	103	2	successful	successful	ADJ
ajst-16340	103	3	applications	application	NOUN
ajst-16340	103	4	not	not	PART
ajst-16340	103	5	only	only	ADV
ajst-16340	103	6	drive	drive	VERB
ajst-16340	103	7	the	the	DET
ajst-16340	103	8	development	development	NOUN
ajst-16340	103	9	of	of	ADP
ajst-16340	103	10	speech	speech	NOUN
ajst-16340	103	11	processing	processing	NOUN
ajst-16340	103	12	technology	technology	NOUN
ajst-16340	103	13	but	but	CCONJ
ajst-16340	103	14	also	also	ADV
ajst-16340	103	15	provide	provide	VERB
ajst-16340	103	16	robust	robust	ADJ
ajst-16340	103	17	support	support	NOUN
ajst-16340	103	18	for	for	ADP
ajst-16340	103	19	the	the	DET
ajst-16340	103	20	practical	practical	ADJ
ajst-16340	103	21	application	application	NOUN
ajst-16340	103	22	of	of	ADP
ajst-16340	103	23	intelligent	intelligent	ADJ
ajst-16340	103	24	assistants	assistant	NOUN
ajst-16340	103	25	.	.	PUNCT
ajst-16340	104	1	through	through	ADP
ajst-16340	104	2	bp	bp	PROPN
ajst-16340	104	3	neural	neural	PROPN
ajst-16340	104	4	networks	network	NOUN
ajst-16340	104	5	,	,	PUNCT
ajst-16340	104	6	advancements	advancement	NOUN
ajst-16340	104	7	have	have	AUX
ajst-16340	104	8	been	be	AUX
ajst-16340	104	9	made	make	VERB
ajst-16340	104	10	in	in	ADP
ajst-16340	104	11	speech	speech	NOUN
ajst-16340	104	12	interaction	interaction	NOUN
ajst-16340	104	13	technology	technology	NOUN
ajst-16340	104	14	,	,	PUNCT
ajst-16340	104	15	including	include	VERB
ajst-16340	104	16	speech	speech	NOUN
ajst-16340	104	17	command	command	NOUN
ajst-16340	104	18	recognition	recognition	NOUN
ajst-16340	104	19	and	and	CCONJ
ajst-16340	104	20	speech	speech	NOUN
ajst-16340	104	21	synthesis	synthesis	NOUN
ajst-16340	104	22	,	,	PUNCT
ajst-16340	104	23	enabling	enable	VERB
ajst-16340	104	24	users	user	NOUN
ajst-16340	104	25	to	to	PART
ajst-16340	104	26	engage	engage	VERB
ajst-16340	104	27	more	more	ADV
ajst-16340	104	28	naturally	naturally	ADV
ajst-16340	104	29	and	and	CCONJ
ajst-16340	104	30	conveniently	conveniently	ADV
ajst-16340	104	31	with	with	ADP
ajst-16340	104	32	smart	smart	ADJ
ajst-16340	104	33	devices	device	NOUN
ajst-16340	104	34	.	.	PUNCT
ajst-16340	105	1	3.3	3.3	NUM
ajst-16340	105	2	.	.	PUNCT
ajst-16340	105	3	natural	natural	ADJ
ajst-16340	105	4	language	language	NOUN
ajst-16340	105	5	processing	processing	NOUN
ajst-16340	105	6	in	in	ADP
ajst-16340	105	7	the	the	DET
ajst-16340	105	8	field	field	NOUN
ajst-16340	105	9	of	of	ADP
ajst-16340	105	10	natural	natural	ADJ
ajst-16340	105	11	language	language	NOUN
ajst-16340	105	12	processing	processing	NOUN
ajst-16340	105	13	(	(	PUNCT
ajst-16340	105	14	nlp	nlp	NOUN
ajst-16340	105	15	)	)	PUNCT
ajst-16340	105	16	,	,	PUNCT
ajst-16340	105	17	bp	bp	PROPN
ajst-16340	105	18	neural	neural	PROPN
ajst-16340	105	19	networks	network	NOUN
ajst-16340	105	20	have	have	AUX
ajst-16340	105	21	made	make	VERB
ajst-16340	105	22	significant	significant	ADJ
ajst-16340	105	23	progress	progress	NOUN
ajst-16340	105	24	.	.	PUNCT
ajst-16340	106	1	the	the	DET
ajst-16340	106	2	introduction	introduction	NOUN
ajst-16340	106	3	of	of	ADP
ajst-16340	106	4	structures	structure	NOUN
ajst-16340	106	5	like	like	ADP
ajst-16340	106	6	recurrent	recurrent	ADJ
ajst-16340	106	7	neural	neural	ADJ
ajst-16340	106	8	networks	network	NOUN
ajst-16340	106	9	(	(	PUNCT
ajst-16340	106	10	rnns	rnns	PROPN
ajst-16340	106	11	)	)	PUNCT
ajst-16340	106	12	enables	enable	VERB
ajst-16340	106	13	networks	network	NOUN
ajst-16340	106	14	to	to	PART
ajst-16340	106	15	process	process	VERB
ajst-16340	106	16	text	text	NOUN
ajst-16340	106	17	data	datum	NOUN
ajst-16340	106	18	more	more	ADV
ajst-16340	106	19	efficiently	efficiently	ADV
ajst-16340	106	20	,	,	PUNCT
ajst-16340	106	21	achieving	achieve	VERB
ajst-16340	106	22	success	success	NOUN
ajst-16340	106	23	in	in	ADP
ajst-16340	106	24	tasks	task	NOUN
ajst-16340	106	25	such	such	ADJ
ajst-16340	106	26	as	as	ADP
ajst-16340	106	27	sentiment	sentiment	NOUN
ajst-16340	106	28	analysis	analysis	NOUN
ajst-16340	106	29	,	,	PUNCT
ajst-16340	106	30	text	text	NOUN
ajst-16340	106	31	generation	generation	NOUN
ajst-16340	106	32	,	,	PUNCT
ajst-16340	106	33	and	and	CCONJ
ajst-16340	106	34	machine	machine	NOUN
ajst-16340	106	35	translation	translation	NOUN
ajst-16340	106	36	.	.	PUNCT
ajst-16340	107	1	the	the	DET
ajst-16340	107	2	successful	successful	ADJ
ajst-16340	107	3	application	application	NOUN
ajst-16340	107	4	of	of	ADP
ajst-16340	107	5	deep	deep	ADJ
ajst-16340	107	6	learning	learning	NOUN
ajst-16340	107	7	models	model	NOUN
ajst-16340	107	8	in	in	ADP
ajst-16340	107	9	the	the	DET
ajst-16340	107	10	nlp	nlp	NOUN
ajst-16340	107	11	domain	domain	NOUN
ajst-16340	107	12	has	have	AUX
ajst-16340	107	13	significantly	significantly	ADV
ajst-16340	107	14	improved	improve	VERB
ajst-16340	107	15	the	the	DET
ajst-16340	107	16	efficiency	efficiency	NOUN
ajst-16340	107	17	and	and	CCONJ
ajst-16340	107	18	accuracy	accuracy	NOUN
ajst-16340	107	19	of	of	ADP
ajst-16340	107	20	automatically	automatically	ADV
ajst-16340	107	21	processing	process	VERB
ajst-16340	107	22	textual	textual	ADJ
ajst-16340	107	23	information	information	NOUN
ajst-16340	107	24	.	.	PUNCT
ajst-16340	108	1	in	in	ADP
ajst-16340	108	2	sentiment	sentiment	NOUN
ajst-16340	108	3	analysis	analysis	NOUN
ajst-16340	108	4	tasks	task	NOUN
ajst-16340	108	5	,	,	PUNCT
ajst-16340	108	6	bp	bp	PROPN
ajst-16340	108	7	neural	neural	ADJ
ajst-16340	108	8	networks	network	NOUN
ajst-16340	108	9	,	,	PUNCT
ajst-16340	108	10	by	by	ADP
ajst-16340	108	11	learning	learn	VERB
ajst-16340	108	12	the	the	DET
ajst-16340	108	13	semantic	semantic	ADJ
ajst-16340	108	14	and	and	CCONJ
ajst-16340	108	15	emotional	emotional	ADJ
ajst-16340	108	16	information	information	NOUN
ajst-16340	108	17	in	in	ADP
ajst-16340	108	18	textual	textual	ADJ
ajst-16340	108	19	data	datum	NOUN
ajst-16340	108	20	,	,	PUNCT
ajst-16340	108	21	can	can	AUX
ajst-16340	108	22	accurately	accurately	ADV
ajst-16340	108	23	determine	determine	VERB
ajst-16340	108	24	the	the	DET
ajst-16340	108	25	sentiment	sentiment	NOUN
ajst-16340	108	26	tendency	tendency	NOUN
ajst-16340	108	27	of	of	ADP
ajst-16340	108	28	the	the	DET
ajst-16340	108	29	text	text	NOUN
ajst-16340	108	30	.	.	PUNCT
ajst-16340	109	1	this	this	PRON
ajst-16340	109	2	provides	provide	VERB
ajst-16340	109	3	a	a	DET
ajst-16340	109	4	reliable	reliable	ADJ
ajst-16340	109	5	solution	solution	NOUN
ajst-16340	109	6	for	for	ADP
ajst-16340	109	7	applications	application	NOUN
ajst-16340	109	8	such	such	ADJ
ajst-16340	109	9	as	as	ADP
ajst-16340	109	10	social	social	ADJ
ajst-16340	109	11	media	medium	NOUN
ajst-16340	109	12	sentiment	sentiment	NOUN
ajst-16340	109	13	analysis	analysis	NOUN
ajst-16340	109	14	and	and	CCONJ
ajst-16340	109	15	sentiment	sentiment	NOUN
ajst-16340	109	16	evaluation	evaluation	NOUN
ajst-16340	109	17	in	in	ADP
ajst-16340	109	18	product	product	NOUN
ajst-16340	109	19	reviews	review	NOUN
ajst-16340	109	20	.	.	PUNCT
ajst-16340	110	1	in	in	ADP
ajst-16340	110	2	text	text	NOUN
ajst-16340	110	3	generation	generation	NOUN
ajst-16340	110	4	,	,	PUNCT
ajst-16340	110	5	bp	bp	PROPN
ajst-16340	110	6	neural	neural	PROPN
ajst-16340	110	7	networks	network	NOUN
ajst-16340	110	8	,	,	PUNCT
ajst-16340	110	9	through	through	ADP
ajst-16340	110	10	learning	learn	VERB
ajst-16340	110	11	the	the	DET
ajst-16340	110	12	language	language	NOUN
ajst-16340	110	13	patterns	pattern	NOUN
ajst-16340	110	14	of	of	ADP
ajst-16340	110	15	large	large	ADJ
ajst-16340	110	16	amounts	amount	NOUN
ajst-16340	110	17	of	of	ADP
ajst-16340	110	18	textual	textual	ADJ
ajst-16340	110	19	data	datum	NOUN
ajst-16340	110	20	,	,	PUNCT
ajst-16340	110	21	can	can	AUX
ajst-16340	110	22	generate	generate	VERB
ajst-16340	110	23	text	text	NOUN
ajst-16340	110	24	content	content	NOUN
ajst-16340	110	25	with	with	ADP
ajst-16340	110	26	a	a	DET
ajst-16340	110	27	certain	certain	ADJ
ajst-16340	110	28	semantic	semantic	ADJ
ajst-16340	110	29	structure	structure	NOUN
ajst-16340	110	30	.	.	PUNCT
ajst-16340	111	1	this	this	PRON
ajst-16340	111	2	finds	find	VERB
ajst-16340	111	3	widespread	widespread	ADJ
ajst-16340	111	4	applications	application	NOUN
ajst-16340	111	5	in	in	ADP
ajst-16340	111	6	automatic	automatic	ADJ
ajst-16340	111	7	text	text	NOUN
ajst-16340	111	8	summarization	summarization	NOUN
ajst-16340	111	9	and	and	CCONJ
ajst-16340	111	10	dialogue	dialogue	NOUN
ajst-16340	111	11	systems	system	NOUN
ajst-16340	111	12	.	.	PUNCT
ajst-16340	112	1	in	in	ADP
ajst-16340	112	2	machine	machine	NOUN
ajst-16340	112	3	translation	translation	NOUN
ajst-16340	112	4	tasks	task	NOUN
ajst-16340	112	5	,	,	PUNCT
ajst-16340	112	6	bp	bp	PROPN
ajst-16340	112	7	neural	neural	ADJ
ajst-16340	112	8	networks	network	NOUN
ajst-16340	112	9	,	,	PUNCT
ajst-16340	112	10	by	by	ADP
ajst-16340	112	11	learning	learn	VERB
ajst-16340	112	12	the	the	DET
ajst-16340	112	13	correspondence	correspondence	NOUN
ajst-16340	112	14	between	between	ADP
ajst-16340	112	15	different	different	ADJ
ajst-16340	112	16	languages	language	NOUN
ajst-16340	112	17	,	,	PUNCT
ajst-16340	112	18	can	can	AUX
ajst-16340	112	19	achieve	achieve	VERB
ajst-16340	112	20	high	high	ADJ
ajst-16340	112	21	-	-	PUNCT
ajst-16340	112	22	quality	quality	NOUN
ajst-16340	112	23	text	text	NOUN
ajst-16340	112	24	translation	translation	NOUN
ajst-16340	112	25	.	.	PUNCT
ajst-16340	113	1	their	their	PRON
ajst-16340	113	2	role	role	NOUN
ajst-16340	113	3	in	in	ADP
ajst-16340	113	4	multilingual	multilingual	ADJ
ajst-16340	113	5	communication	communication	NOUN
ajst-16340	113	6	and	and	CCONJ
ajst-16340	113	7	international	international	ADJ
ajst-16340	113	8	collaboration	collaboration	NOUN
ajst-16340	113	9	is	be	AUX
ajst-16340	113	10	crucial	crucial	ADJ
ajst-16340	113	11	.	.	PUNCT
ajst-16340	114	1	these	these	DET
ajst-16340	114	2	successful	successful	ADJ
ajst-16340	114	3	applications	application	NOUN
ajst-16340	114	4	not	not	PART
ajst-16340	114	5	only	only	ADV
ajst-16340	114	6	elevate	elevate	VERB
ajst-16340	114	7	the	the	DET
ajst-16340	114	8	level	level	NOUN
ajst-16340	114	9	of	of	ADP
ajst-16340	114	10	nlp	nlp	NOUN
ajst-16340	114	11	technology	technology	NOUN
ajst-16340	114	12	but	but	CCONJ
ajst-16340	114	13	also	also	ADV
ajst-16340	114	14	provide	provide	VERB
ajst-16340	114	15	robust	robust	ADJ
ajst-16340	114	16	support	support	NOUN
ajst-16340	114	17	for	for	ADP
ajst-16340	114	18	the	the	DET
ajst-16340	114	19	practical	practical	ADJ
ajst-16340	114	20	application	application	NOUN
ajst-16340	114	21	of	of	ADP
ajst-16340	114	22	text	text	NOUN
ajst-16340	114	23	information	information	NOUN
ajst-16340	114	24	processing	processing	NOUN
ajst-16340	114	25	.	.	PUNCT
ajst-16340	115	1	through	through	ADP
ajst-16340	115	2	153	153	NUM
ajst-16340	115	3	bp	bp	PROPN
ajst-16340	115	4	neural	neural	ADJ
ajst-16340	115	5	networks	network	NOUN
ajst-16340	115	6	,	,	PUNCT
ajst-16340	115	7	the	the	DET
ajst-16340	115	8	nlp	nlp	NOUN
ajst-16340	115	9	field	field	NOUN
ajst-16340	115	10	has	have	AUX
ajst-16340	115	11	made	make	VERB
ajst-16340	115	12	significant	significant	ADJ
ajst-16340	115	13	progress	progress	NOUN
ajst-16340	115	14	in	in	ADP
ajst-16340	115	15	sentiment	sentiment	NOUN
ajst-16340	115	16	analysis	analysis	NOUN
ajst-16340	115	17	,	,	PUNCT
ajst-16340	115	18	text	text	NOUN
ajst-16340	115	19	generation	generation	NOUN
ajst-16340	115	20	,	,	PUNCT
ajst-16340	115	21	and	and	CCONJ
ajst-16340	115	22	machine	machine	NOUN
ajst-16340	115	23	translation	translation	NOUN
ajst-16340	115	24	,	,	PUNCT
ajst-16340	115	25	laying	lay	VERB
ajst-16340	115	26	the	the	DET
ajst-16340	115	27	foundation	foundation	NOUN
ajst-16340	115	28	for	for	ADP
ajst-16340	115	29	more	more	ADV
ajst-16340	115	30	intelligent	intelligent	ADJ
ajst-16340	115	31	and	and	CCONJ
ajst-16340	115	32	efficient	efficient	ADJ
ajst-16340	115	33	text	text	NOUN
ajst-16340	115	34	processing	processing	NOUN
ajst-16340	115	35	.	.	PUNCT
ajst-16340	116	1	3.4	3.4	NUM
ajst-16340	116	2	.	.	PUNCT
ajst-16340	117	1	sequence	sequence	NOUN
ajst-16340	117	2	data	datum	NOUN
ajst-16340	117	3	analysis	analysis	NOUN
ajst-16340	117	4	bp	bp	PROPN
ajst-16340	117	5	neural	neural	PROPN
ajst-16340	117	6	networks	network	NOUN
ajst-16340	117	7	demonstrate	demonstrate	VERB
ajst-16340	117	8	distinct	distinct	ADJ
ajst-16340	117	9	advantages	advantage	NOUN
ajst-16340	117	10	in	in	ADP
ajst-16340	117	11	handling	handle	VERB
ajst-16340	117	12	sequential	sequential	ADJ
ajst-16340	117	13	data	datum	NOUN
ajst-16340	117	14	.	.	PUNCT
ajst-16340	118	1	in	in	ADP
ajst-16340	118	2	the	the	DET
ajst-16340	118	3	financial	financial	ADJ
ajst-16340	118	4	sector	sector	NOUN
ajst-16340	118	5	,	,	PUNCT
ajst-16340	118	6	these	these	DET
ajst-16340	118	7	networks	network	NOUN
ajst-16340	118	8	find	find	VERB
ajst-16340	118	9	extensive	extensive	ADJ
ajst-16340	118	10	applications	application	NOUN
ajst-16340	118	11	in	in	ADP
ajst-16340	118	12	tasks	task	NOUN
ajst-16340	118	13	like	like	ADP
ajst-16340	118	14	predicting	predict	VERB
ajst-16340	118	15	stock	stock	NOUN
ajst-16340	118	16	prices	price	NOUN
ajst-16340	118	17	and	and	CCONJ
ajst-16340	118	18	optimizing	optimize	VERB
ajst-16340	118	19	trading	trading	NOUN
ajst-16340	118	20	strategies	strategy	NOUN
ajst-16340	118	21	.	.	PUNCT
ajst-16340	119	1	by	by	ADP
ajst-16340	119	2	learning	learn	VERB
ajst-16340	119	3	from	from	ADP
ajst-16340	119	4	historical	historical	ADJ
ajst-16340	119	5	market	market	NOUN
ajst-16340	119	6	data	datum	NOUN
ajst-16340	119	7	,	,	PUNCT
ajst-16340	119	8	bp	bp	PROPN
ajst-16340	119	9	neural	neural	ADJ
ajst-16340	119	10	networks	network	NOUN
ajst-16340	119	11	can	can	AUX
ajst-16340	119	12	capture	capture	VERB
ajst-16340	119	13	the	the	DET
ajst-16340	119	14	changing	change	VERB
ajst-16340	119	15	trends	trend	NOUN
ajst-16340	119	16	in	in	ADP
ajst-16340	119	17	stock	stock	NOUN
ajst-16340	119	18	prices	price	NOUN
ajst-16340	119	19	,	,	PUNCT
ajst-16340	119	20	providing	provide	VERB
ajst-16340	119	21	robust	robust	ADJ
ajst-16340	119	22	support	support	NOUN
ajst-16340	119	23	for	for	ADP
ajst-16340	119	24	investment	investment	NOUN
ajst-16340	119	25	decisions	decision	NOUN
ajst-16340	119	26	.	.	PUNCT
ajst-16340	120	1	in	in	ADP
ajst-16340	120	2	meteorology	meteorology	NOUN
ajst-16340	120	3	,	,	PUNCT
ajst-16340	120	4	bp	bp	PROPN
ajst-16340	120	5	neural	neural	PROPN
ajst-16340	120	6	networks	network	NOUN
ajst-16340	120	7	enhance	enhance	VERB
ajst-16340	120	8	the	the	DET
ajst-16340	120	9	accuracy	accuracy	NOUN
ajst-16340	120	10	of	of	ADP
ajst-16340	120	11	predicting	predict	VERB
ajst-16340	120	12	future	future	ADJ
ajst-16340	120	13	climate	climate	NOUN
ajst-16340	120	14	changes	change	NOUN
ajst-16340	120	15	by	by	ADP
ajst-16340	120	16	assimilating	assimilate	VERB
ajst-16340	120	17	temporal	temporal	ADJ
ajst-16340	120	18	patterns	pattern	NOUN
ajst-16340	120	19	from	from	ADP
ajst-16340	120	20	meteorological	meteorological	ADJ
ajst-16340	120	21	data	datum	NOUN
ajst-16340	120	22	.	.	PUNCT
ajst-16340	121	1	this	this	DET
ajst-16340	121	2	application	application	NOUN
ajst-16340	121	3	makes	make	VERB
ajst-16340	121	4	weather	weather	NOUN
ajst-16340	121	5	forecasts	forecast	NOUN
ajst-16340	121	6	more	more	ADV
ajst-16340	121	7	reliable	reliable	ADJ
ajst-16340	121	8	,	,	PUNCT
ajst-16340	121	9	aiding	aid	VERB
ajst-16340	121	10	in	in	ADP
ajst-16340	121	11	addressing	address	VERB
ajst-16340	121	12	the	the	DET
ajst-16340	121	13	challenges	challenge	NOUN
ajst-16340	121	14	posed	pose	VERB
ajst-16340	121	15	by	by	ADP
ajst-16340	121	16	climate	climate	NOUN
ajst-16340	121	17	change	change	NOUN
ajst-16340	121	18	and	and	CCONJ
ajst-16340	121	19	offering	offer	VERB
ajst-16340	121	20	crucial	crucial	ADJ
ajst-16340	121	21	information	information	NOUN
ajst-16340	121	22	for	for	ADP
ajst-16340	121	23	decision	decision	NOUN
ajst-16340	121	24	-	-	PUNCT
ajst-16340	121	25	makers	maker	NOUN
ajst-16340	121	26	.	.	PUNCT
ajst-16340	122	1	3.5	3.5	NUM
ajst-16340	122	2	.	.	PUNCT
ajst-16340	122	3	performance	performance	NOUN
ajst-16340	122	4	evaluation	evaluation	NOUN
ajst-16340	122	5	and	and	CCONJ
ajst-16340	122	6	optimization	optimization	NOUN
ajst-16340	122	7	3.5.1	3.5.1	NUM
ajst-16340	122	8	.	.	PUNCT
ajst-16340	123	1	introduction	introduction	NOUN
ajst-16340	123	2	of	of	ADP
ajst-16340	123	3	optimization	optimization	NOUN
ajst-16340	123	4	algorithms	algorithm	NOUN
ajst-16340	123	5	to	to	PART
ajst-16340	123	6	enhance	enhance	VERB
ajst-16340	123	7	the	the	DET
ajst-16340	123	8	performance	performance	NOUN
ajst-16340	123	9	of	of	ADP
ajst-16340	123	10	bp	bp	PROPN
ajst-16340	123	11	neural	neural	ADJ
ajst-16340	123	12	networks	network	NOUN
ajst-16340	123	13	,	,	PUNCT
ajst-16340	123	14	researchers	researcher	NOUN
ajst-16340	123	15	have	have	AUX
ajst-16340	123	16	explored	explore	VERB
ajst-16340	123	17	various	various	ADJ
ajst-16340	123	18	optimization	optimization	NOUN
ajst-16340	123	19	methods	method	NOUN
ajst-16340	123	20	.	.	PUNCT
ajst-16340	124	1	in	in	ADP
ajst-16340	124	2	addition	addition	NOUN
ajst-16340	124	3	to	to	ADP
ajst-16340	124	4	improving	improve	VERB
ajst-16340	124	5	network	network	NOUN
ajst-16340	124	6	structures	structure	NOUN
ajst-16340	124	7	and	and	CCONJ
ajst-16340	124	8	adjusting	adjust	VERB
ajst-16340	124	9	hyperparameters	hyperparameter	NOUN
ajst-16340	124	10	,	,	PUNCT
ajst-16340	124	11	introducing	introduce	VERB
ajst-16340	124	12	different	different	ADJ
ajst-16340	124	13	optimization	optimization	NOUN
ajst-16340	124	14	algorithms	algorithm	NOUN
ajst-16340	124	15	has	have	AUX
ajst-16340	124	16	become	become	VERB
ajst-16340	124	17	a	a	DET
ajst-16340	124	18	key	key	ADJ
ajst-16340	124	19	means	mean	NOUN
ajst-16340	124	20	of	of	ADP
ajst-16340	124	21	improving	improve	VERB
ajst-16340	124	22	performance	performance	NOUN
ajst-16340	124	23	.	.	PUNCT
ajst-16340	125	1	some	some	DET
ajst-16340	125	2	common	common	ADJ
ajst-16340	125	3	optimization	optimization	NOUN
ajst-16340	125	4	algorithms	algorithm	NOUN
ajst-16340	125	5	include	include	VERB
ajst-16340	125	6	:	:	PUNCT
ajst-16340	125	7	3.5.1.1	3.5.1.1	NUM
ajst-16340	125	8	grey	grey	PROPN
ajst-16340	125	9	wolf	wolf	PROPN
ajst-16340	125	10	algorithm	algorithm	PROPN
ajst-16340	125	11	optimization	optimization	NOUN
ajst-16340	125	12	the	the	DET
ajst-16340	125	13	grey	grey	ADJ
ajst-16340	125	14	wolf	wolf	PROPN
ajst-16340	125	15	algorithm	algorithm	PROPN
ajst-16340	125	16	simulates	simulate	VERB
ajst-16340	125	17	the	the	DET
ajst-16340	125	18	hunting	hunt	VERB
ajst-16340	125	19	behavior	behavior	NOUN
ajst-16340	125	20	of	of	ADP
ajst-16340	125	21	grey	grey	ADJ
ajst-16340	125	22	wolves	wolf	NOUN
ajst-16340	125	23	and	and	CCONJ
ajst-16340	125	24	is	be	AUX
ajst-16340	125	25	introduced	introduce	VERB
ajst-16340	125	26	into	into	ADP
ajst-16340	125	27	the	the	DET
ajst-16340	125	28	optimization	optimization	NOUN
ajst-16340	125	29	process	process	NOUN
ajst-16340	125	30	of	of	ADP
ajst-16340	125	31	bp	bp	PROPN
ajst-16340	125	32	neural	neural	ADJ
ajst-16340	125	33	networks	network	NOUN
ajst-16340	125	34	.	.	PUNCT
ajst-16340	126	1	this	this	DET
ajst-16340	126	2	algorithm	algorithm	NOUN
ajst-16340	126	3	includes	include	VERB
ajst-16340	126	4	stages	stage	NOUN
ajst-16340	126	5	such	such	ADJ
ajst-16340	126	6	as	as	ADP
ajst-16340	126	7	searching	search	VERB
ajst-16340	126	8	for	for	ADP
ajst-16340	126	9	prey	prey	NOUN
ajst-16340	126	10	,	,	PUNCT
ajst-16340	126	11	chasing	chase	VERB
ajst-16340	126	12	and	and	CCONJ
ajst-16340	126	13	surrounding	surround	VERB
ajst-16340	126	14	prey	prey	NOUN
ajst-16340	126	15	until	until	SCONJ
ajst-16340	126	16	the	the	DET
ajst-16340	126	17	prey	prey	NOUN
ajst-16340	126	18	stops	stop	VERB
ajst-16340	126	19	fleeing	flee	VERB
ajst-16340	126	20	,	,	PUNCT
ajst-16340	126	21	and	and	CCONJ
ajst-16340	126	22	besieging	besiege	VERB
ajst-16340	126	23	prey	prey	NOUN
ajst-16340	126	24	.	.	PUNCT
ajst-16340	127	1	by	by	ADP
ajst-16340	127	2	applying	apply	VERB
ajst-16340	127	3	the	the	DET
ajst-16340	127	4	grey	grey	ADJ
ajst-16340	127	5	wolf	wolf	PROPN
ajst-16340	127	6	algorithm	algorithm	NOUN
ajst-16340	127	7	for	for	ADP
ajst-16340	127	8	optimization	optimization	NOUN
ajst-16340	127	9	,	,	PUNCT
ajst-16340	127	10	the	the	DET
ajst-16340	127	11	network	network	NOUN
ajst-16340	127	12	can	can	AUX
ajst-16340	127	13	converge	converge	VERB
ajst-16340	127	14	more	more	ADV
ajst-16340	127	15	effectively	effectively	ADV
ajst-16340	127	16	during	during	ADP
ajst-16340	127	17	training	training	NOUN
ajst-16340	127	18	,	,	PUNCT
ajst-16340	127	19	improving	improve	VERB
ajst-16340	127	20	learning	learning	NOUN
ajst-16340	127	21	efficiency	efficiency	NOUN
ajst-16340	127	22	.	.	PUNCT
ajst-16340	128	1	3.5.1.2	3.5.1.2	NUM
ajst-16340	128	2	genetic	genetic	ADJ
ajst-16340	128	3	algorithm	algorithm	NOUN
ajst-16340	128	4	optimization	optimization	NOUN
ajst-16340	128	5	genetic	genetic	ADJ
ajst-16340	128	6	algorithm[4	algorithm[4	PROPN
ajst-16340	128	7	]	]	PUNCT
ajst-16340	128	8	is	be	AUX
ajst-16340	128	9	an	an	DET
ajst-16340	128	10	optimization	optimization	NOUN
ajst-16340	128	11	algorithm	algorithm	NOUN
ajst-16340	128	12	that	that	PRON
ajst-16340	128	13	simulates	simulate	VERB
ajst-16340	128	14	the	the	DET
ajst-16340	128	15	biological	biological	ADJ
ajst-16340	128	16	evolution	evolution	NOUN
ajst-16340	128	17	process	process	NOUN
ajst-16340	128	18	and	and	CCONJ
ajst-16340	128	19	is	be	AUX
ajst-16340	128	20	applied	apply	VERB
ajst-16340	128	21	to	to	PART
ajst-16340	128	22	optimize	optimize	VERB
ajst-16340	128	23	the	the	DET
ajst-16340	128	24	parameters	parameter	NOUN
ajst-16340	128	25	of	of	ADP
ajst-16340	128	26	bp	bp	PROPN
ajst-16340	128	27	neural	neural	ADJ
ajst-16340	128	28	networks	network	NOUN
ajst-16340	128	29	.	.	PUNCT
ajst-16340	129	1	this	this	DET
ajst-16340	129	2	algorithm	algorithm	NOUN
ajst-16340	129	3	optimizes	optimize	VERB
ajst-16340	129	4	the	the	DET
ajst-16340	129	5	weights	weight	NOUN
ajst-16340	129	6	and	and	CCONJ
ajst-16340	129	7	biases	bias	NOUN
ajst-16340	129	8	of	of	ADP
ajst-16340	129	9	the	the	DET
ajst-16340	129	10	network	network	NOUN
ajst-16340	129	11	through	through	ADP
ajst-16340	129	12	operations	operation	NOUN
ajst-16340	129	13	such	such	ADJ
ajst-16340	129	14	as	as	ADP
ajst-16340	129	15	selection	selection	NOUN
ajst-16340	129	16	,	,	PUNCT
ajst-16340	129	17	crossover	crossover	NOUN
ajst-16340	129	18	,	,	PUNCT
ajst-16340	129	19	and	and	CCONJ
ajst-16340	129	20	mutation	mutation	NOUN
ajst-16340	129	21	.	.	PUNCT
ajst-16340	130	1	due	due	ADP
ajst-16340	130	2	to	to	ADP
ajst-16340	130	3	the	the	DET
ajst-16340	130	4	advantage	advantage	NOUN
ajst-16340	130	5	of	of	ADP
ajst-16340	130	6	genetic	genetic	ADJ
ajst-16340	130	7	algorithms	algorithm	NOUN
ajst-16340	130	8	in	in	ADP
ajst-16340	130	9	global	global	ADJ
ajst-16340	130	10	search	search	NOUN
ajst-16340	130	11	,	,	PUNCT
ajst-16340	130	12	the	the	DET
ajst-16340	130	13	neural	neural	ADJ
ajst-16340	130	14	network	network	NOUN
ajst-16340	130	15	is	be	AUX
ajst-16340	130	16	more	more	ADV
ajst-16340	130	17	likely	likely	ADJ
ajst-16340	130	18	to	to	PART
ajst-16340	130	19	find	find	VERB
ajst-16340	130	20	global	global	ADJ
ajst-16340	130	21	optimal	optimal	ADJ
ajst-16340	130	22	solutions	solution	NOUN
ajst-16340	130	23	.	.	PUNCT
ajst-16340	131	1	this	this	DET
ajst-16340	131	2	approach	approach	NOUN
ajst-16340	131	3	helps	help	VERB
ajst-16340	131	4	improve	improve	VERB
ajst-16340	131	5	the	the	DET
ajst-16340	131	6	performance	performance	NOUN
ajst-16340	131	7	of	of	ADP
ajst-16340	131	8	neural	neural	ADJ
ajst-16340	131	9	networks	network	NOUN
ajst-16340	131	10	,	,	PUNCT
ajst-16340	131	11	enabling	enable	VERB
ajst-16340	131	12	faster	fast	ADJ
ajst-16340	131	13	convergence	convergence	NOUN
ajst-16340	131	14	and	and	CCONJ
ajst-16340	131	15	better	well	ADJ
ajst-16340	131	16	results	result	NOUN
ajst-16340	131	17	during	during	ADP
ajst-16340	131	18	training	training	NOUN
ajst-16340	131	19	.	.	PUNCT
ajst-16340	132	1	3.5.1.3	3.5.1.3	NUM
ajst-16340	132	2	particle	particle	NOUN
ajst-16340	132	3	swarm	swarm	NOUN
ajst-16340	132	4	algorithm	algorithm	NOUN
ajst-16340	132	5	optimization	optimization	NOUN
ajst-16340	132	6	the	the	DET
ajst-16340	132	7	particle	particle	NOUN
ajst-16340	132	8	swarm	swarm	NOUN
ajst-16340	132	9	algorithm[5	algorithm[5	NOUN
ajst-16340	132	10	]	]	PUNCT
ajst-16340	132	11	simulates	simulate	VERB
ajst-16340	132	12	the	the	DET
ajst-16340	132	13	collective	collective	ADJ
ajst-16340	132	14	behavior	behavior	NOUN
ajst-16340	132	15	of	of	ADP
ajst-16340	132	16	birds	bird	NOUN
ajst-16340	132	17	or	or	CCONJ
ajst-16340	132	18	fish	fish	VERB
ajst-16340	132	19	and	and	CCONJ
ajst-16340	132	20	is	be	AUX
ajst-16340	132	21	applied	apply	VERB
ajst-16340	132	22	to	to	ADP
ajst-16340	132	23	parameter	parameter	NOUN
ajst-16340	132	24	adjustment	adjustment	NOUN
ajst-16340	132	25	in	in	ADP
ajst-16340	132	26	bp	bp	PROPN
ajst-16340	132	27	neural	neural	ADJ
ajst-16340	132	28	networks	network	NOUN
ajst-16340	132	29	.	.	PUNCT
ajst-16340	133	1	by	by	ADP
ajst-16340	133	2	simulating	simulate	VERB
ajst-16340	133	3	the	the	DET
ajst-16340	133	4	flight	flight	NOUN
ajst-16340	133	5	and	and	CCONJ
ajst-16340	133	6	collective	collective	ADJ
ajst-16340	133	7	cooperation	cooperation	NOUN
ajst-16340	133	8	of	of	ADP
ajst-16340	133	9	particles	particle	NOUN
ajst-16340	133	10	,	,	PUNCT
ajst-16340	133	11	this	this	DET
ajst-16340	133	12	algorithm	algorithm	NOUN
ajst-16340	133	13	can	can	AUX
ajst-16340	133	14	search	search	VERB
ajst-16340	133	15	for	for	ADP
ajst-16340	133	16	the	the	DET
ajst-16340	133	17	optimal	optimal	ADJ
ajst-16340	133	18	solution	solution	NOUN
ajst-16340	133	19	in	in	ADP
ajst-16340	133	20	parameter	parameter	NOUN
ajst-16340	133	21	space	space	NOUN
ajst-16340	133	22	.	.	PUNCT
ajst-16340	134	1	the	the	DET
ajst-16340	134	2	advantage	advantage	NOUN
ajst-16340	134	3	of	of	ADP
ajst-16340	134	4	the	the	DET
ajst-16340	134	5	particle	particle	NOUN
ajst-16340	134	6	swarm	swarm	NOUN
ajst-16340	134	7	algorithm	algorithm	NOUN
ajst-16340	134	8	lies	lie	VERB
ajst-16340	134	9	in	in	ADP
ajst-16340	134	10	its	its	PRON
ajst-16340	134	11	balance	balance	NOUN
ajst-16340	134	12	between	between	ADP
ajst-16340	134	13	local	local	ADJ
ajst-16340	134	14	search	search	NOUN
ajst-16340	134	15	and	and	CCONJ
ajst-16340	134	16	global	global	ADJ
ajst-16340	134	17	search	search	NOUN
ajst-16340	134	18	,	,	PUNCT
ajst-16340	134	19	making	make	VERB
ajst-16340	134	20	it	it	PRON
ajst-16340	134	21	easier	easy	ADJ
ajst-16340	134	22	for	for	SCONJ
ajst-16340	134	23	bp	bp	PROPN
ajst-16340	134	24	neural	neural	ADJ
ajst-16340	134	25	networks	network	NOUN
ajst-16340	134	26	to	to	PART
ajst-16340	134	27	converge	converge	VERB
ajst-16340	134	28	to	to	ADP
ajst-16340	134	29	good	good	ADJ
ajst-16340	134	30	solutions	solution	NOUN
ajst-16340	134	31	.	.	PUNCT
ajst-16340	135	1	this	this	DET
ajst-16340	135	2	way	way	NOUN
ajst-16340	135	3	,	,	PUNCT
ajst-16340	135	4	the	the	DET
ajst-16340	135	5	network	network	NOUN
ajst-16340	135	6	can	can	AUX
ajst-16340	135	7	more	more	ADV
ajst-16340	135	8	effectively	effectively	ADV
ajst-16340	135	9	adjust	adjust	VERB
ajst-16340	135	10	parameters	parameter	NOUN
ajst-16340	135	11	during	during	ADP
ajst-16340	135	12	training	training	NOUN
ajst-16340	135	13	,	,	PUNCT
ajst-16340	135	14	enhancing	enhance	VERB
ajst-16340	135	15	performance	performance	NOUN
ajst-16340	135	16	.	.	PUNCT
ajst-16340	136	1	3.5.1.4	3.5.1.4	NUM
ajst-16340	136	2	simulated	simulate	VERB
ajst-16340	136	3	annealing	anneal	VERB
ajst-16340	136	4	algorithm	algorithm	NOUN
ajst-16340	136	5	optimization	optimization	NOUN
ajst-16340	136	6	the	the	DET
ajst-16340	136	7	simulated	simulate	VERB
ajst-16340	136	8	annealing	anneal	VERB
ajst-16340	136	9	algorithm	algorithm	NOUN
ajst-16340	136	10	simulates	simulate	VERB
ajst-16340	136	11	the	the	DET
ajst-16340	136	12	physical	physical	ADJ
ajst-16340	136	13	principles	principle	NOUN
ajst-16340	136	14	of	of	ADP
ajst-16340	136	15	metal	metal	NOUN
ajst-16340	136	16	annealing	annealing	NOUN
ajst-16340	136	17	,	,	PUNCT
ajst-16340	136	18	gradually	gradually	ADV
ajst-16340	136	19	searching	search	VERB
ajst-16340	136	20	for	for	ADP
ajst-16340	136	21	the	the	DET
ajst-16340	136	22	global	global	ADJ
ajst-16340	136	23	optimal	optimal	ADJ
ajst-16340	136	24	solution	solution	NOUN
ajst-16340	136	25	in	in	ADP
ajst-16340	136	26	the	the	DET
ajst-16340	136	27	solution	solution	NOUN
ajst-16340	136	28	space	space	NOUN
ajst-16340	136	29	as	as	SCONJ
ajst-16340	136	30	the	the	DET
ajst-16340	136	31	temperature	temperature	NOUN
ajst-16340	136	32	decreases	decrease	VERB
ajst-16340	136	33	.	.	PUNCT
ajst-16340	137	1	introducing	introduce	VERB
ajst-16340	137	2	the	the	DET
ajst-16340	137	3	simulated	simulate	VERB
ajst-16340	137	4	annealing	anneal	VERB
ajst-16340	137	5	algorithm	algorithm	NOUN
ajst-16340	137	6	into	into	ADP
ajst-16340	137	7	the	the	DET
ajst-16340	137	8	training	training	NOUN
ajst-16340	137	9	process	process	NOUN
ajst-16340	137	10	of	of	ADP
ajst-16340	137	11	bp	bp	PROPN
ajst-16340	137	12	neural	neural	ADJ
ajst-16340	137	13	networks	network	NOUN
ajst-16340	137	14	helps	help	VERB
ajst-16340	137	15	the	the	DET
ajst-16340	137	16	network	network	NOUN
ajst-16340	137	17	escape	escape	VERB
ajst-16340	137	18	from	from	ADP
ajst-16340	137	19	local	local	ADJ
ajst-16340	137	20	optima	optima	NOUN
ajst-16340	137	21	and	and	CCONJ
ajst-16340	137	22	better	well	ADJ
ajst-16340	137	23	converge	converge	VERB
ajst-16340	137	24	to	to	ADP
ajst-16340	137	25	global	global	ADJ
ajst-16340	137	26	optimal	optimal	ADJ
ajst-16340	137	27	solutions	solution	NOUN
ajst-16340	137	28	.	.	PUNCT
ajst-16340	138	1	this	this	DET
ajst-16340	138	2	method	method	NOUN
ajst-16340	138	3	,	,	PUNCT
ajst-16340	138	4	by	by	ADP
ajst-16340	138	5	simulating	simulate	VERB
ajst-16340	138	6	the	the	DET
ajst-16340	138	7	gradual	gradual	ADJ
ajst-16340	138	8	cooling	cool	VERB
ajst-16340	138	9	process	process	NOUN
ajst-16340	138	10	of	of	ADP
ajst-16340	138	11	annealing	annealing	NOUN
ajst-16340	138	12	,	,	PUNCT
ajst-16340	138	13	makes	make	VERB
ajst-16340	138	14	the	the	DET
ajst-16340	138	15	network	network	NOUN
ajst-16340	138	16	more	more	ADV
ajst-16340	138	17	flexible	flexible	ADJ
ajst-16340	138	18	in	in	ADP
ajst-16340	138	19	searching	search	VERB
ajst-16340	138	20	parameter	parameter	NOUN
ajst-16340	138	21	space	space	NOUN
ajst-16340	138	22	,	,	PUNCT
ajst-16340	138	23	increasing	increase	VERB
ajst-16340	138	24	the	the	DET
ajst-16340	138	25	probability	probability	NOUN
ajst-16340	138	26	of	of	ADP
ajst-16340	138	27	finding	find	VERB
ajst-16340	138	28	global	global	ADJ
ajst-16340	138	29	optimal	optimal	ADJ
ajst-16340	138	30	solutions	solution	NOUN
ajst-16340	138	31	and	and	CCONJ
ajst-16340	138	32	enhancing	enhance	VERB
ajst-16340	138	33	the	the	DET
ajst-16340	138	34	training	training	NOUN
ajst-16340	138	35	effectiveness	effectiveness	NOUN
ajst-16340	138	36	of	of	ADP
ajst-16340	138	37	the	the	DET
ajst-16340	138	38	neural	neural	ADJ
ajst-16340	138	39	network	network	NOUN
ajst-16340	138	40	.	.	PUNCT
ajst-16340	139	1	3.5.2	3.5.2	X
ajst-16340	139	2	.	.	NOUN
ajst-16340	139	3	comprehensive	comprehensive	ADJ
ajst-16340	139	4	optimization	optimization	NOUN
ajst-16340	139	5	strategies	strategy	NOUN
ajst-16340	139	6	researchers	researcher	NOUN
ajst-16340	139	7	have	have	AUX
ajst-16340	139	8	also	also	ADV
ajst-16340	139	9	attempted	attempt	VERB
ajst-16340	139	10	to	to	PART
ajst-16340	139	11	combine	combine	VERB
ajst-16340	139	12	multiple	multiple	ADJ
ajst-16340	139	13	optimization	optimization	NOUN
ajst-16340	139	14	algorithms	algorithm	NOUN
ajst-16340	139	15	to	to	PART
ajst-16340	139	16	form	form	VERB
ajst-16340	139	17	comprehensive	comprehensive	ADJ
ajst-16340	139	18	optimization	optimization	NOUN
ajst-16340	139	19	strategies	strategy	NOUN
ajst-16340	139	20	.	.	PUNCT
ajst-16340	140	1	by	by	ADP
ajst-16340	140	2	integrating	integrate	VERB
ajst-16340	140	3	genetic	genetic	ADJ
ajst-16340	140	4	algorithm	algorithm	NOUN
ajst-16340	140	5	,	,	PUNCT
ajst-16340	140	6	particle	particle	NOUN
ajst-16340	140	7	swarm	swarm	NOUN
ajst-16340	140	8	algorithm	algorithm	NOUN
ajst-16340	140	9	,	,	PUNCT
ajst-16340	140	10	and	and	CCONJ
ajst-16340	140	11	simulated	simulate	VERB
ajst-16340	140	12	annealing	annealing	NOUN
ajst-16340	140	13	algorithm	algorithm	NOUN
ajst-16340	140	14	,	,	PUNCT
ajst-16340	140	15	for	for	ADP
ajst-16340	140	16	example	example	NOUN
ajst-16340	140	17	,	,	PUNCT
ajst-16340	140	18	researchers	researcher	NOUN
ajst-16340	140	19	can	can	AUX
ajst-16340	140	20	fully	fully	ADV
ajst-16340	140	21	leverage	leverage	VERB
ajst-16340	140	22	their	their	PRON
ajst-16340	140	23	respective	respective	ADJ
ajst-16340	140	24	strengths	strength	NOUN
ajst-16340	140	25	,	,	PUNCT
ajst-16340	140	26	improving	improve	VERB
ajst-16340	140	27	the	the	DET
ajst-16340	140	28	efficiency	efficiency	NOUN
ajst-16340	140	29	of	of	ADP
ajst-16340	140	30	parameter	parameter	NOUN
ajst-16340	140	31	search	search	NOUN
ajst-16340	140	32	.	.	PUNCT
ajst-16340	141	1	this	this	DET
ajst-16340	141	2	comprehensive	comprehensive	ADJ
ajst-16340	141	3	strategy	strategy	NOUN
ajst-16340	141	4	allows	allow	VERB
ajst-16340	141	5	for	for	ADP
ajst-16340	141	6	a	a	DET
ajst-16340	141	7	more	more	ADV
ajst-16340	141	8	comprehensive	comprehensive	ADJ
ajst-16340	141	9	exploration	exploration	NOUN
ajst-16340	141	10	of	of	ADP
ajst-16340	141	11	parameter	parameter	NOUN
ajst-16340	141	12	space	space	NOUN
ajst-16340	141	13	,	,	PUNCT
ajst-16340	141	14	effectively	effectively	ADV
ajst-16340	141	15	enhancing	enhance	VERB
ajst-16340	141	16	the	the	DET
ajst-16340	141	17	performance	performance	NOUN
ajst-16340	141	18	of	of	ADP
ajst-16340	141	19	bp	bp	PROPN
ajst-16340	141	20	neural	neural	ADJ
ajst-16340	141	21	networks	network	NOUN
ajst-16340	141	22	.	.	PUNCT
ajst-16340	142	1	through	through	ADP
ajst-16340	142	2	the	the	DET
ajst-16340	142	3	combination	combination	NOUN
ajst-16340	142	4	of	of	ADP
ajst-16340	142	5	multiple	multiple	ADJ
ajst-16340	142	6	algorithms	algorithm	NOUN
ajst-16340	142	7	,	,	PUNCT
ajst-16340	142	8	researchers	researcher	NOUN
ajst-16340	142	9	can	can	AUX
ajst-16340	142	10	more	more	ADV
ajst-16340	142	11	flexibly	flexibly	ADV
ajst-16340	142	12	address	address	VERB
ajst-16340	142	13	different	different	ADJ
ajst-16340	142	14	network	network	NOUN
ajst-16340	142	15	structures	structure	NOUN
ajst-16340	142	16	and	and	CCONJ
ajst-16340	142	17	types	type	NOUN
ajst-16340	142	18	of	of	ADP
ajst-16340	142	19	problems	problem	NOUN
ajst-16340	142	20	,	,	PUNCT
ajst-16340	142	21	further	far	ADV
ajst-16340	142	22	optimizing	optimize	VERB
ajst-16340	142	23	the	the	DET
ajst-16340	142	24	training	training	NOUN
ajst-16340	142	25	process	process	NOUN
ajst-16340	142	26	of	of	ADP
ajst-16340	142	27	neural	neural	ADJ
ajst-16340	142	28	networks	network	NOUN
ajst-16340	142	29	for	for	ADP
ajst-16340	142	30	better	well	ADJ
ajst-16340	142	31	performance	performance	NOUN
ajst-16340	142	32	.	.	PUNCT
ajst-16340	143	1	3.5.2.1	3.5.2.1	NUM
ajst-16340	143	2	improved	improve	VERB
ajst-16340	143	3	gradient	gradient	ADJ
ajst-16340	143	4	descent	descent	NOUN
ajst-16340	143	5	algorithms	algorithm	NOUN
ajst-16340	143	6	gradient	gradient	ADJ
ajst-16340	143	7	descent	descent	NOUN
ajst-16340	143	8	is	be	AUX
ajst-16340	143	9	a	a	DET
ajst-16340	143	10	commonly	commonly	ADV
ajst-16340	143	11	used	use	VERB
ajst-16340	143	12	optimization	optimization	NOUN
ajst-16340	143	13	algorithm	algorithm	NOUN
ajst-16340	143	14	in	in	ADP
ajst-16340	143	15	deep	deep	ADJ
ajst-16340	143	16	learning	learning	NOUN
ajst-16340	143	17	.	.	PUNCT
ajst-16340	144	1	however	however	ADV
ajst-16340	144	2	,	,	PUNCT
ajst-16340	144	3	it	it	PRON
ajst-16340	144	4	has	have	VERB
ajst-16340	144	5	some	some	DET
ajst-16340	144	6	issues	issue	NOUN
ajst-16340	144	7	,	,	PUNCT
ajst-16340	144	8	such	such	ADJ
ajst-16340	144	9	as	as	ADP
ajst-16340	144	10	slow	slow	ADJ
ajst-16340	144	11	convergence	convergence	NOUN
ajst-16340	144	12	and	and	CCONJ
ajst-16340	144	13	susceptibility	susceptibility	NOUN
ajst-16340	144	14	to	to	ADP
ajst-16340	144	15	local	local	ADJ
ajst-16340	144	16	optima	optima	NOUN
ajst-16340	144	17	.	.	PUNCT
ajst-16340	145	1	to	to	PART
ajst-16340	145	2	address	address	VERB
ajst-16340	145	3	these	these	DET
ajst-16340	145	4	problems	problem	NOUN
ajst-16340	145	5	,	,	PUNCT
ajst-16340	145	6	researchers	researcher	NOUN
ajst-16340	145	7	have	have	AUX
ajst-16340	145	8	introduced	introduce	VERB
ajst-16340	145	9	methods	method	NOUN
ajst-16340	145	10	with	with	ADP
ajst-16340	145	11	adaptive	adaptive	ADJ
ajst-16340	145	12	learning	learning	NOUN
ajst-16340	145	13	rates	rate	NOUN
ajst-16340	145	14	,	,	PUNCT
ajst-16340	145	15	such	such	ADJ
ajst-16340	145	16	as	as	ADP
ajst-16340	145	17	adagrad	adagrad	ADJ
ajst-16340	145	18	and	and	CCONJ
ajst-16340	145	19	adam	adam	PROPN
ajst-16340	145	20	.	.	PUNCT
ajst-16340	146	1	these	these	DET
ajst-16340	146	2	algorithms	algorithm	NOUN
ajst-16340	146	3	dynamically	dynamically	ADV
ajst-16340	146	4	adjust	adjust	VERB
ajst-16340	146	5	the	the	DET
ajst-16340	146	6	learning	learning	NOUN
ajst-16340	146	7	rate	rate	NOUN
ajst-16340	146	8	to	to	PART
ajst-16340	146	9	adapt	adapt	VERB
ajst-16340	146	10	more	more	ADV
ajst-16340	146	11	flexibly	flexibly	ADV
ajst-16340	146	12	to	to	ADP
ajst-16340	146	13	different	different	ADJ
ajst-16340	146	14	parameter	parameter	NOUN
ajst-16340	146	15	update	update	NOUN
ajst-16340	146	16	situations	situation	NOUN
ajst-16340	146	17	during	during	ADP
ajst-16340	146	18	training	training	NOUN
ajst-16340	146	19	,	,	PUNCT
ajst-16340	146	20	improving	improve	VERB
ajst-16340	146	21	the	the	DET
ajst-16340	146	22	convergence	convergence	NOUN
ajst-16340	146	23	speed	speed	NOUN
ajst-16340	146	24	and	and	CCONJ
ajst-16340	146	25	stability	stability	NOUN
ajst-16340	146	26	of	of	ADP
ajst-16340	146	27	the	the	DET
ajst-16340	146	28	algorithm	algorithm	NOUN
ajst-16340	146	29	.	.	PUNCT
ajst-16340	147	1	this	this	DET
ajst-16340	147	2	adaptive	adaptive	ADJ
ajst-16340	147	3	learning	learning	NOUN
ajst-16340	147	4	rate	rate	NOUN
ajst-16340	147	5	strategy	strategy	NOUN
ajst-16340	147	6	makes	make	VERB
ajst-16340	147	7	gradient	gradient	ADJ
ajst-16340	147	8	descent	descent	NOUN
ajst-16340	147	9	more	more	ADV
ajst-16340	147	10	suitable	suitable	ADJ
ajst-16340	147	11	for	for	ADP
ajst-16340	147	12	deep	deep	ADJ
ajst-16340	147	13	learning	learning	NOUN
ajst-16340	147	14	tasks	task	NOUN
ajst-16340	147	15	,	,	PUNCT
ajst-16340	147	16	accelerating	accelerate	VERB
ajst-16340	147	17	the	the	DET
ajst-16340	147	18	model	model	NOUN
ajst-16340	147	19	training	training	NOUN
ajst-16340	147	20	process	process	NOUN
ajst-16340	147	21	and	and	CCONJ
ajst-16340	147	22	reducing	reduce	VERB
ajst-16340	147	23	the	the	DET
ajst-16340	147	24	risk	risk	NOUN
ajst-16340	147	25	of	of	ADP
ajst-16340	147	26	getting	getting	AUX
ajst-16340	147	27	stuck	stick	VERB
ajst-16340	147	28	in	in	ADP
ajst-16340	147	29	local	local	ADJ
ajst-16340	147	30	optima	optima	NOUN
ajst-16340	147	31	.	.	PUNCT
ajst-16340	148	1	3.5.2.2	3.5.2.2	NUM
ajst-16340	148	2	reinforcement	reinforcement	NOUN
ajst-16340	148	3	learning	learning	NOUN
ajst-16340	148	4	algorithms	algorithms	NOUN
ajst-16340	148	5	reinforcement	reinforcement	NOUN
ajst-16340	148	6	learning	learning	NOUN
ajst-16340	148	7	algorithms	algorithm	NOUN
ajst-16340	148	8	are	be	AUX
ajst-16340	148	9	introduced	introduce	VERB
ajst-16340	148	10	to	to	PART
ajst-16340	148	11	optimize	optimize	VERB
ajst-16340	148	12	the	the	DET
ajst-16340	148	13	parameters	parameter	NOUN
ajst-16340	148	14	of	of	ADP
ajst-16340	148	15	bp	bp	PROPN
ajst-16340	148	16	neural	neural	ADJ
ajst-16340	148	17	networks	network	NOUN
ajst-16340	148	18	.	.	PUNCT
ajst-16340	149	1	by	by	ADP
ajst-16340	149	2	establishing	establish	VERB
ajst-16340	149	3	an	an	DET
ajst-16340	149	4	interactive	interactive	ADJ
ajst-16340	149	5	model	model	NOUN
ajst-16340	149	6	between	between	ADP
ajst-16340	149	7	the	the	DET
ajst-16340	149	8	network	network	NOUN
ajst-16340	149	9	and	and	CCONJ
ajst-16340	149	10	the	the	DET
ajst-16340	149	11	environment	environment	NOUN
ajst-16340	149	12	,	,	PUNCT
ajst-16340	149	13	reinforcement	reinforcement	NOUN
ajst-16340	149	14	learning	learning	NOUN
ajst-16340	149	15	algorithms	algorithm	NOUN
ajst-16340	149	16	can	can	AUX
ajst-16340	149	17	continuously	continuously	ADV
ajst-16340	149	18	optimize	optimize	VERB
ajst-16340	149	19	the	the	DET
ajst-16340	149	20	network	network	NOUN
ajst-16340	149	21	's	's	PART
ajst-16340	149	22	parameters	parameter	NOUN
ajst-16340	149	23	through	through	ADP
ajst-16340	149	24	trial	trial	NOUN
ajst-16340	149	25	and	and	CCONJ
ajst-16340	149	26	error	error	NOUN
ajst-16340	149	27	,	,	PUNCT
ajst-16340	149	28	improving	improve	VERB
ajst-16340	149	29	its	its	PRON
ajst-16340	149	30	performance	performance	NOUN
ajst-16340	149	31	.	.	PUNCT
ajst-16340	150	1	this	this	DET
ajst-16340	150	2	approach	approach	NOUN
ajst-16340	150	3	is	be	AUX
ajst-16340	150	4	often	often	ADV
ajst-16340	150	5	used	use	VERB
ajst-16340	150	6	to	to	PART
ajst-16340	150	7	handle	handle	VERB
ajst-16340	150	8	complex	complex	ADJ
ajst-16340	150	9	tasks	task	NOUN
ajst-16340	150	10	and	and	CCONJ
ajst-16340	150	11	enhance	enhance	VERB
ajst-16340	150	12	the	the	DET
ajst-16340	150	13	network	network	NOUN
ajst-16340	150	14	's	's	PART
ajst-16340	150	15	generalization	generalization	NOUN
ajst-16340	150	16	ability	ability	NOUN
ajst-16340	150	17	.	.	PUNCT
ajst-16340	151	1	through	through	ADP
ajst-16340	151	2	reinforcement	reinforcement	NOUN
ajst-16340	151	3	learning	learning	NOUN
ajst-16340	151	4	,	,	PUNCT
ajst-16340	151	5	the	the	DET
ajst-16340	151	6	network	network	NOUN
ajst-16340	151	7	can	can	AUX
ajst-16340	151	8	adjust	adjust	VERB
ajst-16340	151	9	its	its	PRON
ajst-16340	151	10	parameters	parameter	NOUN
ajst-16340	151	11	based	base	VERB
ajst-16340	151	12	on	on	ADP
ajst-16340	151	13	environmental	environmental	ADJ
ajst-16340	151	14	feedback	feedback	NOUN
ajst-16340	151	15	,	,	PUNCT
ajst-16340	151	16	gradually	gradually	ADV
ajst-16340	151	17	learning	learn	VERB
ajst-16340	151	18	and	and	CCONJ
ajst-16340	151	19	optimizing	optimize	VERB
ajst-16340	151	20	its	its	PRON
ajst-16340	151	21	behavior	behavior	NOUN
ajst-16340	151	22	strategy	strategy	NOUN
ajst-16340	151	23	.	.	PUNCT
ajst-16340	152	1	this	this	DET
ajst-16340	152	2	learning	learning	NOUN
ajst-16340	152	3	method	method	NOUN
ajst-16340	152	4	helps	help	VERB
ajst-16340	152	5	the	the	DET
ajst-16340	152	6	network	network	NOUN
ajst-16340	152	7	make	make	VERB
ajst-16340	152	8	more	more	ADV
ajst-16340	152	9	flexible	flexible	ADJ
ajst-16340	152	10	and	and	CCONJ
ajst-16340	152	11	intelligent	intelligent	ADJ
ajst-16340	152	12	decisions	decision	NOUN
ajst-16340	152	13	when	when	SCONJ
ajst-16340	152	14	facing	face	VERB
ajst-16340	152	15	unknown	unknown	ADJ
ajst-16340	152	16	environments	environment	NOUN
ajst-16340	152	17	or	or	CCONJ
ajst-16340	152	18	complex	complex	ADJ
ajst-16340	152	19	tasks	task	NOUN
ajst-16340	152	20	,	,	PUNCT
ajst-16340	152	21	enhancing	enhance	VERB
ajst-16340	152	22	the	the	DET
ajst-16340	152	23	adaptability	adaptability	NOUN
ajst-16340	152	24	and	and	CCONJ
ajst-16340	152	25	performance	performance	NOUN
ajst-16340	152	26	of	of	ADP
ajst-16340	152	27	bp	bp	PROPN
ajst-16340	152	28	neural	neural	ADJ
ajst-16340	152	29	networks	network	NOUN
ajst-16340	152	30	.	.	PUNCT
ajst-16340	153	1	4	4	X
ajst-16340	153	2	.	.	X
ajst-16340	153	3	advances	advance	NOUN
ajst-16340	153	4	and	and	CCONJ
ajst-16340	153	5	future	future	ADJ
ajst-16340	153	6	prospects	prospect	NOUN
ajst-16340	153	7	in	in	ADP
ajst-16340	153	8	deep	deep	ADJ
ajst-16340	153	9	learning	learning	NOUN
ajst-16340	153	10	in	in	ADP
ajst-16340	153	11	recent	recent	ADJ
ajst-16340	153	12	years	year	NOUN
ajst-16340	153	13	,	,	PUNCT
ajst-16340	153	14	significant	significant	ADJ
ajst-16340	153	15	progress	progress	NOUN
ajst-16340	153	16	has	have	AUX
ajst-16340	153	17	been	be	AUX
ajst-16340	153	18	made	make	VERB
ajst-16340	153	19	in	in	ADP
ajst-16340	153	20	the	the	DET
ajst-16340	153	21	field	field	NOUN
ajst-16340	153	22	of	of	ADP
ajst-16340	153	23	deep	deep	ADJ
ajst-16340	153	24	learning	learning	NOUN
ajst-16340	153	25	,	,	PUNCT
ajst-16340	153	26	including	include	VERB
ajst-16340	153	27	innovative	innovative	ADJ
ajst-16340	153	28	activation	activation	NOUN
ajst-16340	153	29	functions	function	NOUN
ajst-16340	153	30	,	,	PUNCT
ajst-16340	153	31	regularization	regularization	NOUN
ajst-16340	153	32	methods	method	NOUN
ajst-16340	153	33	,	,	PUNCT
ajst-16340	153	34	multimodal	multimodal	NOUN
ajst-16340	153	35	fusion	fusion	NOUN
ajst-16340	153	36	,	,	PUNCT
ajst-16340	153	37	and	and	CCONJ
ajst-16340	153	38	cross	cross	ADJ
ajst-16340	153	39	-	-	ADJ
ajst-16340	153	40	domain	domain	ADJ
ajst-16340	153	41	applications	application	NOUN
ajst-16340	153	42	.	.	PUNCT
ajst-16340	154	1	these	these	DET
ajst-16340	154	2	advancements	advancement	NOUN
ajst-16340	154	3	not	not	PART
ajst-16340	154	4	only	only	ADV
ajst-16340	154	5	enhance	enhance	VERB
ajst-16340	154	6	the	the	DET
ajst-16340	154	7	performance	performance	NOUN
ajst-16340	154	8	of	of	ADP
ajst-16340	154	9	deep	deep	ADJ
ajst-16340	154	10	learning	learning	NOUN
ajst-16340	154	11	models	model	NOUN
ajst-16340	154	12	but	but	CCONJ
ajst-16340	154	13	also	also	ADV
ajst-16340	154	14	expand	expand	VERB
ajst-16340	154	15	their	their	PRON
ajst-16340	154	16	application	application	NOUN
ajst-16340	154	17	domains	domain	NOUN
ajst-16340	154	18	.	.	PUNCT
ajst-16340	155	1	firstly	firstly	ADV
ajst-16340	155	2	,	,	PUNCT
ajst-16340	155	3	in	in	ADP
ajst-16340	155	4	terms	term	NOUN
ajst-16340	155	5	of	of	ADP
ajst-16340	155	6	activation	activation	NOUN
ajst-16340	155	7	functions	function	NOUN
ajst-16340	155	8	,	,	PUNCT
ajst-16340	155	9	traditional	traditional	ADJ
ajst-16340	155	10	relu	relu	NOUN
ajst-16340	155	11	has	have	AUX
ajst-16340	155	12	been	be	AUX
ajst-16340	155	13	widely	widely	ADV
ajst-16340	155	14	adopted	adopt	VERB
ajst-16340	155	15	,	,	PUNCT
ajst-16340	155	16	but	but	CCONJ
ajst-16340	155	17	the	the	DET
ajst-16340	155	18	introduction	introduction	NOUN
ajst-16340	155	19	of	of	ADP
ajst-16340	155	20	newgeneration	newgeneration	NOUN
ajst-16340	155	21	activation	activation	NOUN
ajst-16340	155	22	functions	function	NOUN
ajst-16340	155	23	such	such	ADJ
ajst-16340	155	24	as	as	ADP
ajst-16340	155	25	leaky	leaky	ADJ
ajst-16340	155	26	relu	relu	NOUN
ajst-16340	155	27	and	and	CCONJ
ajst-16340	155	28	elu	elu	PROPN
ajst-16340	155	29	has	have	AUX
ajst-16340	155	30	enhanced	enhance	VERB
ajst-16340	155	31	the	the	DET
ajst-16340	155	32	nonlinear	nonlinear	ADJ
ajst-16340	155	33	expressive	expressive	ADJ
ajst-16340	155	34	power	power	NOUN
ajst-16340	155	35	of	of	ADP
ajst-16340	155	36	neural	neural	ADJ
ajst-16340	155	37	networks	network	NOUN
ajst-16340	155	38	,	,	PUNCT
ajst-16340	155	39	effectively	effectively	ADV
ajst-16340	155	40	mitigating	mitigate	VERB
ajst-16340	155	41	the	the	DET
ajst-16340	155	42	vanishing	vanish	VERB
ajst-16340	155	43	gradient	gradient	ADJ
ajst-16340	155	44	154	154	NUM
ajst-16340	155	45	problem	problem	NOUN
ajst-16340	155	46	.	.	PUNCT
ajst-16340	156	1	these	these	DET
ajst-16340	156	2	innovations	innovation	NOUN
ajst-16340	156	3	provide	provide	VERB
ajst-16340	156	4	more	more	ADV
ajst-16340	156	5	stable	stable	ADJ
ajst-16340	156	6	and	and	CCONJ
ajst-16340	156	7	efficient	efficient	ADJ
ajst-16340	156	8	solutions	solution	NOUN
ajst-16340	156	9	for	for	ADP
ajst-16340	156	10	the	the	DET
ajst-16340	156	11	training	training	NOUN
ajst-16340	156	12	of	of	ADP
ajst-16340	156	13	deep	deep	ADJ
ajst-16340	156	14	learning	learning	NOUN
ajst-16340	156	15	models	model	NOUN
ajst-16340	156	16	.	.	PUNCT
ajst-16340	157	1	secondly	secondly	ADV
ajst-16340	157	2	,	,	PUNCT
ajst-16340	157	3	in	in	ADP
ajst-16340	157	4	regularization	regularization	NOUN
ajst-16340	157	5	methods	method	NOUN
ajst-16340	157	6	,	,	PUNCT
ajst-16340	157	7	the	the	DET
ajst-16340	157	8	evolution	evolution	NOUN
ajst-16340	157	9	of	of	ADP
ajst-16340	157	10	l1	l1	PROPN
ajst-16340	157	11	and	and	CCONJ
ajst-16340	157	12	l2	l2	NOUN
ajst-16340	157	13	regularization	regularization	NOUN
ajst-16340	157	14	,	,	PUNCT
ajst-16340	157	15	dropout	dropout	NOUN
ajst-16340	157	16	technique	technique	NOUN
ajst-16340	157	17	,	,	PUNCT
ajst-16340	157	18	and	and	CCONJ
ajst-16340	157	19	batch	batch	NOUN
ajst-16340	157	20	normalization	normalization	NOUN
ajst-16340	157	21	improves	improve	VERB
ajst-16340	157	22	the	the	DET
ajst-16340	157	23	model	model	NOUN
ajst-16340	157	24	's	's	PART
ajst-16340	157	25	generalization	generalization	NOUN
ajst-16340	157	26	ability	ability	NOUN
ajst-16340	157	27	,	,	PUNCT
ajst-16340	157	28	accelerates	accelerate	VERB
ajst-16340	157	29	convergence	convergence	NOUN
ajst-16340	157	30	speed	speed	NOUN
ajst-16340	157	31	,	,	PUNCT
ajst-16340	157	32	and	and	CCONJ
ajst-16340	157	33	enhances	enhance	VERB
ajst-16340	157	34	robustness	robustness	NOUN
ajst-16340	157	35	to	to	ADP
ajst-16340	157	36	initial	initial	ADJ
ajst-16340	157	37	weights	weight	NOUN
ajst-16340	157	38	.	.	PUNCT
ajst-16340	158	1	furthermore	furthermore	ADV
ajst-16340	158	2	,	,	PUNCT
ajst-16340	158	3	multimodal	multimodal	ADJ
ajst-16340	158	4	fusion	fusion	NOUN
ajst-16340	158	5	and	and	CCONJ
ajst-16340	158	6	cross	cross	ADJ
ajst-16340	158	7	-	-	ADJ
ajst-16340	158	8	domain	domain	ADJ
ajst-16340	158	9	applications	application	NOUN
ajst-16340	158	10	are	be	AUX
ajst-16340	158	11	essential	essential	ADJ
ajst-16340	158	12	directions	direction	NOUN
ajst-16340	158	13	for	for	ADP
ajst-16340	158	14	the	the	DET
ajst-16340	158	15	development	development	NOUN
ajst-16340	158	16	of	of	ADP
ajst-16340	158	17	deep	deep	ADJ
ajst-16340	158	18	learning	learning	NOUN
ajst-16340	158	19	technology	technology	NOUN
ajst-16340	158	20	.	.	PUNCT
ajst-16340	159	1	the	the	DET
ajst-16340	159	2	fusion	fusion	NOUN
ajst-16340	159	3	of	of	ADP
ajst-16340	159	4	deep	deep	ADJ
ajst-16340	159	5	learning	learning	NOUN
ajst-16340	159	6	and	and	CCONJ
ajst-16340	159	7	convolutional	convolutional	ADJ
ajst-16340	159	8	neural	neural	ADJ
ajst-16340	159	9	networks	network	NOUN
ajst-16340	159	10	allows	allow	VERB
ajst-16340	159	11	for	for	ADP
ajst-16340	159	12	more	more	ADV
ajst-16340	159	13	flexible	flexible	ADJ
ajst-16340	159	14	processing	processing	NOUN
ajst-16340	159	15	of	of	ADP
ajst-16340	159	16	data	datum	NOUN
ajst-16340	159	17	in	in	ADP
ajst-16340	159	18	different	different	ADJ
ajst-16340	159	19	domains	domain	NOUN
ajst-16340	159	20	,	,	PUNCT
ajst-16340	159	21	such	such	ADJ
ajst-16340	159	22	as	as	ADP
ajst-16340	159	23	images	image	NOUN
ajst-16340	159	24	,	,	PUNCT
ajst-16340	159	25	text	text	NOUN
ajst-16340	159	26	,	,	PUNCT
ajst-16340	159	27	and	and	CCONJ
ajst-16340	159	28	speech	speech	NOUN
ajst-16340	159	29	,	,	PUNCT
ajst-16340	159	30	achieving	achieve	VERB
ajst-16340	159	31	significant	significant	ADJ
ajst-16340	159	32	results	result	NOUN
ajst-16340	159	33	and	and	CCONJ
ajst-16340	159	34	providing	provide	VERB
ajst-16340	159	35	possibilities	possibility	NOUN
ajst-16340	159	36	for	for	ADP
ajst-16340	159	37	the	the	DET
ajst-16340	159	38	widespread	widespread	ADJ
ajst-16340	159	39	application	application	NOUN
ajst-16340	159	40	of	of	ADP
ajst-16340	159	41	deep	deep	ADJ
ajst-16340	159	42	learning	learning	NOUN
ajst-16340	159	43	in	in	ADP
ajst-16340	159	44	practical	practical	ADJ
ajst-16340	159	45	scenarios	scenario	NOUN
ajst-16340	159	46	.	.	PUNCT
ajst-16340	160	1	however	however	ADV
ajst-16340	160	2	,	,	PUNCT
ajst-16340	160	3	challenges	challenge	NOUN
ajst-16340	160	4	remain	remain	VERB
ajst-16340	160	5	in	in	ADP
ajst-16340	160	6	the	the	DET
ajst-16340	160	7	future	future	NOUN
ajst-16340	160	8	,	,	PUNCT
ajst-16340	160	9	such	such	ADJ
ajst-16340	160	10	as	as	ADP
ajst-16340	160	11	model	model	NOUN
ajst-16340	160	12	interpretability	interpretability	NOUN
ajst-16340	160	13	,	,	PUNCT
ajst-16340	160	14	data	datum	NOUN
ajst-16340	160	15	privacy	privacy	NOUN
ajst-16340	160	16	issues	issue	NOUN
ajst-16340	160	17	,	,	PUNCT
ajst-16340	160	18	and	and	CCONJ
ajst-16340	160	19	computational	computational	ADJ
ajst-16340	160	20	resource	resource	NOUN
ajst-16340	160	21	requirements	requirement	NOUN
ajst-16340	160	22	.	.	PUNCT
ajst-16340	161	1	future	future	ADJ
ajst-16340	161	2	research	research	NOUN
ajst-16340	161	3	needs	need	VERB
ajst-16340	161	4	to	to	PART
ajst-16340	161	5	focus	focus	VERB
ajst-16340	161	6	on	on	ADP
ajst-16340	161	7	the	the	DET
ajst-16340	161	8	interpretability	interpretability	NOUN
ajst-16340	161	9	of	of	ADP
ajst-16340	161	10	deep	deep	ADJ
ajst-16340	161	11	learning	learning	NOUN
ajst-16340	161	12	models	model	NOUN
ajst-16340	161	13	,	,	PUNCT
ajst-16340	161	14	develop	develop	VERB
ajst-16340	161	15	more	more	ADV
ajst-16340	161	16	robust	robust	ADJ
ajst-16340	161	17	data	datum	NOUN
ajst-16340	161	18	privacy	privacy	NOUN
ajst-16340	161	19	protection	protection	NOUN
ajst-16340	161	20	strategies	strategy	NOUN
ajst-16340	161	21	,	,	PUNCT
ajst-16340	161	22	and	and	CCONJ
ajst-16340	161	23	strive	strive	VERB
ajst-16340	161	24	to	to	PART
ajst-16340	161	25	develop	develop	VERB
ajst-16340	161	26	more	more	ADV
ajst-16340	161	27	efficient	efficient	ADJ
ajst-16340	161	28	computing	computing	NOUN
ajst-16340	161	29	models	model	NOUN
ajst-16340	161	30	.	.	PUNCT
ajst-16340	162	1	overall	overall	ADJ
ajst-16340	162	2	,	,	PUNCT
ajst-16340	162	3	as	as	ADP
ajst-16340	162	4	a	a	DET
ajst-16340	162	5	core	core	NOUN
ajst-16340	162	6	technology	technology	NOUN
ajst-16340	162	7	in	in	ADP
ajst-16340	162	8	artificial	artificial	ADJ
ajst-16340	162	9	intelligence	intelligence	NOUN
ajst-16340	162	10	,	,	PUNCT
ajst-16340	162	11	the	the	DET
ajst-16340	162	12	future	future	ADJ
ajst-16340	162	13	development	development	NOUN
ajst-16340	162	14	prospects	prospect	NOUN
ajst-16340	162	15	of	of	ADP
ajst-16340	162	16	deep	deep	ADJ
ajst-16340	162	17	learning	learning	NOUN
ajst-16340	162	18	are	be	AUX
ajst-16340	162	19	full	full	ADJ
ajst-16340	162	20	of	of	ADP
ajst-16340	162	21	infinite	infinite	ADJ
ajst-16340	162	22	possibilities	possibility	NOUN
ajst-16340	162	23	.	.	PUNCT
ajst-16340	163	1	through	through	ADP
ajst-16340	163	2	continuous	continuous	ADJ
ajst-16340	163	3	innovation	innovation	NOUN
ajst-16340	163	4	and	and	CCONJ
ajst-16340	163	5	efforts	effort	NOUN
ajst-16340	163	6	,	,	PUNCT
ajst-16340	163	7	we	we	PRON
ajst-16340	163	8	are	be	AUX
ajst-16340	163	9	confident	confident	ADJ
ajst-16340	163	10	in	in	ADP
ajst-16340	163	11	welcoming	welcome	VERB
ajst-16340	163	12	broader	broad	ADJ
ajst-16340	163	13	development	development	NOUN
ajst-16340	163	14	opportunities	opportunity	NOUN
ajst-16340	163	15	in	in	ADP
ajst-16340	163	16	the	the	DET
ajst-16340	163	17	field	field	NOUN
ajst-16340	163	18	of	of	ADP
ajst-16340	163	19	deep	deep	ADJ
ajst-16340	163	20	learning	learning	NOUN
ajst-16340	163	21	and	and	CCONJ
ajst-16340	163	22	making	make	VERB
ajst-16340	163	23	greater	great	ADJ
ajst-16340	163	24	contributions	contribution	NOUN
ajst-16340	163	25	.	.	PUNCT
ajst-16340	164	1	references	reference	NOUN
ajst-16340	164	2	[	[	X
ajst-16340	164	3	1	1	X
ajst-16340	164	4	]	]	PUNCT
ajst-16340	164	5	yang	yang	PROPN
ajst-16340	164	6	s	s	PROPN
ajst-16340	164	7	,	,	PUNCT
ajst-16340	164	8	luo	luo	PROPN
ajst-16340	164	9	l	l	PROPN
ajst-16340	164	10	,	,	PUNCT
ajst-16340	164	11	tan	tan	PROPN
ajst-16340	164	12	b.	b.	PROPN
ajst-16340	164	13	research	research	PROPN
ajst-16340	164	14	on	on	ADP
ajst-16340	164	15	sports	sport	NOUN
ajst-16340	164	16	performance	performance	NOUN
ajst-16340	164	17	prediction	prediction	NOUN
ajst-16340	164	18	based	base	VERB
ajst-16340	164	19	on	on	ADP
ajst-16340	164	20	bp	bp	PROPN
ajst-16340	164	21	neural	neural	PROPN
ajst-16340	164	22	network[j	network[j	PROPN
ajst-16340	164	23	]	]	PUNCT
ajst-16340	164	24	.	.	PUNCT
ajst-16340	165	1	mobile	mobile	ADJ
ajst-16340	165	2	information	information	NOUN
ajst-16340	165	3	systems	system	NOUN
ajst-16340	165	4	,	,	PUNCT
ajst-16340	165	5	2021,2021:1	2021,2021:1	PROPN
ajst-16340	165	6	-	-	PUNCT
ajst-16340	165	7	8	8	NUM
ajst-16340	165	8	.	.	PUNCT
ajst-16340	166	1	[	[	X
ajst-16340	166	2	2	2	NUM
ajst-16340	166	3	]	]	X
ajst-16340	166	4	bai	bai	PROPN
ajst-16340	166	5	y	y	PROPN
ajst-16340	166	6	,	,	PUNCT
ajst-16340	166	7	luo	luo	PROPN
ajst-16340	166	8	m	m	PROPN
ajst-16340	166	9	,	,	PUNCT
ajst-16340	166	10	pang	pang	PROPN
ajst-16340	166	11	f.	f.	PROPN
ajst-16340	166	12	an	an	DET
ajst-16340	166	13	algorithm	algorithm	NOUN
ajst-16340	166	14	for	for	ADP
ajst-16340	166	15	solving	solve	VERB
ajst-16340	166	16	robot	robot	NOUN
ajst-16340	166	17	inverse	inverse	NOUN
ajst-16340	166	18	kinematics	kinematic	NOUN
ajst-16340	166	19	based	base	VERB
ajst-16340	166	20	on	on	ADP
ajst-16340	166	21	foa	foa	PROPN
ajst-16340	166	22	optimized	optimize	VERB
ajst-16340	166	23	bp	bp	PROPN
ajst-16340	166	24	neural	neural	PROPN
ajst-16340	166	25	network[j	network[j	PROPN
ajst-16340	166	26	]	]	PUNCT
ajst-16340	166	27	.	.	PUNCT
ajst-16340	167	1	applied	apply	VERB
ajst-16340	167	2	sciences	sciences	PROPN
ajst-16340	167	3	,	,	PUNCT
ajst-16340	167	4	2021,11(15):7129	2021,11(15):7129	NUM
ajst-16340	167	5	.	.	PUNCT
ajst-16340	168	1	[	[	X
ajst-16340	168	2	3	3	X
ajst-16340	168	3	]	]	X
ajst-16340	168	4	liu	liu	PROPN
ajst-16340	168	5	y	y	PROPN
ajst-16340	168	6	,	,	PUNCT
ajst-16340	168	7	dai	dai	PROPN
ajst-16340	168	8	j	j	PROPN
ajst-16340	168	9	,	,	PUNCT
ajst-16340	168	10	zhao	zhao	PROPN
ajst-16340	168	11	s	s	PROPN
ajst-16340	168	12	,	,	PUNCT
ajst-16340	168	13	et	et	PROPN
ajst-16340	168	14	al	al	PROPN
ajst-16340	168	15	.	.	PUNCT
ajst-16340	169	1	a	a	DET
ajst-16340	169	2	bidirectional	bidirectional	ADJ
ajst-16340	169	3	reflectance	reflectance	NOUN
ajst-16340	169	4	distribution	distribution	NOUN
ajst-16340	169	5	function	function	NOUN
ajst-16340	169	6	model	model	NOUN
ajst-16340	169	7	of	of	ADP
ajst-16340	169	8	space	space	NOUN
ajst-16340	169	9	targets	target	NOUN
ajst-16340	169	10	in	in	ADP
ajst-16340	169	11	visible	visible	ADJ
ajst-16340	169	12	spectrum	spectrum	NOUN
ajst-16340	169	13	based	base	VERB
ajst-16340	169	14	on	on	ADP
ajst-16340	169	15	ga	ga	PROPN
ajst-16340	169	16	-	-	PUNCT
ajst-16340	169	17	bp	bp	PROPN
ajst-16340	169	18	network[j	network[j	PROPN
ajst-16340	169	19	]	]	PUNCT
ajst-16340	169	20	.	.	PUNCT
ajst-16340	170	1	applied	apply	VERB
ajst-16340	170	2	physics	physics	PROPN
ajst-16340	170	3	b	b	PROPN
ajst-16340	170	4	,	,	PUNCT
ajst-16340	170	5	2020,126(6	2020,126(6	NUM
ajst-16340	170	6	)	)	PUNCT
ajst-16340	170	7	.	.	PUNCT
ajst-16340	171	1	[	[	X
ajst-16340	171	2	4	4	X
ajst-16340	171	3	]	]	X
ajst-16340	171	4	yang	yang	PROPN
ajst-16340	171	5	j	j	PROPN
ajst-16340	171	6	,	,	PUNCT
ajst-16340	171	7	hu	hu	PROPN
ajst-16340	171	8	y	y	PROPN
ajst-16340	171	9	,	,	PUNCT
ajst-16340	171	10	zhang	zhang	PROPN
ajst-16340	171	11	k	k	PROPN
ajst-16340	171	12	,	,	PUNCT
ajst-16340	171	13	et	et	PROPN
ajst-16340	171	14	al	al	PROPN
ajst-16340	171	15	.	.	PUNCT
ajst-16340	172	1	an	an	DET
ajst-16340	172	2	improved	improve	VERB
ajst-16340	172	3	evolution	evolution	NOUN
ajst-16340	172	4	algorithm	algorithm	NOUN
ajst-16340	172	5	using	use	VERB
ajst-16340	172	6	population	population	NOUN
ajst-16340	172	7	competition	competition	NOUN
ajst-16340	172	8	genetic	genetic	ADJ
ajst-16340	172	9	algorithm	algorithm	NOUN
ajst-16340	172	10	and	and	CCONJ
ajst-16340	172	11	selfcorrection	selfcorrection	NOUN
ajst-16340	172	12	bp	bp	PROPN
ajst-16340	172	13	neural	neural	PROPN
ajst-16340	172	14	network	network	NOUN
ajst-16340	172	15	based	base	VERB
ajst-16340	172	16	on	on	ADP
ajst-16340	172	17	fitness	fitness	NOUN
ajst-16340	172	18	landscape[j	landscape[j	PROPN
ajst-16340	172	19	]	]	PUNCT
ajst-16340	172	20	.	.	PUNCT
ajst-16340	173	1	soft	soft	ADJ
ajst-16340	173	2	computing	computing	NOUN
ajst-16340	173	3	,	,	PUNCT
ajst-16340	173	4	2021,25(3):1751	2021,25(3):1751	NUM
ajst-16340	173	5	-	-	PUNCT
ajst-16340	173	6	1776	1776	NUM
ajst-16340	173	7	.	.	PUNCT
ajst-16340	174	1	[	[	X
ajst-16340	174	2	5	5	NUM
ajst-16340	174	3	]	]	X
ajst-16340	174	4	quan	quan	NOUN
ajst-16340	174	5	g	g	PROPN
ajst-16340	174	6	,	,	PUNCT
ajst-16340	174	7	zhang	zhang	PROPN
ajst-16340	174	8	y	y	PROPN
ajst-16340	174	9	,	,	PUNCT
ajst-16340	174	10	lei	lei	PROPN
ajst-16340	174	11	s	s	PROPN
ajst-16340	174	12	,	,	PUNCT
ajst-16340	174	13	et	et	PROPN
ajst-16340	174	14	al	al	PROPN
ajst-16340	174	15	.	.	PUNCT
ajst-16340	174	16	characterization	characterization	NOUN
ajst-16340	174	17	of	of	ADP
ajst-16340	174	18	flow	flow	NOUN
ajst-16340	174	19	behaviors	behavior	NOUN
ajst-16340	174	20	by	by	ADP
ajst-16340	174	21	a	a	DET
ajst-16340	174	22	pso	pso	NOUN
ajst-16340	174	23	-	-	PUNCT
ajst-16340	174	24	bp	bp	PROPN
ajst-16340	174	25	integrated	integrate	VERB
ajst-16340	174	26	model	model	NOUN
ajst-16340	174	27	for	for	ADP
ajst-16340	174	28	a	a	DET
ajst-16340	174	29	medium	medium	ADJ
ajst-16340	174	30	carbon	carbon	NOUN
ajst-16340	174	31	alloy	alloy	NOUN
ajst-16340	174	32	steel[j	steel[j	NOUN
ajst-16340	174	33	]	]	X
ajst-16340	174	34	.	.	PUNCT
ajst-16340	175	1	materials	material	NOUN
ajst-16340	175	2	,	,	PUNCT
ajst-16340	175	3	2023,16(8):2982	2023,16(8):2982	NUM
ajst-16340	175	4	.	.	PUNCT