id	sid	tid	token	lemma	pos
fcis-29257	1	1	frontiers	frontier	NOUN
fcis-29257	1	2	in	in	ADP
fcis-29257	1	3	computing	computing	NOUN
fcis-29257	1	4	and	and	CCONJ
fcis-29257	1	5	intelligent	intelligent	ADJ
fcis-29257	1	6	systems	system	NOUN
fcis-29257	1	7	issn	issn	VERB
fcis-29257	1	8	:	:	PUNCT
fcis-29257	1	9	2832	2832	NUM
fcis-29257	1	10	-	-	SYM
fcis-29257	1	11	6024	6024	NUM
fcis-29257	1	12	|	|	NOUN
fcis-29257	1	13	vol	vol	NOUN
fcis-29257	1	14	.	.	PROPN
fcis-29257	2	1	11	11	NUM
fcis-29257	2	2	,	,	PUNCT
fcis-29257	2	3	no	no	INTJ
fcis-29257	2	4	.	.	NOUN
fcis-29257	2	5	1	1	NUM
fcis-29257	2	6	,	,	PUNCT
fcis-29257	2	7	2025	2025	NUM
fcis-29257	2	8	59	59	NUM
fcis-29257	2	9	bimodal	bimodal	NOUN
fcis-29257	2	10	emotion	emotion	NOUN
fcis-29257	2	11	recognition	recognition	NOUN
fcis-29257	2	12	based	base	VERB
fcis-29257	2	13	on	on	ADP
fcis-29257	2	14	sichuan	sichuan	PROPN
fcis-29257	2	15	dialect	dialect	PROPN
fcis-29257	2	16	jia	jia	PROPN
fcis-29257	2	17	wei	wei	PROPN
fcis-29257	2	18	*	*	PROPN
fcis-29257	2	19	,	,	PUNCT
fcis-29257	2	20	xiangguo	xiangguo	PROPN
fcis-29257	2	21	sun	sun	PROPN
fcis-29257	2	22	mechanical	mechanical	ADJ
fcis-29257	2	23	engineering	engineering	PROPN
fcis-29257	2	24	college	college	PROPN
fcis-29257	2	25	,	,	PUNCT
fcis-29257	2	26	sichuan	sichuan	PROPN
fcis-29257	2	27	university	university	PROPN
fcis-29257	2	28	of	of	ADP
fcis-29257	2	29	science	science	NOUN
fcis-29257	2	30	and	and	CCONJ
fcis-29257	2	31	engineering	engineering	NOUN
fcis-29257	2	32	,	,	PUNCT
fcis-29257	2	33	yibin	yibin	PROPN
fcis-29257	2	34	,	,	PUNCT
fcis-29257	2	35	sichuan	sichuan	PROPN
fcis-29257	2	36	,	,	PUNCT
fcis-29257	2	37	china	china	PROPN
fcis-29257	2	38	*	*	PUNCT
fcis-29257	2	39	corresponding	correspond	VERB
fcis-29257	2	40	author	author	NOUN
fcis-29257	2	41	:	:	PUNCT
fcis-29257	2	42	jia	jia	PROPN
fcis-29257	2	43	wei	wei	PROPN
fcis-29257	2	44	(	(	PUNCT
fcis-29257	2	45	email	email	NOUN
fcis-29257	2	46	:	:	PUNCT
fcis-29257	2	47	18040480929@163.com	18040480929@163.com	NUM
fcis-29257	2	48	)	)	PUNCT
fcis-29257	2	49	abstract	abstract	NOUN
fcis-29257	2	50	:	:	PUNCT
fcis-29257	2	51	in	in	ADP
fcis-29257	2	52	view	view	NOUN
fcis-29257	2	53	of	of	ADP
fcis-29257	2	54	the	the	DET
fcis-29257	2	55	main	main	ADJ
fcis-29257	2	56	technical	technical	ADJ
fcis-29257	2	57	challenges	challenge	NOUN
fcis-29257	2	58	faced	face	VERB
fcis-29257	2	59	by	by	ADP
fcis-29257	2	60	dialect	dialect	NOUN
fcis-29257	2	61	emotion	emotion	NOUN
fcis-29257	2	62	recognition	recognition	NOUN
fcis-29257	2	63	(	(	PUNCT
fcis-29257	2	64	data	datum	NOUN
fcis-29257	2	65	scarcity	scarcity	NOUN
fcis-29257	2	66	and	and	CCONJ
fcis-29257	2	67	low	low	ADJ
fcis-29257	2	68	recognition	recognition	NOUN
fcis-29257	2	69	rate	rate	NOUN
fcis-29257	2	70	)	)	PUNCT
fcis-29257	2	71	,	,	PUNCT
fcis-29257	2	72	this	this	DET
fcis-29257	2	73	paper	paper	NOUN
fcis-29257	2	74	discusses	discuss	VERB
fcis-29257	2	75	the	the	DET
fcis-29257	2	76	sichuan	sichuan	PROPN
fcis-29257	2	77	dialect	dialect	PROPN
fcis-29257	2	78	emotion	emotion	NOUN
fcis-29257	2	79	recognition	recognition	NOUN
fcis-29257	2	80	technology	technology	NOUN
fcis-29257	2	81	,	,	PUNCT
fcis-29257	2	82	and	and	CCONJ
fcis-29257	2	83	proposes	propose	VERB
fcis-29257	2	84	a	a	DET
fcis-29257	2	85	multimodal	multimodal	ADJ
fcis-29257	2	86	emotion	emotion	NOUN
fcis-29257	2	87	recognition	recognition	NOUN
fcis-29257	2	88	model	model	NOUN
fcis-29257	2	89	by	by	ADP
fcis-29257	2	90	constructing	construct	VERB
fcis-29257	2	91	a	a	DET
fcis-29257	2	92	high	high	ADJ
fcis-29257	2	93	-	-	PUNCT
fcis-29257	2	94	quality	quality	NOUN
fcis-29257	2	95	dataset	dataset	NOUN
fcis-29257	2	96	containing	contain	VERB
fcis-29257	2	97	multiple	multiple	ADJ
fcis-29257	2	98	emotion	emotion	NOUN
fcis-29257	2	99	categories	category	NOUN
fcis-29257	2	100	.	.	PUNCT
fcis-29257	3	1	the	the	DET
fcis-29257	3	2	model	model	NOUN
fcis-29257	3	3	adopts	adopt	VERB
fcis-29257	3	4	the	the	DET
fcis-29257	3	5	dual	dual	ADJ
fcis-29257	3	6	-	-	PUNCT
fcis-29257	3	7	modal	modal	NOUN
fcis-29257	3	8	fusion	fusion	NOUN
fcis-29257	3	9	strategy	strategy	NOUN
fcis-29257	3	10	of	of	ADP
fcis-29257	3	11	mel	mel	PROPN
fcis-29257	3	12	spectral	spectral	PROPN
fcis-29257	3	13	features	feature	NOUN
fcis-29257	3	14	(	(	PUNCT
fcis-29257	3	15	mfccs	mfccs	PROPN
fcis-29257	3	16	)	)	PUNCT
fcis-29257	3	17	and	and	CCONJ
fcis-29257	3	18	text	text	NOUN
fcis-29257	3	19	features	feature	NOUN
fcis-29257	3	20	,	,	PUNCT
fcis-29257	3	21	uses	use	VERB
fcis-29257	3	22	dynamic	dynamic	ADJ
fcis-29257	3	23	convolutional	convolutional	ADJ
fcis-29257	3	24	network	network	NOUN
fcis-29257	3	25	(	(	PUNCT
fcis-29257	3	26	odconv	odconv	NOUN
fcis-29257	3	27	)	)	PUNCT
fcis-29257	3	28	and	and	CCONJ
fcis-29257	3	29	convolutional	convolutional	ADJ
fcis-29257	3	30	block	block	NOUN
fcis-29257	3	31	attention	attention	NOUN
fcis-29257	3	32	module	module	NOUN
fcis-29257	3	33	(	(	PUNCT
fcis-29257	3	34	cbam	cbam	NOUN
fcis-29257	3	35	)	)	PUNCT
fcis-29257	3	36	to	to	PART
fcis-29257	3	37	extract	extract	VERB
fcis-29257	3	38	features	feature	NOUN
fcis-29257	3	39	,	,	PUNCT
fcis-29257	3	40	and	and	CCONJ
fcis-29257	3	41	combines	combine	VERB
fcis-29257	3	42	the	the	DET
fcis-29257	3	43	text	text	NOUN
fcis-29257	3	44	-	-	PUNCT
fcis-29257	3	45	cnn	cnn	PROPN
fcis-29257	3	46	model	model	NOUN
fcis-29257	3	47	for	for	ADP
fcis-29257	3	48	text	text	NOUN
fcis-29257	3	49	sentiment	sentiment	NOUN
fcis-29257	3	50	analysis	analysis	NOUN
fcis-29257	3	51	.	.	PUNCT
fcis-29257	4	1	experimental	experimental	ADJ
fcis-29257	4	2	results	result	NOUN
fcis-29257	4	3	show	show	VERB
fcis-29257	4	4	that	that	SCONJ
fcis-29257	4	5	the	the	DET
fcis-29257	4	6	proposed	propose	VERB
fcis-29257	4	7	model	model	NOUN
fcis-29257	4	8	has	have	VERB
fcis-29257	4	9	higher	high	ADJ
fcis-29257	4	10	accuracy	accuracy	NOUN
fcis-29257	4	11	and	and	CCONJ
fcis-29257	4	12	robustness	robustness	NOUN
fcis-29257	4	13	than	than	ADP
fcis-29257	4	14	the	the	DET
fcis-29257	4	15	traditional	traditional	ADJ
fcis-29257	4	16	speech	speech	NOUN
fcis-29257	4	17	recognition	recognition	NOUN
fcis-29257	4	18	model	model	PROPN
fcis-29257	4	19	cnn	cnn	PROPN
fcis-29257	4	20	in	in	ADP
fcis-29257	4	21	sichuan	sichuan	PROPN
fcis-29257	4	22	dialect	dialect	PROPN
fcis-29257	4	23	emotion	emotion	NOUN
fcis-29257	4	24	recognition	recognition	NOUN
fcis-29257	4	25	task	task	NOUN
fcis-29257	4	26	.	.	PUNCT
fcis-29257	5	1	keywords	keyword	NOUN
fcis-29257	5	2	:	:	PUNCT
fcis-29257	5	3	dynamic	dynamic	ADJ
fcis-29257	5	4	attention	attention	NOUN
fcis-29257	5	5	;	;	PUNCT
fcis-29257	5	6	channel	channel	NOUN
fcis-29257	5	7	spatial	spatial	ADJ
fcis-29257	5	8	attention	attention	NOUN
fcis-29257	5	9	;	;	PUNCT
fcis-29257	5	10	multimodal	multimodal	ADJ
fcis-29257	5	11	fusion	fusion	NOUN
fcis-29257	5	12	;	;	PUNCT
fcis-29257	5	13	cbam	cbam	NOUN
fcis-29257	5	14	.	.	PUNCT
fcis-29257	6	1	1	1	X
fcis-29257	6	2	.	.	X
fcis-29257	6	3	introduction	introduction	NOUN
fcis-29257	6	4	with	with	ADP
fcis-29257	6	5	the	the	DET
fcis-29257	6	6	rapid	rapid	ADJ
fcis-29257	6	7	development	development	NOUN
fcis-29257	6	8	of	of	ADP
fcis-29257	6	9	artificial	artificial	ADJ
fcis-29257	6	10	intelligence	intelligence	NOUN
fcis-29257	6	11	technology	technology	NOUN
fcis-29257	6	12	,	,	PUNCT
fcis-29257	6	13	the	the	DET
fcis-29257	6	14	ways	way	NOUN
fcis-29257	6	15	in	in	ADP
fcis-29257	6	16	which	which	PRON
fcis-29257	6	17	humans	human	NOUN
fcis-29257	6	18	interact	interact	VERB
fcis-29257	6	19	with	with	ADP
fcis-29257	6	20	intelligent	intelligent	ADJ
fcis-29257	6	21	systems	system	NOUN
fcis-29257	6	22	have	have	AUX
fcis-29257	6	23	become	become	VERB
fcis-29257	6	24	more	more	ADV
fcis-29257	6	25	diverse	diverse	ADJ
fcis-29257	6	26	.	.	PUNCT
fcis-29257	7	1	in	in	ADP
fcis-29257	7	2	this	this	DET
fcis-29257	7	3	context	context	NOUN
fcis-29257	7	4	,	,	PUNCT
fcis-29257	7	5	the	the	DET
fcis-29257	7	6	application	application	NOUN
fcis-29257	7	7	value	value	NOUN
fcis-29257	7	8	of	of	ADP
fcis-29257	7	9	emotion	emotion	NOUN
fcis-29257	7	10	recognition	recognition	NOUN
fcis-29257	7	11	technology	technology	NOUN
fcis-29257	7	12	is	be	AUX
fcis-29257	7	13	becoming	become	VERB
fcis-29257	7	14	more	more	ADV
fcis-29257	7	15	and	and	CCONJ
fcis-29257	7	16	more	more	ADV
fcis-29257	7	17	prominent	prominent	ADJ
fcis-29257	7	18	,	,	PUNCT
fcis-29257	7	19	especially	especially	ADV
fcis-29257	7	20	for	for	ADP
fcis-29257	7	21	the	the	DET
fcis-29257	7	22	recognition	recognition	NOUN
fcis-29257	7	23	of	of	ADP
fcis-29257	7	24	dialect	dialect	ADJ
fcis-29257	7	25	emotion	emotion	NOUN
fcis-29257	7	26	.	.	PUNCT
fcis-29257	8	1	the	the	DET
fcis-29257	8	2	integration	integration	NOUN
fcis-29257	8	3	of	of	ADP
fcis-29257	8	4	speech	speech	NOUN
fcis-29257	8	5	recognition	recognition	NOUN
fcis-29257	8	6	and	and	CCONJ
fcis-29257	8	7	natural	natural	ADJ
fcis-29257	8	8	language	language	NOUN
fcis-29257	8	9	processing	processing	NOUN
fcis-29257	8	10	technology	technology	NOUN
fcis-29257	8	11	,	,	PUNCT
fcis-29257	8	12	especially	especially	ADV
fcis-29257	8	13	the	the	DET
fcis-29257	8	14	breakthrough	breakthrough	NOUN
fcis-29257	8	15	in	in	ADP
fcis-29257	8	16	the	the	DET
fcis-29257	8	17	field	field	NOUN
fcis-29257	8	18	of	of	ADP
fcis-29257	8	19	emotion	emotion	NOUN
fcis-29257	8	20	recognition	recognition	NOUN
fcis-29257	8	21	,	,	PUNCT
fcis-29257	8	22	provides	provide	VERB
fcis-29257	8	23	strong	strong	ADJ
fcis-29257	8	24	support	support	NOUN
fcis-29257	8	25	for	for	ADP
fcis-29257	8	26	personalized	personalized	ADJ
fcis-29257	8	27	services	service	NOUN
fcis-29257	8	28	in	in	ADP
fcis-29257	8	29	multiple	multiple	ADJ
fcis-29257	8	30	scenarios	scenario	NOUN
fcis-29257	8	31	.	.	PUNCT
fcis-29257	9	1	with	with	ADP
fcis-29257	9	2	the	the	DET
fcis-29257	9	3	rapid	rapid	ADJ
fcis-29257	9	4	rise	rise	NOUN
fcis-29257	9	5	of	of	ADP
fcis-29257	9	6	artificial	artificial	ADJ
fcis-29257	9	7	neural	neural	ADJ
fcis-29257	9	8	networks	network	NOUN
fcis-29257	9	9	,	,	PUNCT
fcis-29257	9	10	especially	especially	ADV
fcis-29257	9	11	deep	deep	ADJ
fcis-29257	9	12	learning	learning	NOUN
fcis-29257	9	13	technology	technology	NOUN
fcis-29257	9	14	,	,	PUNCT
fcis-29257	9	15	the	the	DET
fcis-29257	9	16	performance	performance	NOUN
fcis-29257	9	17	of	of	ADP
fcis-29257	9	18	emotion	emotion	NOUN
fcis-29257	9	19	recognition	recognition	NOUN
fcis-29257	9	20	models	model	NOUN
fcis-29257	9	21	has	have	AUX
fcis-29257	9	22	been	be	AUX
fcis-29257	9	23	greatly	greatly	ADV
fcis-29257	9	24	improved	improve	VERB
fcis-29257	9	25	,	,	PUNCT
fcis-29257	9	26	laying	lay	VERB
fcis-29257	9	27	a	a	DET
fcis-29257	9	28	solid	solid	ADJ
fcis-29257	9	29	foundation	foundation	NOUN
fcis-29257	9	30	for	for	ADP
fcis-29257	9	31	emotion	emotion	NOUN
fcis-29257	9	32	recognition	recognition	NOUN
fcis-29257	9	33	in	in	ADP
fcis-29257	9	34	more	more	ADJ
fcis-29257	9	35	complex	complex	ADJ
fcis-29257	9	36	language	language	NOUN
fcis-29257	9	37	environments	environment	NOUN
fcis-29257	9	38	.	.	PUNCT
fcis-29257	10	1	at	at	ADP
fcis-29257	10	2	present	present	ADJ
fcis-29257	10	3	,	,	PUNCT
fcis-29257	10	4	a	a	DET
fcis-29257	10	5	number	number	NOUN
fcis-29257	10	6	of	of	ADP
fcis-29257	10	7	well	well	ADV
fcis-29257	10	8	-	-	PUNCT
fcis-29257	10	9	known	know	VERB
fcis-29257	10	10	speech	speech	NOUN
fcis-29257	10	11	emotion	emotion	NOUN
fcis-29257	10	12	datasets	dataset	NOUN
fcis-29257	10	13	in	in	ADP
fcis-29257	10	14	the	the	DET
fcis-29257	10	15	world	world	NOUN
fcis-29257	10	16	have	have	AUX
fcis-29257	10	17	promoted	promote	VERB
fcis-29257	10	18	the	the	DET
fcis-29257	10	19	in	in	ADP
fcis-29257	10	20	-	-	PUNCT
fcis-29257	10	21	depth	depth	NOUN
fcis-29257	10	22	development	development	NOUN
fcis-29257	10	23	of	of	ADP
fcis-29257	10	24	research	research	NOUN
fcis-29257	10	25	.	.	PUNCT
fcis-29257	11	1	however	however	ADV
fcis-29257	11	2	,	,	PUNCT
fcis-29257	11	3	much	much	ADJ
fcis-29257	11	4	research	research	NOUN
fcis-29257	11	5	has	have	AUX
fcis-29257	11	6	focused	focus	VERB
fcis-29257	11	7	on	on	ADP
fcis-29257	11	8	mandarin	mandarin	NOUN
fcis-29257	11	9	or	or	CCONJ
fcis-29257	11	10	the	the	DET
fcis-29257	11	11	international	international	ADJ
fcis-29257	11	12	lingua	lingua	PROPN
fcis-29257	11	13	franca	franca	NOUN
fcis-29257	11	14	,	,	PUNCT
fcis-29257	11	15	while	while	SCONJ
fcis-29257	11	16	the	the	DET
fcis-29257	11	17	study	study	NOUN
fcis-29257	11	18	of	of	ADP
fcis-29257	11	19	dialects	dialect	NOUN
fcis-29257	11	20	has	have	AUX
fcis-29257	11	21	often	often	ADV
fcis-29257	11	22	been	be	AUX
fcis-29257	11	23	neglected	neglect	VERB
fcis-29257	11	24	.	.	PUNCT
fcis-29257	12	1	for	for	ADP
fcis-29257	12	2	some	some	DET
fcis-29257	12	3	specific	specific	ADJ
fcis-29257	12	4	scenarios	scenario	NOUN
fcis-29257	12	5	,	,	PUNCT
fcis-29257	12	6	such	such	ADJ
fcis-29257	12	7	as	as	ADP
fcis-29257	12	8	sichuan	sichuan	PROPN
fcis-29257	12	9	dialect	dialect	NOUN
fcis-29257	12	10	[	[	X
fcis-29257	12	11	1	1	NUM
fcis-29257	12	12	]	]	PUNCT
fcis-29257	12	13	,	,	PUNCT
fcis-29257	12	14	the	the	DET
fcis-29257	12	15	importance	importance	NOUN
fcis-29257	12	16	of	of	ADP
fcis-29257	12	17	emotion	emotion	NOUN
fcis-29257	12	18	recognition	recognition	NOUN
fcis-29257	12	19	lies	lie	VERB
fcis-29257	12	20	in	in	ADP
fcis-29257	12	21	the	the	DET
fcis-29257	12	22	fact	fact	NOUN
fcis-29257	12	23	that	that	SCONJ
fcis-29257	12	24	it	it	PRON
fcis-29257	12	25	can	can	AUX
fcis-29257	12	26	not	not	PART
fcis-29257	12	27	only	only	ADV
fcis-29257	12	28	promote	promote	VERB
fcis-29257	12	29	more	more	ADV
fcis-29257	12	30	efficient	efficient	ADJ
fcis-29257	12	31	cross	cross	NOUN
fcis-29257	12	32	-	-	NOUN
fcis-29257	12	33	language	language	NOUN
fcis-29257	12	34	and	and	CCONJ
fcis-29257	12	35	cross	cross	ADJ
fcis-29257	12	36	-	-	ADJ
fcis-29257	12	37	cultural	cultural	ADJ
fcis-29257	12	38	communication	communication	NOUN
fcis-29257	12	39	in	in	ADP
fcis-29257	12	40	sichuan	sichuan	PROPN
fcis-29257	12	41	,	,	PUNCT
fcis-29257	12	42	but	but	CCONJ
fcis-29257	12	43	also	also	ADV
fcis-29257	12	44	provide	provide	VERB
fcis-29257	12	45	more	more	ADV
fcis-29257	12	46	accurate	accurate	ADJ
fcis-29257	12	47	support	support	NOUN
fcis-29257	12	48	for	for	ADP
fcis-29257	12	49	local	local	ADJ
fcis-29257	12	50	social	social	ADJ
fcis-29257	12	51	needs	need	NOUN
fcis-29257	12	52	.	.	PUNCT
fcis-29257	13	1	the	the	DET
fcis-29257	13	2	main	main	ADJ
fcis-29257	13	3	content	content	NOUN
fcis-29257	13	4	of	of	ADP
fcis-29257	13	5	the	the	DET
fcis-29257	13	6	research	research	NOUN
fcis-29257	13	7	in	in	ADP
fcis-29257	13	8	this	this	DET
fcis-29257	13	9	dissertation	dissertation	NOUN
fcis-29257	13	10	:	:	PUNCT
fcis-29257	13	11	in	in	ADP
fcis-29257	13	12	view	view	NOUN
fcis-29257	13	13	of	of	ADP
fcis-29257	13	14	the	the	DET
fcis-29257	13	15	problems	problem	NOUN
fcis-29257	13	16	existing	exist	VERB
fcis-29257	13	17	in	in	ADP
fcis-29257	13	18	the	the	DET
fcis-29257	13	19	current	current	ADJ
fcis-29257	13	20	emotion	emotion	NOUN
fcis-29257	13	21	recognition	recognition	NOUN
fcis-29257	13	22	technology	technology	NOUN
fcis-29257	13	23	,	,	PUNCT
fcis-29257	13	24	this	this	DET
fcis-29257	13	25	paper	paper	NOUN
fcis-29257	13	26	conducts	conduct	VERB
fcis-29257	13	27	an	an	DET
fcis-29257	13	28	in	in	ADP
fcis-29257	13	29	-	-	PUNCT
fcis-29257	13	30	depth	depth	NOUN
fcis-29257	13	31	study	study	NOUN
fcis-29257	13	32	.	.	PUNCT
fcis-29257	14	1	the	the	DET
fcis-29257	14	2	main	main	ADJ
fcis-29257	14	3	research	research	NOUN
fcis-29257	14	4	contents	content	NOUN
fcis-29257	14	5	include	include	VERB
fcis-29257	14	6	the	the	DET
fcis-29257	14	7	following	follow	VERB
fcis-29257	14	8	aspects	aspect	NOUN
fcis-29257	14	9	:	:	PUNCT
fcis-29257	14	10	(	(	PUNCT
fcis-29257	14	11	1	1	X
fcis-29257	14	12	)	)	PUNCT
fcis-29257	14	13	phonetic	phonetic	ADJ
fcis-29257	14	14	dataset	dataset	NOUN
fcis-29257	14	15	construction	construction	NOUN
fcis-29257	14	16	:	:	PUNCT
fcis-29257	14	17	in	in	ADP
fcis-29257	14	18	order	order	NOUN
fcis-29257	14	19	to	to	PART
fcis-29257	14	20	overcome	overcome	VERB
fcis-29257	14	21	the	the	DET
fcis-29257	14	22	problem	problem	NOUN
fcis-29257	14	23	of	of	ADP
fcis-29257	14	24	insufficient	insufficient	ADJ
fcis-29257	14	25	database	database	NOUN
fcis-29257	14	26	,	,	PUNCT
fcis-29257	14	27	this	this	DET
fcis-29257	14	28	paper	paper	NOUN
fcis-29257	14	29	collects	collect	VERB
fcis-29257	14	30	phonetic	phonetic	ADJ
fcis-29257	14	31	samples	sample	NOUN
fcis-29257	14	32	about	about	ADP
fcis-29257	14	33	sichuan	sichuan	PROPN
fcis-29257	14	34	dialects	dialect	NOUN
fcis-29257	14	35	,	,	PUNCT
fcis-29257	14	36	covering	cover	VERB
fcis-29257	14	37	a	a	DET
fcis-29257	14	38	variety	variety	NOUN
fcis-29257	14	39	of	of	ADP
fcis-29257	14	40	emotion	emotion	NOUN
fcis-29257	14	41	categories	category	NOUN
fcis-29257	14	42	.	.	PUNCT
fcis-29257	15	1	at	at	ADP
fcis-29257	15	2	the	the	DET
fcis-29257	15	3	same	same	ADJ
fcis-29257	15	4	time	time	NOUN
fcis-29257	15	5	,	,	PUNCT
fcis-29257	15	6	speech	speech	NOUN
fcis-29257	15	7	recognition	recognition	NOUN
fcis-29257	15	8	technology	technology	NOUN
fcis-29257	15	9	is	be	AUX
fcis-29257	15	10	used	use	VERB
fcis-29257	15	11	to	to	PART
fcis-29257	15	12	transcribe	transcribe	VERB
fcis-29257	15	13	speech	speech	NOUN
fcis-29257	15	14	into	into	ADP
fcis-29257	15	15	text	text	NOUN
fcis-29257	15	16	to	to	PART
fcis-29257	15	17	ensure	ensure	VERB
fcis-29257	15	18	that	that	SCONJ
fcis-29257	15	19	the	the	DET
fcis-29257	15	20	dataset	dataset	NOUN
fcis-29257	15	21	covers	cover	VERB
fcis-29257	15	22	a	a	DET
fcis-29257	15	23	wide	wide	ADJ
fcis-29257	15	24	range	range	NOUN
fcis-29257	15	25	of	of	ADP
fcis-29257	15	26	sentiment	sentiment	NOUN
fcis-29257	15	27	categories	category	NOUN
fcis-29257	15	28	and	and	CCONJ
fcis-29257	15	29	maintain	maintain	VERB
fcis-29257	15	30	the	the	DET
fcis-29257	15	31	consistency	consistency	NOUN
fcis-29257	15	32	of	of	ADP
fcis-29257	15	33	sentiment	sentiment	NOUN
fcis-29257	15	34	annotation	annotation	NOUN
fcis-29257	15	35	.	.	PUNCT
fcis-29257	16	1	this	this	DET
fcis-29257	16	2	step	step	NOUN
fcis-29257	16	3	provides	provide	VERB
fcis-29257	16	4	strong	strong	ADJ
fcis-29257	16	5	support	support	NOUN
fcis-29257	16	6	for	for	ADP
fcis-29257	16	7	subsequent	subsequent	ADJ
fcis-29257	16	8	emotional	emotional	ADJ
fcis-29257	16	9	feature	feature	NOUN
fcis-29257	16	10	extraction	extraction	NOUN
fcis-29257	16	11	and	and	CCONJ
fcis-29257	16	12	model	model	NOUN
fcis-29257	16	13	training	training	NOUN
fcis-29257	16	14	.	.	PUNCT
fcis-29257	17	1	(	(	PUNCT
fcis-29257	17	2	2	2	X
fcis-29257	17	3	)	)	PUNCT
fcis-29257	17	4	emotion	emotion	NOUN
fcis-29257	17	5	recognition	recognition	NOUN
fcis-29257	17	6	model	model	NOUN
fcis-29257	17	7	design	design	NOUN
fcis-29257	17	8	:	:	PUNCT
fcis-29257	17	9	most	most	ADJ
fcis-29257	17	10	of	of	ADP
fcis-29257	17	11	the	the	DET
fcis-29257	17	12	existing	exist	VERB
fcis-29257	17	13	models	model	NOUN
fcis-29257	17	14	are	be	AUX
fcis-29257	17	15	trained	train	VERB
fcis-29257	17	16	based	base	VERB
fcis-29257	17	17	on	on	ADP
fcis-29257	17	18	putonghua	putonghua	PROPN
fcis-29257	17	19	or	or	CCONJ
fcis-29257	17	20	international	international	ADJ
fcis-29257	17	21	common	common	ADJ
fcis-29257	17	22	language	language	NOUN
fcis-29257	17	23	datasets	dataset	NOUN
fcis-29257	17	24	,	,	PUNCT
fcis-29257	17	25	which	which	PRON
fcis-29257	17	26	are	be	AUX
fcis-29257	17	27	difficult	difficult	ADJ
fcis-29257	17	28	to	to	PART
fcis-29257	17	29	be	be	AUX
fcis-29257	17	30	directly	directly	ADV
fcis-29257	17	31	applied	apply	VERB
fcis-29257	17	32	to	to	PART
fcis-29257	17	33	dialect	dialect	VERB
fcis-29257	17	34	emotion	emotion	NOUN
fcis-29257	17	35	recognition	recognition	NOUN
fcis-29257	17	36	tasks	task	NOUN
fcis-29257	17	37	.	.	PUNCT
fcis-29257	18	1	therefore	therefore	ADV
fcis-29257	18	2	,	,	PUNCT
fcis-29257	18	3	it	it	PRON
fcis-29257	18	4	is	be	AUX
fcis-29257	18	5	necessary	necessary	ADJ
fcis-29257	18	6	to	to	PART
fcis-29257	18	7	optimize	optimize	VERB
fcis-29257	18	8	and	and	CCONJ
fcis-29257	18	9	verify	verify	VERB
fcis-29257	18	10	the	the	DET
fcis-29257	18	11	model	model	NOUN
fcis-29257	18	12	for	for	ADP
fcis-29257	18	13	sichuan	sichuan	PROPN
fcis-29257	18	14	dialect	dialect	PROPN
fcis-29257	18	15	.	.	PUNCT
fcis-29257	19	1	in	in	ADP
fcis-29257	19	2	this	this	DET
fcis-29257	19	3	paper	paper	NOUN
fcis-29257	19	4	,	,	PUNCT
fcis-29257	19	5	the	the	DET
fcis-29257	19	6	current	current	ADJ
fcis-29257	19	7	efficient	efficient	ADJ
fcis-29257	19	8	network	network	NOUN
fcis-29257	19	9	model	model	NOUN
fcis-29257	19	10	is	be	AUX
fcis-29257	19	11	selected	select	VERB
fcis-29257	19	12	for	for	ADP
fcis-29257	19	13	feature	feature	NOUN
fcis-29257	19	14	extraction	extraction	NOUN
fcis-29257	19	15	,	,	PUNCT
fcis-29257	19	16	which	which	PRON
fcis-29257	19	17	is	be	AUX
fcis-29257	19	18	helpful	helpful	ADJ
fcis-29257	19	19	to	to	PART
fcis-29257	19	20	improve	improve	VERB
fcis-29257	19	21	the	the	DET
fcis-29257	19	22	accuracy	accuracy	NOUN
fcis-29257	19	23	and	and	CCONJ
fcis-29257	19	24	robustness	robustness	NOUN
fcis-29257	19	25	of	of	ADP
fcis-29257	19	26	emotion	emotion	NOUN
fcis-29257	19	27	recognition	recognition	NOUN
fcis-29257	19	28	.	.	PUNCT
fcis-29257	20	1	(	(	PUNCT
fcis-29257	20	2	3	3	X
fcis-29257	20	3	)	)	PUNCT
fcis-29257	20	4	aiming	aim	VERB
fcis-29257	20	5	at	at	ADP
fcis-29257	20	6	the	the	DET
fcis-29257	20	7	problem	problem	NOUN
fcis-29257	20	8	of	of	ADP
fcis-29257	20	9	low	low	ADJ
fcis-29257	20	10	recognition	recognition	NOUN
fcis-29257	20	11	rate	rate	NOUN
fcis-29257	20	12	of	of	ADP
fcis-29257	20	13	individual	individual	ADJ
fcis-29257	20	14	categories	category	NOUN
fcis-29257	20	15	:	:	PUNCT
fcis-29257	20	16	this	this	DET
fcis-29257	20	17	paper	paper	NOUN
fcis-29257	20	18	makes	make	VERB
fcis-29257	20	19	up	up	ADP
fcis-29257	20	20	for	for	ADP
fcis-29257	20	21	it	it	PRON
fcis-29257	20	22	by	by	ADP
fcis-29257	20	23	text	text	NOUN
fcis-29257	20	24	emotion	emotion	NOUN
fcis-29257	20	25	recognition	recognition	NOUN
fcis-29257	20	26	,	,	PUNCT
fcis-29257	20	27	and	and	CCONJ
fcis-29257	20	28	adopts	adopt	VERB
fcis-29257	20	29	a	a	DET
fcis-29257	20	30	dual	dual	ADJ
fcis-29257	20	31	-	-	PUNCT
fcis-29257	20	32	modal	modal	NOUN
fcis-29257	20	33	emotion	emotion	NOUN
fcis-29257	20	34	recognition	recognition	NOUN
fcis-29257	20	35	method	method	NOUN
fcis-29257	20	36	based	base	VERB
fcis-29257	20	37	on	on	ADP
fcis-29257	20	38	the	the	DET
fcis-29257	20	39	fusion	fusion	NOUN
fcis-29257	20	40	of	of	ADP
fcis-29257	20	41	speech	speech	NOUN
fcis-29257	20	42	and	and	CCONJ
fcis-29257	20	43	text	text	NOUN
fcis-29257	20	44	.	.	PUNCT
fcis-29257	21	1	by	by	ADP
fcis-29257	21	2	constructing	construct	VERB
fcis-29257	21	3	a	a	DET
fcis-29257	21	4	rich	rich	ADJ
fcis-29257	21	5	speech	speech	NOUN
fcis-29257	21	6	dataset	dataset	NOUN
fcis-29257	21	7	,	,	PUNCT
fcis-29257	21	8	extracting	extract	VERB
fcis-29257	21	9	comprehensive	comprehensive	ADJ
fcis-29257	21	10	emotional	emotional	ADJ
fcis-29257	21	11	features	feature	NOUN
fcis-29257	21	12	,	,	PUNCT
fcis-29257	21	13	and	and	CCONJ
fcis-29257	21	14	fusing	fuse	VERB
fcis-29257	21	15	multimodal	multimodal	NOUN
fcis-29257	21	16	information	information	NOUN
fcis-29257	21	17	,	,	PUNCT
fcis-29257	21	18	the	the	DET
fcis-29257	21	19	recognition	recognition	NOUN
fcis-29257	21	20	of	of	ADP
fcis-29257	21	21	emotions	emotion	NOUN
fcis-29257	21	22	is	be	AUX
fcis-29257	21	23	realized	realize	VERB
fcis-29257	21	24	.	.	PUNCT
fcis-29257	22	1	2	2	X
fcis-29257	22	2	.	.	X
fcis-29257	22	3	dataset	dataset	VERB
fcis-29257	22	4	the	the	DET
fcis-29257	22	5	speech	speech	NOUN
fcis-29257	22	6	emotion	emotion	NOUN
fcis-29257	22	7	corpus	corpus	NOUN
fcis-29257	22	8	is	be	AUX
fcis-29257	22	9	the	the	DET
fcis-29257	22	10	basis	basis	NOUN
fcis-29257	22	11	for	for	ADP
fcis-29257	22	12	speech	speech	NOUN
fcis-29257	22	13	emotion	emotion	NOUN
fcis-29257	22	14	recognition	recognition	NOUN
fcis-29257	22	15	,	,	PUNCT
fcis-29257	22	16	and	and	CCONJ
fcis-29257	22	17	the	the	DET
fcis-29257	22	18	size	size	NOUN
fcis-29257	22	19	and	and	CCONJ
fcis-29257	22	20	quality	quality	NOUN
fcis-29257	22	21	of	of	ADP
fcis-29257	22	22	the	the	DET
fcis-29257	22	23	database	database	NOUN
fcis-29257	22	24	directly	directly	ADV
fcis-29257	22	25	determine	determine	VERB
fcis-29257	22	26	the	the	DET
fcis-29257	22	27	performance	performance	NOUN
fcis-29257	22	28	of	of	ADP
fcis-29257	22	29	the	the	DET
fcis-29257	22	30	speech	speech	NOUN
fcis-29257	22	31	emotion	emotion	NOUN
fcis-29257	22	32	recognition	recognition	NOUN
fcis-29257	22	33	system	system	NOUN
fcis-29257	22	34	trained	train	VERB
fcis-29257	22	35	based	base	VERB
fcis-29257	22	36	on	on	ADP
fcis-29257	22	37	the	the	DET
fcis-29257	22	38	corpus	corpus	NOUN
fcis-29257	22	39	.	.	PUNCT
fcis-29257	23	1	at	at	ADP
fcis-29257	23	2	present	present	ADJ
fcis-29257	23	3	,	,	PUNCT
fcis-29257	23	4	the	the	DET
fcis-29257	23	5	construction	construction	NOUN
fcis-29257	23	6	of	of	ADP
fcis-29257	23	7	the	the	DET
fcis-29257	23	8	speech	speech	NOUN
fcis-29257	23	9	emotion	emotion	NOUN
fcis-29257	23	10	database	database	NOUN
fcis-29257	23	11	is	be	AUX
fcis-29257	23	12	based	base	VERB
fcis-29257	23	13	on	on	ADP
fcis-29257	23	14	the	the	DET
fcis-29257	23	15	specific	specific	ADJ
fcis-29257	23	16	requirements	requirement	NOUN
fcis-29257	23	17	of	of	ADP
fcis-29257	23	18	the	the	DET
fcis-29257	23	19	task	task	NOUN
fcis-29257	23	20	.	.	PUNCT
fcis-29257	24	1	the	the	DET
fcis-29257	24	2	production	production	NOUN
fcis-29257	24	3	of	of	ADP
fcis-29257	24	4	the	the	DET
fcis-29257	24	5	dataset	dataset	NOUN
fcis-29257	24	6	in	in	ADP
fcis-29257	24	7	this	this	DET
fcis-29257	24	8	paper	paper	NOUN
fcis-29257	24	9	comes	come	VERB
fcis-29257	24	10	from	from	ADP
fcis-29257	24	11	two	two	NUM
fcis-29257	24	12	aspects	aspect	NOUN
fcis-29257	24	13	:	:	PUNCT
fcis-29257	24	14	one	one	NUM
fcis-29257	24	15	is	be	AUX
fcis-29257	24	16	the	the	DET
fcis-29257	24	17	use	use	NOUN
fcis-29257	24	18	of	of	ADP
fcis-29257	24	19	recording	record	VERB
fcis-29257	24	20	software	software	NOUN
fcis-29257	24	21	to	to	PART
fcis-29257	24	22	record	record	VERB
fcis-29257	24	23	the	the	DET
fcis-29257	24	24	human	human	ADJ
fcis-29257	24	25	voice	voice	NOUN
fcis-29257	24	26	,	,	PUNCT
fcis-29257	24	27	which	which	PRON
fcis-29257	24	28	is	be	AUX
fcis-29257	24	29	performed	perform	VERB
fcis-29257	24	30	by	by	ADP
fcis-29257	24	31	3	3	NUM
fcis-29257	24	32	men	man	NOUN
fcis-29257	24	33	and	and	CCONJ
fcis-29257	24	34	3	3	NUM
fcis-29257	24	35	women	woman	NOUN
fcis-29257	24	36	according	accord	VERB
fcis-29257	24	37	to	to	ADP
fcis-29257	24	38	the	the	DET
fcis-29257	24	39	prepared	prepared	ADJ
fcis-29257	24	40	text	text	NOUN
fcis-29257	24	41	manuscript	manuscript	NOUN
fcis-29257	24	42	,	,	PUNCT
fcis-29257	24	43	which	which	PRON
fcis-29257	24	44	belongs	belong	VERB
fcis-29257	24	45	to	to	ADP
fcis-29257	24	46	the	the	DET
fcis-29257	24	47	spontaneous	spontaneous	ADJ
fcis-29257	24	48	emotion	emotion	NOUN
fcis-29257	24	49	dataset	dataset	NOUN
fcis-29257	24	50	(	(	PUNCT
fcis-29257	24	51	that	that	ADV
fcis-29257	24	52	is	is	ADV
fcis-29257	24	53	,	,	PUNCT
fcis-29257	24	54	the	the	DET
fcis-29257	24	55	emotion	emotion	NOUN
fcis-29257	24	56	is	be	AUX
fcis-29257	24	57	not	not	PART
fcis-29257	24	58	specified	specify	VERB
fcis-29257	24	59	during	during	ADP
fcis-29257	24	60	the	the	DET
fcis-29257	24	61	recording	recording	NOUN
fcis-29257	24	62	,	,	PUNCT
fcis-29257	24	63	)	)	PUNCT
fcis-29257	24	64	;	;	PUNCT
fcis-29257	24	65	the	the	DET
fcis-29257	24	66	second	second	NOUN
fcis-29257	24	67	is	be	AUX
fcis-29257	24	68	to	to	PART
fcis-29257	24	69	obtain	obtain	VERB
fcis-29257	24	70	audio	audio	NOUN
fcis-29257	24	71	and	and	CCONJ
fcis-29257	24	72	video	video	NOUN
fcis-29257	24	73	through	through	ADP
fcis-29257	24	74	the	the	DET
fcis-29257	24	75	network	network	NOUN
fcis-29257	24	76	and	and	CCONJ
fcis-29257	24	77	edit	edit	NOUN
fcis-29257	24	78	and	and	CCONJ
fcis-29257	24	79	process	process	VERB
fcis-29257	24	80	them	they	PRON
fcis-29257	24	81	in	in	ADP
fcis-29257	24	82	adobe	adobe	PROPN
fcis-29257	24	83	audition	audition	PROPN
fcis-29257	24	84	software	software	NOUN
fcis-29257	24	85	.	.	PUNCT
fcis-29257	25	1	in	in	ADP
fcis-29257	25	2	this	this	DET
fcis-29257	25	3	work	work	NOUN
fcis-29257	25	4	,	,	PUNCT
fcis-29257	25	5	the	the	DET
fcis-29257	25	6	speech	speech	NOUN
fcis-29257	25	7	data	datum	NOUN
fcis-29257	25	8	was	be	AUX
fcis-29257	25	9	extracted	extract	VERB
fcis-29257	25	10	from	from	ADP
fcis-29257	25	11	sichuan	sichuan	PROPN
fcis-29257	25	12	dialect	dialect	PROPN
fcis-29257	25	13	film	film	NOUN
fcis-29257	25	14	and	and	CCONJ
fcis-29257	25	15	television	television	NOUN
fcis-29257	25	16	dramas	drama	NOUN
fcis-29257	25	17	.	.	PUNCT
fcis-29257	26	1	the	the	DET
fcis-29257	26	2	main	main	ADJ
fcis-29257	26	3	job	job	NOUN
fcis-29257	26	4	of	of	ADP
fcis-29257	26	5	this	this	DET
fcis-29257	26	6	part	part	NOUN
fcis-29257	26	7	is	be	AUX
fcis-29257	26	8	to	to	PART
fcis-29257	26	9	watch	watch	VERB
fcis-29257	26	10	and	and	CCONJ
fcis-29257	26	11	check	check	VERB
fcis-29257	26	12	each	each	DET
fcis-29257	26	13	set	set	NOUN
fcis-29257	26	14	of	of	ADP
fcis-29257	26	15	videos	video	NOUN
fcis-29257	26	16	,	,	PUNCT
fcis-29257	26	17	select	select	ADJ
fcis-29257	26	18	videos	video	NOUN
fcis-29257	26	19	that	that	PRON
fcis-29257	26	20	contain	contain	VERB
fcis-29257	26	21	clear	clear	ADJ
fcis-29257	26	22	emotions	emotion	NOUN
fcis-29257	26	23	and	and	CCONJ
fcis-29257	26	24	have	have	VERB
fcis-29257	26	25	no	no	DET
fcis-29257	26	26	background	background	NOUN
fcis-29257	26	27	music	music	NOUN
fcis-29257	26	28	and	and	CCONJ
fcis-29257	26	29	noise	noise	NOUN
fcis-29257	26	30	,	,	PUNCT
fcis-29257	26	31	and	and	CCONJ
fcis-29257	26	32	then	then	ADV
fcis-29257	26	33	extract	extract	VERB
fcis-29257	26	34	them	they	PRON
fcis-29257	26	35	from	from	ADP
fcis-29257	26	36	the	the	DET
fcis-29257	26	37	video	video	NOUN
fcis-29257	26	38	as	as	ADP
fcis-29257	26	39	audio	audio	ADJ
fcis-29257	26	40	files	file	NOUN
fcis-29257	26	41	using	use	VERB
fcis-29257	26	42	adobe	adobe	PROPN
fcis-29257	26	43	audition	audition	NOUN
fcis-29257	26	44	,	,	PUNCT
fcis-29257	26	45	and	and	CCONJ
fcis-29257	26	46	divide	divide	VERB
fcis-29257	26	47	them	they	PRON
fcis-29257	26	48	into	into	ADP
fcis-29257	26	49	smaller	small	ADJ
fcis-29257	26	50	audio	audio	NOUN
fcis-29257	26	51	according	accord	VERB
fcis-29257	26	52	to	to	ADP
fcis-29257	26	53	the	the	DET
fcis-29257	26	54	nature	nature	NOUN
fcis-29257	26	55	of	of	ADP
fcis-29257	26	56	the	the	DET
fcis-29257	26	57	speech	speech	NOUN
fcis-29257	26	58	in	in	ADP
fcis-29257	26	59	a	a	DET
fcis-29257	26	60	single	single	ADJ
fcis-29257	26	61	audio	audio	NOUN
fcis-29257	26	62	,	,	PUNCT
fcis-29257	26	63	each	each	DET
fcis-29257	26	64	audio	audio	NOUN
fcis-29257	26	65	duration	duration	NOUN
fcis-29257	26	66	is	be	AUX
fcis-29257	26	67	controlled	control	VERB
fcis-29257	26	68	at	at	ADP
fcis-29257	26	69	3	3	NUM
fcis-29257	26	70	-	-	SYM
fcis-29257	26	71	5s	5s	NUM
fcis-29257	26	72	,	,	PUNCT
fcis-29257	26	73	the	the	DET
fcis-29257	26	74	sampling	sample	VERB
fcis-29257	26	75	rate	rate	NOUN
fcis-29257	26	76	is	be	AUX
fcis-29257	26	77	16000hz	16000hz	ADJ
fcis-29257	26	78	,	,	PUNCT
fcis-29257	26	79	mono	mono	NOUN
fcis-29257	26	80	,	,	PUNCT
fcis-29257	26	81	and	and	CCONJ
fcis-29257	26	82	the	the	DET
fcis-29257	26	83	bit	bit	NOUN
fcis-29257	26	84	depth	depth	NOUN
fcis-29257	26	85	is	be	AUX
fcis-29257	26	86	16	16	NUM
fcis-29257	26	87	.	.	PUNCT
fcis-29257	27	1	60	60	NUM
fcis-29257	27	2	table	table	NOUN
fcis-29257	27	3	1	1	NUM
fcis-29257	27	4	.	.	PUNCT
fcis-29257	28	1	the	the	DET
fcis-29257	28	2	number	number	NOUN
fcis-29257	28	3	of	of	ADP
fcis-29257	28	4	different	different	ADJ
fcis-29257	28	5	emotional	emotional	ADJ
fcis-29257	28	6	categories	category	NOUN
fcis-29257	28	7	category	category	NOUN
fcis-29257	28	8	angry	angry	ADJ
fcis-29257	28	9	fear	fear	VERB
fcis-29257	28	10	happy	happy	ADJ
fcis-29257	28	11	neutral	neutral	ADJ
fcis-29257	28	12	surprise	surprise	NOUN
fcis-29257	28	13	sad	sad	ADJ
fcis-29257	28	14	number	number	NOUN
fcis-29257	28	15	1002	1002	NUM
fcis-29257	28	16	336	336	NUM
fcis-29257	28	17	1001	1001	NUM
fcis-29257	28	18	1022	1022	NUM
fcis-29257	28	19	394	394	NUM
fcis-29257	28	20	755	755	NUM
fcis-29257	28	21	3	3	NUM
fcis-29257	28	22	.	.	PUNCT
fcis-29257	28	23	related	relate	VERB
fcis-29257	28	24	work	work	NOUN
fcis-29257	28	25	emotion	emotion	NOUN
fcis-29257	28	26	recognition	recognition	NOUN
fcis-29257	28	27	has	have	AUX
fcis-29257	28	28	been	be	AUX
fcis-29257	28	29	studied	study	VERB
fcis-29257	28	30	for	for	ADP
fcis-29257	28	31	decades	decade	NOUN
fcis-29257	28	32	,	,	PUNCT
fcis-29257	28	33	with	with	ADP
fcis-29257	28	34	scholars	scholar	NOUN
fcis-29257	28	35	extracting	extract	VERB
fcis-29257	28	36	features	feature	NOUN
fcis-29257	28	37	from	from	ADP
fcis-29257	28	38	audio	audio	ADJ
fcis-29257	28	39	data	datum	NOUN
fcis-29257	28	40	and	and	CCONJ
fcis-29257	28	41	then	then	ADV
fcis-29257	28	42	applying	apply	VERB
fcis-29257	28	43	those	those	DET
fcis-29257	28	44	features	feature	NOUN
fcis-29257	28	45	to	to	ADP
fcis-29257	28	46	a	a	DET
fcis-29257	28	47	range	range	NOUN
fcis-29257	28	48	of	of	ADP
fcis-29257	28	49	classifiers	classifier	NOUN
fcis-29257	28	50	.	.	PUNCT
fcis-29257	29	1	these	these	DET
fcis-29257	29	2	classifiers	classifier	NOUN
fcis-29257	29	3	include	include	VERB
fcis-29257	29	4	:	:	PUNCT
fcis-29257	29	5	hidden	hidden	ADJ
fcis-29257	29	6	markov	markov	NOUN
fcis-29257	29	7	models	model	NOUN
fcis-29257	29	8	,	,	PUNCT
fcis-29257	29	9	convolutional	convolutional	ADJ
fcis-29257	29	10	recursive	recursive	ADJ
fcis-29257	29	11	networks	network	NOUN
fcis-29257	29	12	,	,	PUNCT
fcis-29257	29	13	svms	svms	NOUN
fcis-29257	29	14	,	,	PUNCT
fcis-29257	29	15	hierarchical	hierarchical	ADJ
fcis-29257	29	16	binary	binary	ADJ
fcis-29257	29	17	decision	decision	NOUN
fcis-29257	29	18	trees	tree	NOUN
fcis-29257	29	19	,	,	PUNCT
fcis-29257	29	20	gaussian	gaussian	ADJ
fcis-29257	29	21	mixtures	mixture	NOUN
fcis-29257	29	22	,	,	PUNCT
fcis-29257	29	23	neural	neural	ADJ
fcis-29257	29	24	networks	network	NOUN
fcis-29257	29	25	,	,	PUNCT
fcis-29257	29	26	and	and	CCONJ
fcis-29257	29	27	more	more	ADJ
fcis-29257	29	28	.	.	PUNCT
fcis-29257	30	1	much	much	ADJ
fcis-29257	30	2	of	of	ADP
fcis-29257	30	3	the	the	DET
fcis-29257	30	4	above	above	ADJ
fcis-29257	30	5	work	work	NOUN
fcis-29257	30	6	relies	rely	VERB
fcis-29257	30	7	on	on	ADP
fcis-29257	30	8	context	context	NOUN
fcis-29257	30	9	to	to	PART
fcis-29257	30	10	provide	provide	VERB
fcis-29257	30	11	additional	additional	ADJ
fcis-29257	30	12	information	information	NOUN
fcis-29257	30	13	to	to	PART
fcis-29257	30	14	infer	infer	VERB
fcis-29257	30	15	the	the	DET
fcis-29257	30	16	emotional	emotional	ADJ
fcis-29257	30	17	content	content	NOUN
fcis-29257	30	18	extracted	extract	VERB
fcis-29257	30	19	from	from	ADP
fcis-29257	30	20	the	the	DET
fcis-29257	30	21	data	datum	NOUN
fcis-29257	30	22	.	.	PUNCT
fcis-29257	31	1	in	in	ADP
fcis-29257	31	2	the	the	DET
fcis-29257	31	3	process	process	NOUN
fcis-29257	31	4	of	of	ADP
fcis-29257	31	5	developing	develop	VERB
fcis-29257	31	6	emotion	emotion	NOUN
fcis-29257	31	7	recognition	recognition	NOUN
fcis-29257	31	8	,	,	PUNCT
fcis-29257	31	9	scholars	scholar	NOUN
fcis-29257	31	10	have	have	AUX
fcis-29257	31	11	proposed	propose	VERB
fcis-29257	31	12	a	a	DET
fcis-29257	31	13	variety	variety	NOUN
fcis-29257	31	14	of	of	ADP
fcis-29257	31	15	innovative	innovative	ADJ
fcis-29257	31	16	models	model	NOUN
fcis-29257	31	17	.	.	PUNCT
fcis-29257	32	1	for	for	ADP
fcis-29257	32	2	example	example	NOUN
fcis-29257	32	3	,	,	PUNCT
fcis-29257	32	4	attention	attention	NOUN
fcis-29257	32	5	-	-	PUNCT
fcis-29257	32	6	based	base	VERB
fcis-29257	32	7	networks	network	NOUN
fcis-29257	32	8	,	,	PUNCT
fcis-29257	32	9	which	which	PRON
fcis-29257	32	10	are	be	AUX
fcis-29257	32	11	designed	design	VERB
fcis-29257	32	12	to	to	PART
fcis-29257	32	13	align	align	VERB
fcis-29257	32	14	text	text	NOUN
fcis-29257	32	15	and	and	CCONJ
fcis-29257	32	16	audio	audio	NOUN
fcis-29257	32	17	information	information	NOUN
fcis-29257	32	18	and	and	CCONJ
fcis-29257	32	19	perform	perform	VERB
fcis-29257	32	20	feature	feature	NOUN
fcis-29257	32	21	extraction	extraction	NOUN
fcis-29257	32	22	.	.	PUNCT
fcis-29257	33	1	however	however	ADV
fcis-29257	33	2	,	,	PUNCT
fcis-29257	33	3	with	with	ADP
fcis-29257	33	4	the	the	DET
fcis-29257	33	5	development	development	NOUN
fcis-29257	33	6	of	of	ADP
fcis-29257	33	7	technology	technology	NOUN
fcis-29257	33	8	,	,	PUNCT
fcis-29257	33	9	dynamic	dynamic	ADJ
fcis-29257	33	10	convolutional	convolutional	ADJ
fcis-29257	33	11	networks	network	NOUN
fcis-29257	33	12	[	[	X
fcis-29257	33	13	2	2	X
fcis-29257	33	14	]	]	PUNCT
fcis-29257	33	15	has	have	AUX
fcis-29257	33	16	gradually	gradually	ADV
fcis-29257	33	17	become	become	VERB
fcis-29257	33	18	a	a	DET
fcis-29257	33	19	research	research	NOUN
fcis-29257	33	20	hotspot	hotspot	NOUN
fcis-29257	33	21	due	due	ADP
fcis-29257	33	22	to	to	ADP
fcis-29257	33	23	its	its	PRON
fcis-29257	33	24	efficient	efficient	ADJ
fcis-29257	33	25	feature	feature	NOUN
fcis-29257	33	26	extraction	extraction	NOUN
fcis-29257	33	27	ability	ability	NOUN
fcis-29257	33	28	and	and	CCONJ
fcis-29257	33	29	good	good	ADJ
fcis-29257	33	30	processing	processing	NOUN
fcis-29257	33	31	ability	ability	NOUN
fcis-29257	33	32	of	of	ADP
fcis-29257	33	33	time	time	NOUN
fcis-29257	33	34	series	series	PROPN
fcis-29257	33	35	data	data	PROPN
fcis-29257	33	36	.	.	PUNCT
fcis-29257	34	1	some	some	DET
fcis-29257	34	2	scholars	scholar	NOUN
fcis-29257	34	3	have	have	AUX
fcis-29257	34	4	applied	apply	VERB
fcis-29257	34	5	dynamic	dynamic	ADJ
fcis-29257	34	6	convolutional	convolutional	ADJ
fcis-29257	34	7	networks	network	NOUN
fcis-29257	34	8	to	to	ADP
fcis-29257	34	9	speech	speech	NOUN
fcis-29257	34	10	emotion	emotion	NOUN
fcis-29257	34	11	recognition	recognition	NOUN
fcis-29257	34	12	,	,	PUNCT
fcis-29257	34	13	and	and	CCONJ
fcis-29257	34	14	they	they	PRON
fcis-29257	34	15	have	have	AUX
fcis-29257	34	16	achieved	achieve	VERB
fcis-29257	34	17	a	a	DET
fcis-29257	34	18	more	more	ADV
fcis-29257	34	19	accurate	accurate	ADJ
fcis-29257	34	20	capture	capture	NOUN
fcis-29257	34	21	of	of	ADP
fcis-29257	34	22	emotional	emotional	ADJ
fcis-29257	34	23	information	information	NOUN
fcis-29257	34	24	in	in	ADP
fcis-29257	34	25	audio	audio	ADJ
fcis-29257	34	26	data	datum	NOUN
fcis-29257	34	27	by	by	ADP
fcis-29257	34	28	adjusting	adjust	VERB
fcis-29257	34	29	the	the	DET
fcis-29257	34	30	network	network	NOUN
fcis-29257	34	31	structure	structure	NOUN
fcis-29257	34	32	and	and	CCONJ
fcis-29257	34	33	parameters	parameter	NOUN
fcis-29257	34	34	.	.	PUNCT
fcis-29257	35	1	at	at	ADP
fcis-29257	35	2	the	the	DET
fcis-29257	35	3	same	same	ADJ
fcis-29257	35	4	time	time	NOUN
fcis-29257	35	5	,	,	PUNCT
fcis-29257	35	6	tang	tang	X
fcis-29257	35	7	et	et	PROPN
fcis-29257	35	8	al	al	PROPN
fcis-29257	35	9	.	.	PUNCT
fcis-29257	36	1	[	[	X
fcis-29257	36	2	2]proposed	2]propose	VERB
fcis-29257	36	3	a	a	DET
fcis-29257	36	4	multimodality	multimodality	NOUN
fcis-29257	36	5	-	-	PUNCT
fcis-29257	36	6	based	base	VERB
fcis-29257	36	7	model	model	NOUN
fcis-29257	36	8	as	as	ADP
fcis-29257	36	9	a	a	DET
fcis-29257	36	10	benchmark	benchmark	NOUN
fcis-29257	36	11	,	,	PUNCT
fcis-29257	36	12	which	which	PRON
fcis-29257	36	13	uses	use	VERB
fcis-29257	36	14	a	a	DET
fcis-29257	36	15	multi	multi	NOUN
fcis-29257	36	16	-	-	ADJ
fcis-29257	36	17	modal	modal	ADJ
fcis-29257	36	18	to	to	PART
fcis-29257	36	19	effectively	effectively	ADV
fcis-29257	36	20	fuse	fuse	VERB
fcis-29257	36	21	speech	speech	NOUN
fcis-29257	36	22	,	,	PUNCT
fcis-29257	36	23	text	text	NOUN
fcis-29257	36	24	,	,	PUNCT
fcis-29257	36	25	and	and	CCONJ
fcis-29257	36	26	external	external	ADJ
fcis-29257	36	27	knowledge	knowledge	NOUN
fcis-29257	36	28	.	.	PUNCT
fcis-29257	37	1	however	however	ADV
fcis-29257	37	2	,	,	PUNCT
fcis-29257	37	3	in	in	ADP
fcis-29257	37	4	the	the	DET
fcis-29257	37	5	field	field	NOUN
fcis-29257	37	6	of	of	ADP
fcis-29257	37	7	feature	feature	NOUN
fcis-29257	37	8	extraction	extraction	NOUN
fcis-29257	37	9	,	,	PUNCT
fcis-29257	37	10	the	the	DET
fcis-29257	37	11	convolutional	convolutional	ADJ
fcis-29257	37	12	block	block	NOUN
fcis-29257	37	13	attention	attention	NOUN
fcis-29257	37	14	module	module	NOUN
fcis-29257	37	15	[	[	X
fcis-29257	37	16	4	4	NUM
fcis-29257	37	17	]	]	X
fcis-29257	37	18	(	(	PUNCT
fcis-29257	37	19	cbam	cbam	NOUN
fcis-29257	37	20	)	)	PUNCT
fcis-29257	37	21	has	have	AUX
fcis-29257	37	22	attracted	attract	VERB
fcis-29257	37	23	much	much	ADJ
fcis-29257	37	24	attention	attention	NOUN
fcis-29257	37	25	due	due	ADP
fcis-29257	37	26	to	to	ADP
fcis-29257	37	27	its	its	PRON
fcis-29257	37	28	powerful	powerful	ADJ
fcis-29257	37	29	attention	attention	NOUN
fcis-29257	37	30	mechanism	mechanism	NOUN
fcis-29257	37	31	.	.	PUNCT
fcis-29257	38	1	another	another	DET
fcis-29257	38	2	scholar	scholar	NOUN
fcis-29257	38	3	introduced	introduce	VERB
fcis-29257	38	4	cbam	cbam	NOUN
fcis-29257	38	5	and	and	CCONJ
fcis-29257	38	6	dynamic	dynamic	ADJ
fcis-29257	38	7	convolution[4]models	convolution[4]model	NOUN
fcis-29257	38	8	into	into	ADP
fcis-29257	38	9	the	the	DET
fcis-29257	38	10	field	field	NOUN
fcis-29257	38	11	of	of	ADP
fcis-29257	38	12	speech	speech	NOUN
fcis-29257	38	13	emotion	emotion	NOUN
fcis-29257	38	14	recognition	recognition	NOUN
fcis-29257	38	15	,	,	PUNCT
fcis-29257	38	16	and	and	CCONJ
fcis-29257	38	17	they	they	PRON
fcis-29257	38	18	used	use	VERB
fcis-29257	38	19	the	the	DET
fcis-29257	38	20	attention	attention	NOUN
fcis-29257	38	21	mechanism	mechanism	NOUN
fcis-29257	38	22	of	of	ADP
fcis-29257	38	23	cbam	cbam	NOUN
fcis-29257	38	24	to	to	PART
fcis-29257	38	25	enhance	enhance	VERB
fcis-29257	38	26	the	the	DET
fcis-29257	38	27	recognition	recognition	NOUN
fcis-29257	38	28	ability	ability	NOUN
fcis-29257	38	29	of	of	ADP
fcis-29257	38	30	key	key	ADJ
fcis-29257	38	31	emotional	emotional	ADJ
fcis-29257	38	32	information	information	NOUN
fcis-29257	38	33	by	by	ADP
fcis-29257	38	34	combining	combine	VERB
fcis-29257	38	35	the	the	DET
fcis-29257	38	36	features	feature	NOUN
fcis-29257	38	37	of	of	ADP
fcis-29257	38	38	audio	audio	NOUN
fcis-29257	38	39	and	and	CCONJ
fcis-29257	38	40	text	text	NOUN
fcis-29257	38	41	data	datum	NOUN
fcis-29257	38	42	.	.	PUNCT
fcis-29257	39	1	in	in	ADP
fcis-29257	39	2	addition	addition	NOUN
fcis-29257	39	3	,	,	PUNCT
fcis-29257	39	4	in	in	ADP
fcis-29257	39	5	terms	term	NOUN
fcis-29257	39	6	of	of	ADP
fcis-29257	39	7	text	text	NOUN
fcis-29257	39	8	recognition	recognition	NOUN
fcis-29257	39	9	,	,	PUNCT
fcis-29257	39	10	the	the	DET
fcis-29257	39	11	text	text	NOUN
fcis-29257	39	12	-	-	PUNCT
fcis-29257	39	13	cnn	cnn	PROPN
fcis-29257	39	14	model	model	NOUN
fcis-29257	39	15	is	be	AUX
fcis-29257	39	16	widely	widely	ADV
fcis-29257	39	17	used	use	VERB
fcis-29257	39	18	because	because	SCONJ
fcis-29257	39	19	of	of	ADP
fcis-29257	39	20	its	its	PRON
fcis-29257	39	21	efficient	efficient	ADJ
fcis-29257	39	22	text	text	NOUN
fcis-29257	39	23	feature	feature	NOUN
fcis-29257	39	24	extraction	extraction	NOUN
fcis-29257	39	25	ability	ability	NOUN
fcis-29257	39	26	.	.	PUNCT
fcis-29257	40	1	combining	combine	VERB
fcis-29257	40	2	the	the	DET
fcis-29257	40	3	text	text	NOUN
fcis-29257	40	4	-	-	PUNCT
fcis-29257	40	5	cnn	cnn	PROPN
fcis-29257	40	6	model	model	NOUN
fcis-29257	40	7	with	with	ADP
fcis-29257	40	8	audio	audio	ADJ
fcis-29257	40	9	emotion	emotion	NOUN
fcis-29257	40	10	recognition	recognition	NOUN
fcis-29257	40	11	enables	enable	VERB
fcis-29257	40	12	a	a	DET
fcis-29257	40	13	more	more	ADV
fcis-29257	40	14	comprehensive	comprehensive	ADJ
fcis-29257	40	15	analysis	analysis	NOUN
fcis-29257	40	16	of	of	ADP
fcis-29257	40	17	sentiment	sentiment	NOUN
fcis-29257	40	18	information	information	NOUN
fcis-29257	40	19	by	by	ADP
fcis-29257	40	20	extracting	extract	VERB
fcis-29257	40	21	key	key	ADJ
fcis-29257	40	22	information	information	NOUN
fcis-29257	40	23	from	from	ADP
fcis-29257	40	24	the	the	DET
fcis-29257	40	25	text	text	NOUN
fcis-29257	40	26	and	and	CCONJ
fcis-29257	40	27	combining	combine	VERB
fcis-29257	40	28	it	it	PRON
fcis-29257	40	29	with	with	ADP
fcis-29257	40	30	audio	audio	ADJ
fcis-29257	40	31	features	feature	NOUN
fcis-29257	40	32	.	.	PUNCT
fcis-29257	41	1	4	4	X
fcis-29257	41	2	.	.	X
fcis-29257	41	3	experimental	experimental	ADJ
fcis-29257	41	4	methods	method	NOUN
fcis-29257	41	5	in	in	ADP
fcis-29257	41	6	this	this	DET
fcis-29257	41	7	section	section	NOUN
fcis-29257	41	8	,	,	PUNCT
fcis-29257	41	9	we	we	PRON
fcis-29257	41	10	will	will	AUX
fcis-29257	41	11	introduce	introduce	VERB
fcis-29257	41	12	the	the	DET
fcis-29257	41	13	dynamic	dynamic	ADJ
fcis-29257	41	14	convolutional	convolutional	ADJ
fcis-29257	41	15	model	model	NOUN
fcis-29257	41	16	,	,	PUNCT
fcis-29257	41	17	cbam	cbam	NOUN
fcis-29257	41	18	model	model	NOUN
fcis-29257	41	19	,	,	PUNCT
fcis-29257	41	20	and	and	CCONJ
fcis-29257	41	21	text	text	NOUN
fcis-29257	41	22	-	-	PUNCT
fcis-29257	41	23	cnn	cnn	PROPN
fcis-29257	41	24	sentiment	sentiment	NOUN
fcis-29257	41	25	recognition	recognition	NOUN
fcis-29257	41	26	model	model	NOUN
fcis-29257	41	27	based	base	VERB
fcis-29257	41	28	on	on	ADP
fcis-29257	41	29	the	the	DET
fcis-29257	41	30	previous	previous	ADJ
fcis-29257	41	31	ones	one	NOUN
fcis-29257	41	32	,	,	PUNCT
fcis-29257	41	33	and	and	CCONJ
fcis-29257	41	34	the	the	DET
fcis-29257	41	35	model	model	NOUN
fcis-29257	41	36	we	we	PRON
fcis-29257	41	37	propose	propose	VERB
fcis-29257	41	38	is	be	AUX
fcis-29257	41	39	based	base	VERB
fcis-29257	41	40	on	on	ADP
fcis-29257	41	41	the	the	DET
fcis-29257	41	42	above	above	ADJ
fcis-29257	41	43	model	model	NOUN
fcis-29257	41	44	.	.	PUNCT
fcis-29257	42	1	the	the	DET
fcis-29257	42	2	model	model	NOUN
fcis-29257	42	3	uses	use	VERB
fcis-29257	42	4	two	two	NUM
fcis-29257	42	5	different	different	ADJ
fcis-29257	42	6	data	datum	NOUN
fcis-29257	42	7	modalities	modality	NOUN
fcis-29257	42	8	as	as	ADP
fcis-29257	42	9	input	input	NOUN
fcis-29257	42	10	sources	source	NOUN
fcis-29257	42	11	:	:	PUNCT
fcis-29257	42	12	mfccs	mfccs	ADJ
fcis-29257	42	13	and	and	CCONJ
fcis-29257	42	14	word	word	NOUN
fcis-29257	42	15	vectors	vector	NOUN
fcis-29257	42	16	.	.	PUNCT
fcis-29257	43	1	initially	initially	ADV
fcis-29257	43	2	,	,	PUNCT
fcis-29257	43	3	each	each	DET
fcis-29257	43	4	modality	modality	NOUN
fcis-29257	43	5	is	be	AUX
fcis-29257	43	6	handled	handle	VERB
fcis-29257	43	7	individually	individually	ADV
fcis-29257	43	8	.	.	PUNCT
fcis-29257	44	1	the	the	DET
fcis-29257	44	2	overall	overall	ADJ
fcis-29257	44	3	model	model	NOUN
fcis-29257	44	4	structure	structure	NOUN
fcis-29257	44	5	is	be	AUX
fcis-29257	44	6	shown	show	VERB
fcis-29257	44	7	in	in	ADP
fcis-29257	44	8	figure	figure	NOUN
fcis-29257	44	9	1	1	NUM
fcis-29257	44	10	.	.	PUNCT
fcis-29257	45	1	the	the	DET
fcis-29257	45	2	architecture	architecture	NOUN
fcis-29257	45	3	is	be	AUX
fcis-29257	45	4	a	a	DET
fcis-29257	45	5	multimodal	multimodal	ADJ
fcis-29257	45	6	classification	classification	NOUN
fcis-29257	45	7	model	model	NOUN
fcis-29257	45	8	,	,	PUNCT
fcis-29257	45	9	which	which	PRON
fcis-29257	45	10	combines	combine	VERB
fcis-29257	45	11	the	the	DET
fcis-29257	45	12	spectral	spectral	ADJ
fcis-29257	45	13	features	feature	NOUN
fcis-29257	45	14	of	of	ADP
fcis-29257	45	15	speech	speech	NOUN
fcis-29257	45	16	signals	signal	NOUN
fcis-29257	45	17	(	(	PUNCT
fcis-29257	45	18	mfcc	mfcc	NOUN
fcis-29257	45	19	)	)	PUNCT
fcis-29257	45	20	and	and	CCONJ
fcis-29257	45	21	the	the	DET
fcis-29257	45	22	text	text	NOUN
fcis-29257	45	23	generated	generate	VERB
fcis-29257	45	24	by	by	ADP
fcis-29257	45	25	speech	speech	NOUN
fcis-29257	45	26	recognition	recognition	NOUN
fcis-29257	45	27	,	,	PUNCT
fcis-29257	45	28	extracts	extract	NOUN
fcis-29257	45	29	feature	feature	VERB
fcis-29257	45	30	through	through	ADP
fcis-29257	45	31	two	two	NUM
fcis-29257	45	32	parallel	parallel	ADJ
fcis-29257	45	33	paths	path	NOUN
fcis-29257	45	34	and	and	CCONJ
fcis-29257	45	35	fuses	fuse	VERB
fcis-29257	45	36	them	they	PRON
fcis-29257	45	37	to	to	PART
fcis-29257	45	38	complete	complete	VERB
fcis-29257	45	39	the	the	DET
fcis-29257	45	40	classification	classification	NOUN
fcis-29257	45	41	task	task	NOUN
fcis-29257	45	42	.	.	PUNCT
fcis-29257	46	1	the	the	DET
fcis-29257	46	2	audio	audio	ADJ
fcis-29257	46	3	path	path	NOUN
fcis-29257	46	4	focuses	focus	VERB
fcis-29257	46	5	on	on	ADP
fcis-29257	46	6	capturing	capture	VERB
fcis-29257	46	7	the	the	DET
fcis-29257	46	8	low	low	ADJ
fcis-29257	46	9	-	-	PUNCT
fcis-29257	46	10	level	level	NOUN
fcis-29257	46	11	acoustic	acoustic	ADJ
fcis-29257	46	12	features	feature	NOUN
fcis-29257	46	13	of	of	ADP
fcis-29257	46	14	speech	speech	NOUN
fcis-29257	46	15	,	,	PUNCT
fcis-29257	46	16	while	while	SCONJ
fcis-29257	46	17	the	the	DET
fcis-29257	46	18	text	text	NOUN
fcis-29257	46	19	path	path	NOUN
fcis-29257	46	20	captures	capture	VERB
fcis-29257	46	21	highlevel	highlevel	ADJ
fcis-29257	46	22	semantic	semantic	ADJ
fcis-29257	46	23	information	information	NOUN
fcis-29257	46	24	.	.	PUNCT
fcis-29257	47	1	the	the	DET
fcis-29257	47	2	two	two	NUM
fcis-29257	47	3	paths	path	NOUN
fcis-29257	47	4	of	of	ADP
fcis-29257	47	5	the	the	DET
fcis-29257	47	6	model	model	NOUN
fcis-29257	47	7	are	be	AUX
fcis-29257	47	8	respectively	respectively	ADV
fcis-29257	47	9	for	for	ADP
fcis-29257	47	10	speech	speech	NOUN
fcis-29257	47	11	and	and	CCONJ
fcis-29257	47	12	text	text	NOUN
fcis-29257	47	13	modalities	modality	NOUN
fcis-29257	47	14	,	,	PUNCT
fcis-29257	47	15	and	and	CCONJ
fcis-29257	47	16	the	the	DET
fcis-29257	47	17	dynamic	dynamic	ADJ
fcis-29257	47	18	convolutional	convolutional	ADJ
fcis-29257	47	19	network	network	NOUN
fcis-29257	47	20	,	,	PUNCT
fcis-29257	47	21	cbam	cbam	NOUN
fcis-29257	47	22	model	model	NOUN
fcis-29257	47	23	and	and	CCONJ
fcis-29257	47	24	attention	attention	NOUN
fcis-29257	47	25	mechanism	mechanism	NOUN
fcis-29257	47	26	are	be	AUX
fcis-29257	47	27	used	use	VERB
fcis-29257	47	28	to	to	PART
fcis-29257	47	29	extract	extract	VERB
fcis-29257	47	30	their	their	PRON
fcis-29257	47	31	respective	respective	ADJ
fcis-29257	47	32	features	feature	NOUN
fcis-29257	47	33	,	,	PUNCT
fcis-29257	47	34	and	and	CCONJ
fcis-29257	47	35	finally	finally	ADV
fcis-29257	47	36	,	,	PUNCT
fcis-29257	47	37	the	the	DET
fcis-29257	47	38	fused	fuse	VERB
fcis-29257	47	39	features	feature	NOUN
fcis-29257	47	40	are	be	AUX
fcis-29257	47	41	generated	generate	VERB
fcis-29257	47	42	by	by	ADP
fcis-29257	47	43	the	the	DET
fcis-29257	47	44	fully	fully	ADV
fcis-29257	47	45	connected	connect	VERB
fcis-29257	47	46	layer	layer	NOUN
fcis-29257	47	47	and	and	CCONJ
fcis-29257	47	48	the	the	DET
fcis-29257	47	49	classification	classification	NOUN
fcis-29257	47	50	module	module	NOUN
fcis-29257	47	51	.	.	PUNCT
fcis-29257	48	1	speech	speech	NOUN
fcis-29257	48	2	modal	modal	ADJ
fcis-29257	48	3	input	input	NOUN
fcis-29257	48	4	:	:	PUNCT
fcis-29257	48	5	the	the	DET
fcis-29257	48	6	input	input	NOUN
fcis-29257	48	7	is	be	AUX
fcis-29257	48	8	a	a	DET
fcis-29257	48	9	speech	speech	NOUN
fcis-29257	48	10	signal	signal	NOUN
fcis-29257	48	11	,	,	PUNCT
fcis-29257	48	12	and	and	CCONJ
fcis-29257	48	13	the	the	DET
fcis-29257	48	14	acoustic	acoustic	ADJ
fcis-29257	48	15	signature	signature	NOUN
fcis-29257	48	16	is	be	AUX
fcis-29257	48	17	extracted	extract	VERB
fcis-29257	48	18	by	by	ADP
fcis-29257	48	19	the	the	DET
fcis-29257	48	20	mel	mel	PROPN
fcis-29257	48	21	frequency	frequency	PROPN
fcis-29257	48	22	cepstrum	cepstrum	PROPN
fcis-29257	48	23	coefficient	coefficient	NOUN
fcis-29257	48	24	(	(	PUNCT
fcis-29257	48	25	mfcc	mfcc	NOUN
fcis-29257	48	26	)	)	PUNCT
fcis-29257	48	27	.	.	PUNCT
fcis-29257	49	1	text	text	NOUN
fcis-29257	49	2	modal	modal	ADJ
fcis-29257	49	3	input	input	NOUN
fcis-29257	49	4	:	:	PUNCT
fcis-29257	49	5	speech	speech	NOUN
fcis-29257	49	6	signals	signal	NOUN
fcis-29257	49	7	are	be	AUX
fcis-29257	49	8	transcribed	transcribe	VERB
fcis-29257	49	9	into	into	ADP
fcis-29257	49	10	text	text	NOUN
fcis-29257	49	11	form	form	NOUN
fcis-29257	49	12	by	by	ADP
fcis-29257	49	13	speech	speech	NOUN
fcis-29257	49	14	.	.	PUNCT
fcis-29257	50	1	figure	figure	NOUN
fcis-29257	50	2	1	1	NUM
fcis-29257	50	3	.	.	PUNCT
fcis-29257	50	4	flow	flow	VERB
fcis-29257	50	5	chart	chart	NOUN
fcis-29257	50	6	of	of	ADP
fcis-29257	50	7	multimodal	multimodal	ADJ
fcis-29257	50	8	emotion	emotion	NOUN
fcis-29257	50	9	recognition	recognition	NOUN
fcis-29257	50	10	4.1	4.1	NUM
fcis-29257	50	11	.	.	PUNCT
fcis-29257	50	12	audio	audio	NOUN
fcis-29257	50	13	modal	modal	NOUN
fcis-29257	50	14	processing	processing	NOUN
fcis-29257	50	15	4.1.1	4.1.1	PROPN
fcis-29257	50	16	.	.	PUNCT
fcis-29257	51	1	mfcc	mfcc	NOUN
fcis-29257	51	2	coefficient	coefficient	NOUN
fcis-29257	51	3	extraction	extraction	NOUN
fcis-29257	51	4	the	the	DET
fcis-29257	51	5	mel	mel	PROPN
fcis-29257	51	6	frequency	frequency	PROPN
fcis-29257	51	7	cepstrum	cepstrum	PROPN
fcis-29257	51	8	coefficient	coefficient	NOUN
fcis-29257	51	9	(	(	PUNCT
fcis-29257	51	10	mfcc	mfcc	NOUN
fcis-29257	51	11	)	)	PUNCT
fcis-29257	51	12	is	be	AUX
fcis-29257	51	13	extracted	extract	VERB
fcis-29257	51	14	from	from	ADP
fcis-29257	51	15	the	the	DET
fcis-29257	51	16	input	input	NOUN
fcis-29257	51	17	speech	speech	NOUN
fcis-29257	51	18	signal	signal	NOUN
fcis-29257	51	19	to	to	PART
fcis-29257	51	20	obtain	obtain	VERB
fcis-29257	51	21	a	a	DET
fcis-29257	51	22	twodimensional	twodimensional	ADJ
fcis-29257	51	23	feature	feature	NOUN
fcis-29257	51	24	matrix	matrix	NOUN
fcis-29257	51	25	:	:	PUNCT
fcis-29257	51	26	is	be	AUX
fcis-29257	51	27	the	the	DET
fcis-29257	51	28	number	number	NOUN
fcis-29257	51	29	of	of	ADP
fcis-29257	51	30	time	time	NOUN
fcis-29257	51	31	steps	step	NOUN
fcis-29257	51	32	,	,	PUNCT
fcis-29257	51	33	,	,	PUNCT
fcis-29257	51	34	∈	∈	PROPN
fcis-29257	51	35	r	r	NOUN
fcis-29257	51	36	,	,	PUNCT
fcis-29257	51	37	t	t	PROPN
fcis-29257	51	38	is	be	AUX
fcis-29257	51	39	the	the	DET
fcis-29257	51	40	number	number	NOUN
fcis-29257	51	41	of	of	ADP
fcis-29257	51	42	time	time	NOUN
fcis-29257	51	43	steps	step	NOUN
fcis-29257	51	44	,	,	PUNCT
fcis-29257	51	45	and	and	CCONJ
fcis-29257	51	46	d	d	NOUN
fcis-29257	51	47	is	be	AUX
fcis-29257	51	48	the	the	DET
fcis-29257	51	49	mfcc	mfcc	NOUN
fcis-29257	51	50	feature	feature	NOUN
fcis-29257	51	51	dimension	dimension	NOUN
fcis-29257	51	52	.	.	PUNCT
fcis-29257	52	1	mfcc	mfcc	NOUN
fcis-29257	52	2	preserves	preserve	VERB
fcis-29257	52	3	the	the	DET
fcis-29257	52	4	spectral	spectral	ADJ
fcis-29257	52	5	properties	property	NOUN
fcis-29257	52	6	of	of	ADP
fcis-29257	52	7	speech	speech	NOUN
fcis-29257	52	8	and	and	CCONJ
fcis-29257	52	9	is	be	AUX
fcis-29257	52	10	a	a	DET
fcis-29257	52	11	low	low	ADJ
fcis-29257	52	12	-	-	PUNCT
fcis-29257	52	13	dimensional	dimensional	ADJ
fcis-29257	52	14	feature	feature	NOUN
fcis-29257	52	15	that	that	PRON
fcis-29257	52	16	describes	describe	VERB
fcis-29257	52	17	the	the	DET
fcis-29257	52	18	physical	physical	ADJ
fcis-29257	52	19	properties	property	NOUN
fcis-29257	52	20	of	of	ADP
fcis-29257	52	21	speech	speech	NOUN
fcis-29257	52	22	.	.	PUNCT
fcis-29257	53	1	4.1.2	4.1.2	NUM
fcis-29257	53	2	.	.	PUNCT
fcis-29257	53	3	audio	audio	ADJ
fcis-29257	53	4	modal	modal	ADJ
fcis-29257	53	5	feature	feature	NOUN
fcis-29257	53	6	extraction	extraction	NOUN
fcis-29257	53	7	(	(	PUNCT
fcis-29257	53	8	1	1	X
fcis-29257	53	9	)	)	PUNCT
fcis-29257	53	10	convolutional	convolutional	ADJ
fcis-29257	53	11	neural	neural	ADJ
fcis-29257	53	12	network	network	NOUN
fcis-29257	53	13	(	(	PUNCT
fcis-29257	53	14	cnn	cnn	PROPN
fcis-29257	53	15	)	)	PUNCT
fcis-29257	53	16	feature	feature	NOUN
fcis-29257	53	17	extraction	extraction	NOUN
fcis-29257	53	18	:	:	PUNCT
fcis-29257	53	19	mfcc	mfcc	NOUN
fcis-29257	53	20	features	feature	NOUN
fcis-29257	53	21	are	be	AUX
fcis-29257	53	22	processed	process	VERB
fcis-29257	53	23	through	through	ADP
fcis-29257	53	24	multi	multi	ADJ
fcis-29257	53	25	-	-	ADJ
fcis-29257	53	26	layer	layer	ADJ
fcis-29257	53	27	convolutional	convolutional	ADJ
fcis-29257	53	28	networks	network	NOUN
fcis-29257	53	29	to	to	PART
fcis-29257	53	30	extract	extract	VERB
fcis-29257	53	31	local	local	ADJ
fcis-29257	53	32	temporal	temporal	ADJ
fcis-29257	53	33	and	and	CCONJ
fcis-29257	53	34	spatial	spatial	ADJ
fcis-29257	53	35	features	feature	NOUN
fcis-29257	53	36	:	:	PUNCT
fcis-29257	53	37	convolutional	convolutional	ADJ
fcis-29257	53	38	networks	network	NOUN
fcis-29257	53	39	can	can	AUX
fcis-29257	53	40	capture	capture	VERB
fcis-29257	53	41	the	the	DET
fcis-29257	53	42	feature	feature	NOUN
fcis-29257	53	43	patterns	pattern	NOUN
fcis-29257	53	44	of	of	ADP
fcis-29257	53	45	speech	speech	NOUN
fcis-29257	53	46	signals	signal	NOUN
fcis-29257	53	47	in	in	ADP
fcis-29257	53	48	different	different	ADJ
fcis-29257	53	49	time	time	NOUN
fcis-29257	53	50	windows	window	NOUN
fcis-29257	53	51	.	.	PUNCT
fcis-29257	54	1	(	(	PUNCT
fcis-29257	54	2	2	2	X
fcis-29257	54	3	)	)	PUNCT
fcis-29257	54	4	dynamic	dynamic	ADJ
fcis-29257	54	5	convolution	convolution	NOUN
fcis-29257	54	6	and	and	CCONJ
fcis-29257	54	7	cbam	cbam	NOUN
fcis-29257	54	8	(	(	PUNCT
fcis-29257	54	9	attention	attention	NOUN
fcis-29257	54	10	module	module	NOUN
fcis-29257	54	11	):	):	PUNCT
fcis-29257	54	12	dynamic	dynamic	ADJ
fcis-29257	54	13	convolution	convolution	NOUN
fcis-29257	54	14	enhances	enhance	VERB
fcis-29257	54	15	the	the	DET
fcis-29257	54	16	ability	ability	NOUN
fcis-29257	54	17	to	to	PART
fcis-29257	54	18	model	model	VERB
fcis-29257	54	19	the	the	DET
fcis-29257	54	20	change	change	NOUN
fcis-29257	54	21	of	of	ADP
fcis-29257	54	22	speech	speech	NOUN
fcis-29257	54	23	signals	signal	NOUN
fcis-29257	54	24	over	over	ADP
fcis-29257	54	25	time	time	NOUN
fcis-29257	54	26	.	.	PUNCT
fcis-29257	55	1	the	the	DET
fcis-29257	55	2	convolutional	convolutional	ADJ
fcis-29257	55	3	block	block	NOUN
fcis-29257	55	4	attention	attention	NOUN
fcis-29257	55	5	module	module	NOUN
fcis-29257	55	6	(	(	PUNCT
fcis-29257	55	7	cbam	cbam	NOUN
fcis-29257	55	8	)	)	PUNCT
fcis-29257	55	9	focuses	focus	VERB
fcis-29257	55	10	on	on	ADP
fcis-29257	55	11	the	the	DET
fcis-29257	55	12	most	most	ADV
fcis-29257	55	13	important	important	ADJ
fcis-29257	55	14	acoustic	acoustic	ADJ
fcis-29257	55	15	features	feature	NOUN
fcis-29257	55	16	through	through	ADP
fcis-29257	55	17	channel	channel	NOUN
fcis-29257	55	18	attention	attention	NOUN
fcis-29257	55	19	and	and	CCONJ
fcis-29257	55	20	spatial	spatial	ADJ
fcis-29257	55	21	attention	attention	NOUN
fcis-29257	55	22	mechanisms	mechanism	NOUN
fcis-29257	55	23	.	.	PUNCT
fcis-29257	56	1	final	final	ADJ
fcis-29257	56	2	output	output	NOUN
fcis-29257	56	3	processed	process	VERB
fcis-29257	56	4	audio	audio	ADJ
fcis-29257	56	5	characteristics	characteristic	NOUN
fcis-29257	56	6	:	:	PUNCT
fcis-29257	56	7	.	.	PUNCT
fcis-29257	57	1	odconv[5]is	odconv[5]is	PROPN
fcis-29257	57	2	created	create	VERB
fcis-29257	57	3	based	base	VERB
fcis-29257	57	4	on	on	ADP
fcis-29257	57	5	dyconv	dyconv	NOUN
fcis-29257	57	6	and	and	CCONJ
fcis-29257	57	7	condconv[7	condconv[7	PROPN
fcis-29257	57	8	]	]	X
fcis-29257	57	9	]	]	PUNCT
fcis-29257	57	10	.	.	PUNCT
fcis-29257	58	1	unlike	unlike	ADP
fcis-29257	58	2	traditional	traditional	ADJ
fcis-29257	58	3	convolution	convolution	NOUN
fcis-29257	58	4	,	,	PUNCT
fcis-29257	58	5	which	which	PRON
fcis-29257	58	6	has	have	AUX
fcis-29257	58	7	fixed	fix	VERB
fcis-29257	58	8	weights	weight	NOUN
fcis-29257	58	9	,	,	PUNCT
fcis-29257	58	10	odconv	odconv	NOUN
fcis-29257	58	11	dynamically	dynamically	ADV
fcis-29257	58	12	generates	generate	VERB
fcis-29257	58	13	kernel	kernel	PROPN
fcis-29257	58	14	weights	weight	NOUN
fcis-29257	58	15	,	,	PUNCT
fcis-29257	58	16	allowing	allow	VERB
fcis-29257	58	17	for	for	ADP
fcis-29257	58	18	more	more	ADV
fcis-29257	58	19	flexible	flexible	ADJ
fcis-29257	58	20	and	and	CCONJ
fcis-29257	58	21	input	input	NOUN
fcis-29257	58	22	-	-	PUNCT
fcis-29257	58	23	dependent	dependent	ADJ
fcis-29257	58	24	feature	feature	NOUN
fcis-29257	58	25	extraction	extraction	NOUN
fcis-29257	58	26	.	.	PUNCT
fcis-29257	59	1	this	this	DET
fcis-29257	59	2	adaptive	adaptive	ADJ
fcis-29257	59	3	feature	feature	NOUN
fcis-29257	59	4	enables	enable	VERB
fcis-29257	59	5	odconv	odconv	ADJ
fcis-29257	59	6	to	to	PART
fcis-29257	59	7	capture	capture	VERB
fcis-29257	59	8	more	more	ADV
fcis-29257	59	9	complex	complex	ADJ
fcis-29257	59	10	and	and	CCONJ
fcis-29257	59	11	diverse	diverse	ADJ
fcis-29257	59	12	patterns	pattern	NOUN
fcis-29257	59	13	in	in	ADP
fcis-29257	59	14	the	the	DET
fcis-29257	59	15	data	datum	NOUN
fcis-29257	59	16	,	,	PUNCT
fcis-29257	59	17	especially	especially	ADV
fcis-29257	59	18	when	when	SCONJ
fcis-29257	59	19	used	use	VERB
fcis-29257	59	20	in	in	ADP
fcis-29257	59	21	mfcc	mfcc	NOUN
fcis-29257	59	22	's	's	PART
fcis-29257	59	23	data	datum	NOUN
fcis-29257	59	24	.	.	PUNCT
fcis-29257	60	1	figure	figure	NOUN
fcis-29257	60	2	2	2	NUM
fcis-29257	60	3	illustrates	illustrate	VERB
fcis-29257	60	4	the	the	DET
fcis-29257	60	5	structure	structure	NOUN
fcis-29257	60	6	of	of	ADP
fcis-29257	60	7	odconv	odconv	NOUN
fcis-29257	60	8	,	,	PUNCT
fcis-29257	60	9	where	where	SCONJ
fcis-29257	60	10	the	the	DET
fcis-29257	60	11	convolutional	convolutional	ADJ
fcis-29257	60	12	kernel	kernel	NOUN
fcis-29257	60	13	is	be	AUX
fcis-29257	60	14	represented	represent	VERB
fcis-29257	60	15	and	and	CCONJ
fcis-29257	60	16	the	the	DET
fcis-29257	60	17	compressed	compress	VERB
fcis-29257	60	18	eigenvectors	eigenvector	NOUN
fcis-29257	60	19	are	be	AUX
fcis-29257	60	20	mapped	map	VERB
fcis-29257	60	21	into	into	ADP
fcis-29257	60	22	a	a	DET
fcis-29257	60	23	low	low	ADJ
fcis-29257	60	24	-	-	PUNCT
fcis-29257	60	25	dimensional	dimensional	ADJ
fcis-29257	60	26	space	space	NOUN
fcis-29257	60	27	at	at	ADP
fcis-29257	60	28	the	the	DET
fcis-29257	60	29	fc	fc	PROPN
fcis-29257	60	30	layer	layer	NOUN
fcis-29257	60	31	.	.	PUNCT
fcis-29257	61	1	compared	compare	VERB
fcis-29257	61	2	with	with	ADP
fcis-29257	61	3	ordinary	ordinary	ADJ
fcis-29257	61	4	convolutional	convolutional	ADJ
fcis-29257	61	5	networks	network	NOUN
fcis-29257	61	6	(	(	PUNCT
fcis-29257	61	7	cnns	cnns	PROPN
fcis-29257	61	8	)	)	PUNCT
fcis-29257	61	9	,	,	PUNCT
fcis-29257	61	10	the	the	DET
fcis-29257	61	11	odconv	odconv	NOUN
fcis-29257	61	12	has	have	VERB
fcis-29257	61	13	many	many	ADJ
fcis-29257	61	14	advantages	advantage	NOUN
fcis-29257	61	15	when	when	SCONJ
fcis-29257	61	16	identifying	identify	VERB
fcis-29257	61	17	mfcc	mfcc	NOUN
fcis-29257	61	18	,	,	PUNCT
fcis-29257	61	19	and	and	CCONJ
fcis-29257	61	20	the	the	DET
fcis-29257	61	21	dynamic	dynamic	ADJ
fcis-29257	61	22	convolutional	convolutional	ADJ
fcis-29257	61	23	kernel	kernel	NOUN
fcis-29257	61	24	can	can	AUX
fcis-29257	61	25	be	be	AUX
fcis-29257	61	26	adaptively	adaptively	ADV
fcis-29257	61	27	adjusted	adjust	VERB
fcis-29257	61	28	according	accord	VERB
fcis-29257	61	29	to	to	ADP
fcis-29257	61	30	the	the	DET
fcis-29257	61	31	local	local	ADJ
fcis-29257	61	32	features	feature	NOUN
fcis-29257	61	33	of	of	ADP
fcis-29257	61	34	the	the	DET
fcis-29257	61	35	input	input	NOUN
fcis-29257	61	36	feature	feature	NOUN
fcis-29257	61	37	map	map	NOUN
fcis-29257	61	38	.	.	PUNCT
fcis-29257	62	1	this	this	DET
fcis-29257	62	2	adaptability	adaptability	NOUN
fcis-29257	62	3	allows	allow	VERB
fcis-29257	62	4	the	the	DET
fcis-29257	62	5	network	network	NOUN
fcis-29257	62	6	to	to	PART
fcis-29257	62	7	better	well	ADV
fcis-29257	62	8	capture	capture	VERB
fcis-29257	62	9	features	feature	NOUN
fcis-29257	62	10	in	in	ADP
fcis-29257	62	11	different	different	ADJ
fcis-29257	62	12	audio	audio	NOUN
fcis-29257	62	13	.	.	PUNCT
fcis-29257	63	1	the	the	DET
fcis-29257	63	2	dynamic	dynamic	ADJ
fcis-29257	63	3	convolutional	convolutional	ADJ
fcis-29257	63	4	kernel	kernel	NOUN
fcis-29257	63	5	enhances	enhance	VERB
fcis-29257	63	6	the	the	DET
fcis-29257	63	7	robustness	robustness	NOUN
fcis-29257	63	8	of	of	ADP
fcis-29257	63	9	the	the	DET
fcis-29257	63	10	model	model	NOUN
fcis-29257	63	11	to	to	ADP
fcis-29257	63	12	changes	change	NOUN
fcis-29257	63	13	in	in	ADP
fcis-29257	63	14	the	the	DET
fcis-29257	63	15	input	input	NOUN
fcis-29257	63	16	data	datum	NOUN
fcis-29257	63	17	,	,	PUNCT
fcis-29257	63	18	while	while	SCONJ
fcis-29257	63	19	capturing	capture	VERB
fcis-29257	63	20	both	both	CCONJ
fcis-29257	63	21	global	global	ADJ
fcis-29257	63	22	featuresand	featuresand	NOUN
fcis-29257	63	23	local	local	ADJ
fcis-29257	63	24	features	feature	NOUN
fcis-29257	63	25	.	.	PUNCT
fcis-29257	64	1	61	61	NUM
fcis-29257	64	2	figure	figure	NOUN
fcis-29257	64	3	2	2	NUM
fcis-29257	64	4	.	.	NOUN
fcis-29257	64	5	odconv	odconv	PROPN
fcis-29257	64	6	model	model	PROPN
fcis-29257	64	7	diagram	diagram	NOUN
fcis-29257	64	8	where	where	SCONJ
fcis-29257	64	9	,	,	PUNCT
fcis-29257	64	10	represents	represent	VERB
fcis-29257	64	11	the	the	DET
fcis-29257	64	12	scalar	scalar	ADJ
fcis-29257	64	13	attention	attention	NOUN
fcis-29257	64	14	factor	factor	NOUN
fcis-29257	64	15	of	of	ADP
fcis-29257	64	16	the	the	DET
fcis-29257	64	17	convolution	convolution	NOUN
fcis-29257	64	18	kernel	kernel	NOUN
fcis-29257	64	19	;	;	PUNCT
fcis-29257	64	20	∈	∈	NOUN
fcis-29257	64	21	,	,	PUNCT
fcis-29257	64	22	representing	represent	VERB
fcis-29257	64	23	∈	∈	NOUN
fcis-29257	64	24	,	,	PUNCT
fcis-29257	64	25	∈	∈	PROPN
fcis-29257	64	26	three	three	NUM
fcis-29257	64	27	new	new	ADJ
fcis-29257	64	28	attention	attention	NOUN
fcis-29257	64	29	factors	factor	NOUN
fcis-29257	64	30	calculated	calculate	VERB
fcis-29257	64	31	along	along	ADP
fcis-29257	64	32	the	the	DET
fcis-29257	64	33	spatial	spatial	ADJ
fcis-29257	64	34	,	,	PUNCT
fcis-29257	64	35	output	output	NOUN
fcis-29257	64	36	,	,	PUNCT
fcis-29257	64	37	and	and	CCONJ
fcis-29257	64	38	input	input	NOUN
fcis-29257	64	39	dimensions	dimension	NOUN
fcis-29257	64	40	,	,	PUNCT
fcis-29257	64	41	respectively	respectively	ADV
fcis-29257	64	42	.	.	PUNCT
fcis-29257	65	1	⊗	⊗	PROPN
fcis-29257	65	2	represents	represent	VERB
fcis-29257	65	3	element	element	NOUN
fcis-29257	65	4	-	-	PUNCT
fcis-29257	65	5	by	by	ADP
fcis-29257	65	6	-	-	PUNCT
fcis-29257	65	7	element	element	NOUN
fcis-29257	65	8	multiplication	multiplication	NOUN
fcis-29257	65	9	;	;	PUNCT
fcis-29257	65	10	*	*	PUNCT
fcis-29257	65	11	denotes	denote	VERB
fcis-29257	65	12	a	a	DET
fcis-29257	65	13	convolution	convolution	NOUN
fcis-29257	65	14	operation	operation	NOUN
fcis-29257	65	15	.	.	PUNCT
fcis-29257	66	1	(	(	PUNCT
fcis-29257	66	2	3	3	X
fcis-29257	66	3	)	)	PUNCT
fcis-29257	66	4	the	the	DET
fcis-29257	66	5	cbam	cbam	NOUN
fcis-29257	66	6	model	model	NOUN
fcis-29257	67	1	[	[	X
fcis-29257	67	2	9	9	NUM
fcis-29257	67	3	]	]	PUNCT
fcis-29257	67	4	mainly	mainly	ADV
fcis-29257	67	5	includes	include	VERB
fcis-29257	67	6	the	the	DET
fcis-29257	67	7	channel	channel	NOUN
fcis-29257	67	8	attention	attention	NOUN
fcis-29257	67	9	mechanism	mechanism	NOUN
fcis-29257	67	10	as	as	SCONJ
fcis-29257	67	11	shown	show	VERB
fcis-29257	67	12	in	in	ADP
fcis-29257	67	13	figure	figure	NOUN
fcis-29257	67	14	3	3	NUM
fcis-29257	67	15	and	and	CCONJ
fcis-29257	67	16	the	the	DET
fcis-29257	67	17	spatial	spatial	ADJ
fcis-29257	67	18	attention	attention	NOUN
fcis-29257	67	19	mechanism	mechanism	NOUN
fcis-29257	67	20	as	as	SCONJ
fcis-29257	67	21	shown	show	VERB
fcis-29257	67	22	in	in	ADP
fcis-29257	67	23	figure	figure	NOUN
fcis-29257	67	24	4	4	NUM
fcis-29257	67	25	.	.	PUNCT
fcis-29257	68	1	the	the	DET
fcis-29257	68	2	main	main	ADJ
fcis-29257	68	3	purpose	purpose	NOUN
fcis-29257	68	4	of	of	ADP
fcis-29257	68	5	the	the	DET
fcis-29257	68	6	channel	channel	NOUN
fcis-29257	68	7	attention	attention	NOUN
fcis-29257	68	8	mechanism	mechanism	NOUN
fcis-29257	68	9	is	be	AUX
fcis-29257	68	10	to	to	PART
fcis-29257	68	11	calculate	calculate	VERB
fcis-29257	68	12	the	the	DET
fcis-29257	68	13	attention	attention	NOUN
fcis-29257	68	14	weight	weight	NOUN
fcis-29257	68	15	in	in	ADP
fcis-29257	68	16	the	the	DET
fcis-29257	68	17	channel	channel	NOUN
fcis-29257	68	18	dimension	dimension	NOUN
fcis-29257	68	19	,	,	PUNCT
fcis-29257	68	20	so	so	SCONJ
fcis-29257	68	21	as	as	SCONJ
fcis-29257	68	22	to	to	PART
fcis-29257	68	23	highlight	highlight	VERB
fcis-29257	68	24	the	the	DET
fcis-29257	68	25	important	important	ADJ
fcis-29257	68	26	channel	channel	NOUN
fcis-29257	68	27	information	information	NOUN
fcis-29257	68	28	.	.	PUNCT
fcis-29257	69	1	cbam	cbam	NOUN
fcis-29257	69	2	can	can	AUX
fcis-29257	69	3	effectively	effectively	ADV
fcis-29257	69	4	enhance	enhance	VERB
fcis-29257	69	5	the	the	DET
fcis-29257	69	6	model	model	NOUN
fcis-29257	69	7	's	's	PART
fcis-29257	69	8	ability	ability	NOUN
fcis-29257	69	9	to	to	PART
fcis-29257	69	10	capture	capture	VERB
fcis-29257	69	11	key	key	ADJ
fcis-29257	69	12	information	information	NOUN
fcis-29257	69	13	by	by	ADP
fcis-29257	69	14	calculating	calculate	VERB
fcis-29257	69	15	the	the	DET
fcis-29257	69	16	attention	attention	NOUN
fcis-29257	69	17	weights	weight	NOUN
fcis-29257	69	18	in	in	ADP
fcis-29257	69	19	the	the	DET
fcis-29257	69	20	channel	channel	NOUN
fcis-29257	69	21	and	and	CCONJ
fcis-29257	69	22	spatial	spatial	ADJ
fcis-29257	69	23	dimensions	dimension	NOUN
fcis-29257	69	24	respectively	respectively	ADV
fcis-29257	69	25	.	.	PUNCT
fcis-29257	70	1	the	the	DET
fcis-29257	70	2	channel	channel	NOUN
fcis-29257	70	3	attention	attention	NOUN
fcis-29257	70	4	mechanism	mechanism	NOUN
fcis-29257	70	5	focuses	focus	VERB
fcis-29257	70	6	on	on	ADP
fcis-29257	70	7	the	the	DET
fcis-29257	70	8	importance	importance	NOUN
fcis-29257	70	9	of	of	ADP
fcis-29257	70	10	different	different	ADJ
fcis-29257	70	11	channels	channel	NOUN
fcis-29257	70	12	,	,	PUNCT
fcis-29257	70	13	while	while	SCONJ
fcis-29257	70	14	the	the	DET
fcis-29257	70	15	spatial	spatial	ADJ
fcis-29257	70	16	attention	attention	NOUN
fcis-29257	70	17	mechanism	mechanism	NOUN
fcis-29257	70	18	focuses	focus	VERB
fcis-29257	70	19	on	on	ADP
fcis-29257	70	20	the	the	DET
fcis-29257	70	21	importance	importance	NOUN
fcis-29257	70	22	of	of	ADP
fcis-29257	70	23	different	different	ADJ
fcis-29257	70	24	spatial	spatial	ADJ
fcis-29257	70	25	positions	position	NOUN
fcis-29257	70	26	.	.	PUNCT
fcis-29257	71	1	through	through	ADP
fcis-29257	71	2	the	the	DET
fcis-29257	71	3	combination	combination	NOUN
fcis-29257	71	4	of	of	ADP
fcis-29257	71	5	these	these	DET
fcis-29257	71	6	two	two	NUM
fcis-29257	71	7	mechanisms	mechanism	NOUN
fcis-29257	71	8	,	,	PUNCT
fcis-29257	71	9	cbam	cbam	NOUN
fcis-29257	71	10	is	be	AUX
fcis-29257	71	11	able	able	ADJ
fcis-29257	71	12	to	to	PART
fcis-29257	71	13	significantly	significantly	ADV
fcis-29257	71	14	improve	improve	VERB
fcis-29257	71	15	the	the	DET
fcis-29257	71	16	performance	performance	NOUN
fcis-29257	71	17	of	of	ADP
fcis-29257	71	18	the	the	DET
fcis-29257	71	19	model	model	NOUN
fcis-29257	71	20	in	in	ADP
fcis-29257	71	21	a	a	DET
fcis-29257	71	22	variety	variety	NOUN
fcis-29257	71	23	of	of	ADP
fcis-29257	71	24	tasks	task	NOUN
fcis-29257	71	25	,	,	PUNCT
fcis-29257	71	26	especially	especially	ADV
fcis-29257	71	27	those	those	PRON
fcis-29257	71	28	with	with	ADP
fcis-29257	71	29	complex	complex	ADJ
fcis-29257	71	30	features	feature	NOUN
fcis-29257	71	31	and	and	CCONJ
fcis-29257	71	32	backgrounds	background	NOUN
fcis-29257	71	33	.	.	PUNCT
fcis-29257	72	1	the	the	DET
fcis-29257	72	2	formula	formula	NOUN
fcis-29257	72	3	for	for	ADP
fcis-29257	72	4	calculating	calculate	VERB
fcis-29257	72	5	the	the	DET
fcis-29257	72	6	channel	channel	NOUN
fcis-29257	72	7	attention	attention	NOUN
fcis-29257	72	8	mechanism	mechanism	NOUN
fcis-29257	72	9	is	be	AUX
fcis-29257	72	10	as	as	SCONJ
fcis-29257	72	11	follows	follow	VERB
fcis-29257	72	12	:	:	PUNCT
fcis-29257	72	13	figure	figure	VERB
fcis-29257	72	14	3	3	NUM
fcis-29257	72	15	.	.	NOUN
fcis-29257	72	16	channel	channel	NOUN
fcis-29257	72	17	attention	attention	NOUN
fcis-29257	72	18	mechanism	mechanism	NOUN
fcis-29257	72	19	the	the	DET
fcis-29257	72	20	input	input	NOUN
fcis-29257	72	21	is	be	AUX
fcis-29257	72	22	a	a	DET
fcis-29257	72	23	feature	feature	NOUN
fcis-29257	72	24	tensor	tensor	NOUN
fcis-29257	72	25	∈	∈	NOUN
fcis-29257	72	26	,	,	PUNCT
fcis-29257	72	27	where	where	SCONJ
fcis-29257	72	28	c	c	PROPN
fcis-29257	72	29	is	be	AUX
fcis-29257	72	30	the	the	DET
fcis-29257	72	31	number	number	NOUN
fcis-29257	72	32	of	of	ADP
fcis-29257	72	33	channels	channel	NOUN
fcis-29257	72	34	and	and	CCONJ
fcis-29257	72	35	h	h	NOUN
fcis-29257	72	36	and	and	CCONJ
fcis-29257	72	37	w	w	PROPN
fcis-29257	72	38	are	be	AUX
fcis-29257	72	39	spatial	spatial	ADJ
fcis-29257	72	40	dimensions	dimension	NOUN
fcis-29257	72	41	.	.	PUNCT
fcis-29257	73	1	the	the	DET
fcis-29257	73	2	input	input	NOUN
fcis-29257	73	3	feature	feature	NOUN
fcis-29257	73	4	maps	map	NOUN
fcis-29257	73	5	are	be	AUX
fcis-29257	73	6	subjected	subject	VERB
fcis-29257	73	7	to	to	ADP
fcis-29257	73	8	global	global	ADJ
fcis-29257	73	9	average	average	ADJ
fcis-29257	73	10	pooling	pooling	NOUN
fcis-29257	73	11	and	and	CCONJ
fcis-29257	73	12	global	global	ADJ
fcis-29257	73	13	maximum	maximum	ADJ
fcis-29257	73	14	pooling	pooling	NOUN
fcis-29257	73	15	,	,	PUNCT
fcis-29257	73	16	respectively	respectively	ADV
fcis-29257	73	17	,	,	PUNCT
fcis-29257	73	18	to	to	PART
fcis-29257	73	19	extract	extract	VERB
fcis-29257	73	20	spatial	spatial	ADJ
fcis-29257	73	21	information	information	NOUN
fcis-29257	73	22	and	and	CCONJ
fcis-29257	73	23	perform	perform	VERB
fcis-29257	73	24	channel	channel	NOUN
fcis-29257	73	25	compression	compression	NOUN
fcis-29257	73	26	.	.	PUNCT
fcis-29257	74	1	obtain	obtain	VERB
fcis-29257	74	2	two	two	NUM
fcis-29257	74	3	channel	channel	NOUN
fcis-29257	74	4	description	description	NOUN
fcis-29257	74	5	vectors	vector	NOUN
fcis-29257	74	6	.	.	PUNCT
fcis-29257	75	1	the	the	DET
fcis-29257	75	2	results	result	NOUN
fcis-29257	75	3	of	of	ADP
fcis-29257	75	4	average	average	ADJ
fcis-29257	75	5	pooling	pooling	NOUN
fcis-29257	75	6	and	and	CCONJ
fcis-29257	75	7	maximum	maximum	ADJ
fcis-29257	75	8	pooling	pooling	NOUN
fcis-29257	75	9	are	be	AUX
fcis-29257	75	10	input	input	NOUN
fcis-29257	75	11	into	into	ADP
fcis-29257	75	12	a	a	DET
fcis-29257	75	13	two	two	NUM
fcis-29257	75	14	-	-	PUNCT
fcis-29257	75	15	layer	layer	NOUN
fcis-29257	75	16	mlp	mlp	NOUN
fcis-29257	75	17	with	with	ADP
fcis-29257	75	18	shared	share	VERB
fcis-29257	75	19	weights	weight	NOUN
fcis-29257	75	20	,	,	PUNCT
fcis-29257	75	21	which	which	PRON
fcis-29257	75	22	is	be	AUX
fcis-29257	75	23	used	use	VERB
fcis-29257	75	24	to	to	PART
fcis-29257	75	25	extract	extract	VERB
fcis-29257	75	26	nonlinear	nonlinear	ADJ
fcis-29257	75	27	relationships	relationship	NOUN
fcis-29257	75	28	between	between	ADP
fcis-29257	75	29	channels	channel	NOUN
fcis-29257	75	30	.	.	PUNCT
fcis-29257	76	1	the	the	DET
fcis-29257	76	2	outputs	output	NOUN
fcis-29257	76	3	of	of	ADP
fcis-29257	76	4	the	the	DET
fcis-29257	76	5	two	two	NUM
fcis-29257	76	6	mlps	mlp	NOUN
fcis-29257	76	7	are	be	AUX
fcis-29257	76	8	fused	fuse	VERB
fcis-29257	76	9	by	by	ADP
fcis-29257	76	10	element	element	NOUN
fcis-29257	76	11	wise	wise	ADJ
fcis-29257	76	12	addition	addition	NOUN
fcis-29257	76	13	,	,	PUNCT
fcis-29257	76	14	and	and	CCONJ
fcis-29257	76	15	the	the	DET
fcis-29257	76	16	fusion	fusion	NOUN
fcis-29257	76	17	result	result	NOUN
fcis-29257	76	18	is	be	AUX
fcis-29257	76	19	activated	activate	VERB
fcis-29257	76	20	by	by	ADP
fcis-29257	76	21	a	a	DET
fcis-29257	76	22	sigmoid	sigmoid	NOUN
fcis-29257	76	23	activation	activation	NOUN
fcis-29257	76	24	function	function	NOUN
fcis-29257	76	25	to	to	PART
fcis-29257	76	26	generate	generate	VERB
fcis-29257	76	27	attention	attention	NOUN
fcis-29257	76	28	weights	weight	NOUN
fcis-29257	76	29	for	for	ADP
fcis-29257	76	30	each	each	DET
fcis-29257	76	31	channel	channel	NOUN
fcis-29257	76	32	.	.	PUNCT
fcis-29257	77	1	the	the	DET
fcis-29257	77	2	generated	generate	VERB
fcis-29257	77	3	channel	channel	NOUN
fcis-29257	77	4	weights	weight	VERB
fcis-29257	77	5	s	s	NOUN
fcis-29257	77	6	are	be	AUX
fcis-29257	77	7	used	use	VERB
fcis-29257	77	8	to	to	PART
fcis-29257	77	9	weight	weight	VERB
fcis-29257	77	10	the	the	DET
fcis-29257	77	11	input	input	NOUN
fcis-29257	77	12	feature	feature	NOUN
fcis-29257	77	13	map	map	NOUN
fcis-29257	77	14	,	,	PUNCT
fcis-29257	77	15	and	and	CCONJ
fcis-29257	77	16	the	the	DET
fcis-29257	77	17	channel	channel	NOUN
fcis-29257	77	18	attention	attention	NOUN
fcis-29257	77	19	enhanced	enhance	VERB
fcis-29257	77	20	features	feature	NOUN
fcis-29257	77	21	are	be	AUX
fcis-29257	77	22	output	output	NOUN
fcis-29257	77	23	.	.	PUNCT
fcis-29257	78	1	(	(	PUNCT
fcis-29257	78	2	1	1	X
fcis-29257	78	3	)	)	PUNCT
fcis-29257	78	4	mlp	mlp	NOUN
fcis-29257	78	5	f	f	PROPN
fcis-29257	78	6	(	(	PUNCT
fcis-29257	78	7	2	2	NUM
fcis-29257	78	8	)	)	PUNCT
fcis-29257	78	9	w	w	PROPN
fcis-29257	78	10	f	f	PROPN
fcis-29257	78	11	(	(	PUNCT
fcis-29257	78	12	3	3	NUM
fcis-29257	78	13	)	)	PUNCT
fcis-29257	78	14	among	among	ADP
fcis-29257	78	15	them	they	PRON
fcis-29257	78	16	,	,	PUNCT
fcis-29257	78	17	∈	∈	PROPN
fcis-29257	78	18	,	,	PUNCT
fcis-29257	78	19	∈	∈	PROPN
fcis-29257	78	20	.for	.for	PUNCT
fcis-29257	79	1	the	the	DET
fcis-29257	79	2	weights	weight	NOUN
fcis-29257	79	3	of	of	ADP
fcis-29257	79	4	two	two	NUM
fcis-29257	79	5	fully	fully	ADV
fcis-29257	79	6	connected	connected	ADJ
fcis-29257	79	7	layers	layer	NOUN
fcis-29257	79	8	,	,	PUNCT
fcis-29257	79	9	where	where	SCONJ
fcis-29257	79	10	r	r	NOUN
fcis-29257	79	11	is	be	AUX
fcis-29257	79	12	the	the	DET
fcis-29257	79	13	compression	compression	NOUN
fcis-29257	79	14	factor	factor	NOUN
fcis-29257	79	15	for	for	ADP
fcis-29257	79	16	the	the	DET
fcis-29257	79	17	number	number	NOUN
fcis-29257	79	18	of	of	ADP
fcis-29257	79	19	channels	channel	NOUN
fcis-29257	79	20	.	.	PUNCT
fcis-29257	80	1	σ	σ	NOUN
fcis-29257	80	2	is	be	AUX
fcis-29257	80	3	the	the	DET
fcis-29257	80	4	activation	activation	NOUN
fcis-29257	80	5	function	function	NOUN
fcis-29257	80	6	,	,	PUNCT
fcis-29257	80	7	and	and	CCONJ
fcis-29257	80	8	relu	relu	NOUN
fcis-29257	80	9	is	be	AUX
fcis-29257	80	10	usually	usually	ADV
fcis-29257	80	11	chosen	choose	VERB
fcis-29257	80	12	.	.	PUNCT
fcis-29257	81	1	global	global	ADJ
fcis-29257	81	2	mean	mean	NOUN
fcis-29257	81	3	pooling	pooling	NOUN
fcis-29257	81	4	(	(	PUNCT
fcis-29257	81	5	avgpool	avgpool	ADV
fcis-29257	81	6	):	):	PUNCT
fcis-29257	81	7	averages	average	NOUN
fcis-29257	81	8	feature	feature	NOUN
fcis-29257	81	9	maps	map	NOUN
fcis-29257	81	10	in	in	ADP
fcis-29257	81	11	the	the	DET
fcis-29257	81	12	spatial	spatial	ADJ
fcis-29257	81	13	dimensions	dimension	NOUN
fcis-29257	81	14	(	(	PUNCT
fcis-29257	81	15	h	h	NOUN
fcis-29257	81	16	and	and	CCONJ
fcis-29257	81	17	w	w	NOUN
fcis-29257	81	18	)	)	PUNCT
fcis-29257	81	19	.	.	PUNCT
fcis-29257	82	1	global	global	ADJ
fcis-29257	82	2	maxpool	maxpool	PROPN
fcis-29257	82	3	:	:	PUNCT
fcis-29257	82	4	takes	take	VERB
fcis-29257	82	5	the	the	DET
fcis-29257	82	6	maximum	maximum	ADJ
fcis-29257	82	7	value	value	NOUN
fcis-29257	82	8	in	in	ADP
fcis-29257	82	9	the	the	DET
fcis-29257	82	10	spatial	spatial	ADJ
fcis-29257	82	11	dimension	dimension	NOUN
fcis-29257	82	12	of	of	ADP
fcis-29257	82	13	the	the	DET
fcis-29257	82	14	feature	feature	NOUN
fcis-29257	82	15	map	map	NOUN
fcis-29257	82	16	the	the	DET
fcis-29257	82	17	pooled	pool	VERB
fcis-29257	82	18	features	feature	NOUN
fcis-29257	82	19	are	be	AUX
fcis-29257	82	20	passed	pass	VERB
fcis-29257	82	21	through	through	ADP
fcis-29257	82	22	a	a	DET
fcis-29257	82	23	shared	share	VERB
fcis-29257	82	24	multilayer	multilayer	ADJ
fcis-29257	82	25	perceptron	perceptron	NOUN
fcis-29257	82	26	(	(	PUNCT
fcis-29257	82	27	mlp	mlp	PROPN
fcis-29257	82	28	)	)	PUNCT
fcis-29257	82	29	,	,	PUNCT
fcis-29257	82	30	which	which	PRON
fcis-29257	82	31	usually	usually	ADV
fcis-29257	82	32	contains	contain	VERB
fcis-29257	82	33	a	a	DET
fcis-29257	82	34	hidden	hide	VERB
fcis-29257	82	35	layer	layer	NOUN
fcis-29257	82	36	,	,	PUNCT
fcis-29257	82	37	and	and	CCONJ
fcis-29257	82	38	the	the	DET
fcis-29257	82	39	dimension	dimension	NOUN
fcis-29257	82	40	of	of	ADP
fcis-29257	82	41	the	the	DET
fcis-29257	82	42	hidden	hide	VERB
fcis-29257	82	43	layer	layer	NOUN
fcis-29257	82	44	can	can	AUX
fcis-29257	82	45	be	be	AUX
fcis-29257	82	46	compressed	compress	VERB
fcis-29257	82	47	by	by	ADP
fcis-29257	82	48	the	the	DET
fcis-29257	82	49	number	number	NOUN
fcis-29257	82	50	of	of	ADP
fcis-29257	82	51	channels	channel	NOUN
fcis-29257	82	52	.	.	PUNCT
fcis-29257	83	1	where	where	SCONJ
fcis-29257	83	2	the	the	DET
fcis-29257	83	3	sum	sum	NOUN
fcis-29257	83	4	is	be	AUX
fcis-29257	83	5	the	the	DET
fcis-29257	83	6	weight	weight	NOUN
fcis-29257	83	7	of	of	ADP
fcis-29257	83	8	the	the	DET
fcis-29257	83	9	mlp	mlp	NOUN
fcis-29257	83	10	,	,	PUNCT
fcis-29257	83	11	and	and	CCONJ
fcis-29257	83	12	σ	σ	PROPN
fcis-29257	83	13	is	be	AUX
fcis-29257	83	14	the	the	DET
fcis-29257	83	15	activation	activation	NOUN
fcis-29257	83	16	function	function	NOUN
fcis-29257	83	17	(	(	PUNCT
fcis-29257	83	18	sigmoid	sigmoid	NOUN
fcis-29257	83	19	)	)	PUNCT
fcis-29257	83	20	.	.	PUNCT
fcis-29257	84	1	by	by	ADP
fcis-29257	84	2	adding	add	VERB
fcis-29257	84	3	the	the	DET
fcis-29257	84	4	average	average	ADJ
fcis-29257	84	5	pooling	pooling	NOUN
fcis-29257	84	6	features	feature	NOUN
fcis-29257	84	7	and	and	CCONJ
fcis-29257	84	8	the	the	DET
fcis-29257	84	9	maximum	maximum	ADJ
fcis-29257	84	10	pooling	pool	VERB
fcis-29257	84	11	features	feature	NOUN
fcis-29257	84	12	of	of	ADP
fcis-29257	84	13	mlp	mlp	PROPN
fcis-29257	84	14	,	,	PUNCT
fcis-29257	84	15	the	the	DET
fcis-29257	84	16	fusion	fusion	NOUN
fcis-29257	84	17	features	feature	NOUN
fcis-29257	84	18	are	be	AUX
fcis-29257	84	19	generated	generate	VERB
fcis-29257	84	20	by	by	ADP
fcis-29257	84	21	the	the	DET
fcis-29257	84	22	sigmoid	sigmoid	NOUN
fcis-29257	84	23	activation	activation	NOUN
fcis-29257	84	24	function	function	NOUN
fcis-29257	84	25	,	,	PUNCT
fcis-29257	84	26	and	and	CCONJ
fcis-29257	84	27	each	each	DET
fcis-29257	84	28	channel	channel	NOUN
fcis-29257	84	29	of	of	ADP
fcis-29257	84	30	the	the	DET
fcis-29257	84	31	input	input	NOUN
fcis-29257	84	32	feature	feature	NOUN
fcis-29257	84	33	map	map	NOUN
fcis-29257	84	34	is	be	AUX
fcis-29257	84	35	multiplied	multiply	VERB
fcis-29257	84	36	by	by	ADP
fcis-29257	84	37	the	the	DET
fcis-29257	84	38	corresponding	correspond	VERB
fcis-29257	84	39	attention	attention	NOUN
fcis-29257	84	40	weight	weight	NOUN
fcis-29257	84	41	to	to	PART
fcis-29257	84	42	achieve	achieve	VERB
fcis-29257	84	43	channel	channel	NOUN
fcis-29257	84	44	enhancement	enhancement	NOUN
fcis-29257	84	45	.	.	PUNCT
fcis-29257	85	1	as	as	SCONJ
fcis-29257	85	2	shown	show	VERB
fcis-29257	85	3	in	in	ADP
fcis-29257	85	4	figure	figure	NOUN
fcis-29257	85	5	4	4	NUM
fcis-29257	85	6	,	,	PUNCT
fcis-29257	85	7	the	the	DET
fcis-29257	85	8	main	main	ADJ
fcis-29257	85	9	purpose	purpose	NOUN
fcis-29257	85	10	of	of	ADP
fcis-29257	85	11	the	the	DET
fcis-29257	85	12	spatial	spatial	ADJ
fcis-29257	85	13	attention	attention	NOUN
fcis-29257	85	14	mechanism	mechanism	NOUN
fcis-29257	85	15	is	be	AUX
fcis-29257	85	16	to	to	PART
fcis-29257	85	17	calculate	calculate	VERB
fcis-29257	85	18	the	the	DET
fcis-29257	85	19	attention	attention	NOUN
fcis-29257	85	20	weights	weight	NOUN
fcis-29257	85	21	in	in	ADP
fcis-29257	85	22	the	the	DET
fcis-29257	85	23	spatial	spatial	ADJ
fcis-29257	85	24	dimension	dimension	NOUN
fcis-29257	85	25	,	,	PUNCT
fcis-29257	85	26	so	so	SCONJ
fcis-29257	85	27	as	as	SCONJ
fcis-29257	85	28	to	to	PART
fcis-29257	85	29	highlight	highlight	VERB
fcis-29257	85	30	important	important	ADJ
fcis-29257	85	31	spatial	spatial	ADJ
fcis-29257	85	32	regions	region	NOUN
fcis-29257	85	33	.	.	PUNCT
fcis-29257	86	1	the	the	DET
fcis-29257	86	2	main	main	ADJ
fcis-29257	86	3	calculation	calculation	NOUN
fcis-29257	86	4	formula	formula	NOUN
fcis-29257	86	5	is	be	AUX
fcis-29257	86	6	as	as	SCONJ
fcis-29257	86	7	follows	follow	VERB
fcis-29257	86	8	.	.	PUNCT
fcis-29257	87	1	figure	figure	VERB
fcis-29257	87	2	4	4	NUM
fcis-29257	87	3	.	.	PUNCT
fcis-29257	87	4	spatial	spatial	ADJ
fcis-29257	87	5	attention	attention	NOUN
fcis-29257	87	6	mechanisms	mechanism	NOUN
fcis-29257	87	7	,	,	PUNCT
fcis-29257	87	8	∑	∑	ADP
fcis-29257	87	9	  	  	SPACE
fcis-29257	87	10	,	,	PUNCT
fcis-29257	87	11	(	(	PUNCT
fcis-29257	87	12	4	4	NUM
fcis-29257	87	13	)	)	PUNCT
fcis-29257	87	14	,	,	PUNCT
fcis-29257	87	15	  	  	SPACE
fcis-29257	87	16	,	,	PUNCT
fcis-29257	87	17	(	(	PUNCT
fcis-29257	87	18	5	5	NUM
fcis-29257	87	19	)	)	PUNCT
fcis-29257	87	20	,	,	PUNCT
fcis-29257	87	21	,	,	PUNCT
fcis-29257	87	22	,	,	PUNCT
fcis-29257	87	23	(	(	PUNCT
fcis-29257	87	24	6	6	X
fcis-29257	87	25	)	)	PUNCT
fcis-29257	87	26	convolution	convolution	NOUN
fcis-29257	87	27	(	(	PUNCT
fcis-29257	87	28	7	7	NUM
fcis-29257	87	29	)	)	PUNCT
fcis-29257	87	30	,	,	PUNCT
fcis-29257	87	31	,	,	PUNCT
fcis-29257	87	32	⋅	⋅	X
fcis-29257	87	33	,	,	PUNCT
fcis-29257	87	34	,	,	PUNCT
fcis-29257	87	35	(	(	PUNCT
fcis-29257	87	36	8)	8)	NUM
fcis-29257	87	37	,	,	PUNCT
fcis-29257	87	38	,	,	PUNCT
fcis-29257	87	39	,	,	PUNCT
fcis-29257	87	40	⋅	⋅	X
fcis-29257	87	41	,	,	PUNCT
fcis-29257	87	42	,	,	PUNCT
fcis-29257	87	43	(	(	PUNCT
fcis-29257	87	44	9	9	X
fcis-29257	87	45	)	)	PUNCT
fcis-29257	87	46	62	62	NUM
fcis-29257	87	47	perform	perform	VERB
fcis-29257	87	48	a	a	DET
fcis-29257	87	49	channel	channel	NOUN
fcis-29257	87	50	-	-	PUNCT
fcis-29257	87	51	by	by	ADP
fcis-29257	87	52	-	-	PUNCT
fcis-29257	87	53	channel	channel	NOUN
fcis-29257	87	54	global	global	ADJ
fcis-29257	87	55	average	average	ADJ
fcis-29257	87	56	pooling	pool	VERB
fcis-29257	87	57	operation	operation	NOUN
fcis-29257	87	58	on	on	ADP
fcis-29257	87	59	each	each	DET
fcis-29257	87	60	channel	channel	NOUN
fcis-29257	87	61	of	of	ADP
fcis-29257	87	62	the	the	DET
fcis-29257	87	63	input	input	NOUN
fcis-29257	87	64	feature	feature	NOUN
fcis-29257	87	65	map	map	NOUN
fcis-29257	87	66	f	f	PROPN
fcis-29257	87	67	to	to	PART
fcis-29257	87	68	obtain	obtain	VERB
fcis-29257	87	69	a	a	DET
fcis-29257	87	70	spatially	spatially	ADV
fcis-29257	87	71	averaged	average	VERB
fcis-29257	87	72	feature	feature	NOUN
fcis-29257	87	73	map	map	NOUN
fcis-29257	87	74	.	.	PUNCT
fcis-29257	88	1	then	then	ADV
fcis-29257	88	2	,	,	PUNCT
fcis-29257	88	3	a	a	DET
fcis-29257	88	4	channel	channel	NOUN
fcis-29257	88	5	by	by	ADP
fcis-29257	88	6	channel	channel	PROPN
fcis-29257	88	7	global	global	PROPN
fcis-29257	88	8	max	max	PROPN
fcis-29257	88	9	pooling	pooling	NOUN
fcis-29257	88	10	operation	operation	NOUN
fcis-29257	88	11	is	be	AUX
fcis-29257	88	12	performed	perform	VERB
fcis-29257	88	13	on	on	ADP
fcis-29257	88	14	each	each	DET
fcis-29257	88	15	channel	channel	NOUN
fcis-29257	88	16	of	of	ADP
fcis-29257	88	17	the	the	DET
fcis-29257	88	18	input	input	NOUN
fcis-29257	88	19	feature	feature	NOUN
fcis-29257	88	20	map	map	NOUN
fcis-29257	88	21	f	f	PROPN
fcis-29257	88	22	to	to	PART
fcis-29257	88	23	obtain	obtain	VERB
fcis-29257	88	24	a	a	DET
fcis-29257	88	25	spatial	spatial	ADJ
fcis-29257	88	26	dimension	dimension	NOUN
fcis-29257	88	27	maximum	maximum	ADJ
fcis-29257	88	28	feature	feature	NOUN
fcis-29257	88	29	map	map	NOUN
fcis-29257	88	30	.	.	PUNCT
fcis-29257	89	1	add	add	VERB
fcis-29257	89	2	the	the	DET
fcis-29257	89	3	two	two	NUM
fcis-29257	89	4	feature	feature	NOUN
fcis-29257	89	5	maps	map	NOUN
fcis-29257	89	6	,	,	PUNCT
fcis-29257	89	7	and	and	CCONJ
fcis-29257	89	8	,	,	PUNCT
fcis-29257	89	9	element	element	NOUN
fcis-29257	89	10	by	by	ADP
fcis-29257	89	11	element	element	NOUN
fcis-29257	89	12	to	to	PART
fcis-29257	89	13	obtain	obtain	VERB
fcis-29257	89	14	a	a	DET
fcis-29257	89	15	comprehensive	comprehensive	ADJ
fcis-29257	89	16	feature	feature	NOUN
fcis-29257	89	17	map	map	NOUN
fcis-29257	89	18	,	,	PUNCT
fcis-29257	89	19	perform	perform	VERB
fcis-29257	89	20	convolution	convolution	NOUN
fcis-29257	89	21	operation	operation	NOUN
fcis-29257	89	22	on	on	ADP
fcis-29257	89	23	the	the	DET
fcis-29257	89	24	comprehensive	comprehensive	ADJ
fcis-29257	89	25	feature	feature	NOUN
fcis-29257	89	26	map	map	NOUN
fcis-29257	89	27	to	to	PART
fcis-29257	89	28	reduce	reduce	VERB
fcis-29257	89	29	the	the	DET
fcis-29257	89	30	number	number	NOUN
fcis-29257	89	31	of	of	ADP
fcis-29257	89	32	channels	channel	NOUN
fcis-29257	89	33	and	and	CCONJ
fcis-29257	89	34	increase	increase	VERB
fcis-29257	89	35	nonlinearity	nonlinearity	NOUN
fcis-29257	89	36	.	.	PUNCT
fcis-29257	90	1	is	be	AUX
fcis-29257	90	2	the	the	DET
fcis-29257	90	3	sigmoid	sigmoid	NOUN
fcis-29257	90	4	activation	activation	NOUN
fcis-29257	90	5	function	function	NOUN
fcis-29257	90	6	.	.	PUNCT
fcis-29257	91	1	4.2	4.2	NUM
fcis-29257	91	2	.	.	PUNCT
fcis-29257	91	3	text	text	NOUN
fcis-29257	91	4	modal	modal	NOUN
fcis-29257	91	5	processing	processing	NOUN
fcis-29257	91	6	4.2.1	4.2.1	NOUN
fcis-29257	91	7	.	.	PUNCT
fcis-29257	92	1	text	text	NOUN
fcis-29257	92	2	data	datum	NOUN
fcis-29257	92	3	preprocessing	preprocessing	NOUN
fcis-29257	92	4	before	before	SCONJ
fcis-29257	92	5	you	you	PRON
fcis-29257	92	6	can	can	AUX
fcis-29257	92	7	feed	feed	VERB
fcis-29257	92	8	text	text	NOUN
fcis-29257	92	9	data	datum	NOUN
fcis-29257	92	10	into	into	ADP
fcis-29257	92	11	your	your	PRON
fcis-29257	92	12	model	model	NOUN
fcis-29257	92	13	,	,	PUNCT
fcis-29257	92	14	we	we	PRON
fcis-29257	92	15	need	need	VERB
fcis-29257	92	16	to	to	PART
fcis-29257	92	17	do	do	VERB
fcis-29257	92	18	some	some	DET
fcis-29257	92	19	preprocessing	preprocessing	NOUN
fcis-29257	92	20	steps	step	NOUN
fcis-29257	92	21	to	to	PART
fcis-29257	92	22	ensure	ensure	VERB
fcis-29257	92	23	that	that	SCONJ
fcis-29257	92	24	the	the	DET
fcis-29257	92	25	data	data	NOUN
fcis-29257	92	26	is	be	AUX
fcis-29257	92	27	suitable	suitable	ADJ
fcis-29257	92	28	for	for	ADP
fcis-29257	92	29	model	model	NOUN
fcis-29257	92	30	training	training	NOUN
fcis-29257	92	31	and	and	CCONJ
fcis-29257	92	32	inference	inference	NOUN
fcis-29257	92	33	.	.	PUNCT
fcis-29257	93	1	start	start	VERB
fcis-29257	93	2	by	by	ADP
fcis-29257	93	3	removing	remove	VERB
fcis-29257	93	4	noise	noise	NOUN
fcis-29257	93	5	from	from	ADP
fcis-29257	93	6	the	the	DET
fcis-29257	93	7	text	text	NOUN
fcis-29257	93	8	(	(	PUNCT
fcis-29257	93	9	html	html	NOUN
fcis-29257	93	10	tags	tag	NOUN
fcis-29257	93	11	,	,	PUNCT
fcis-29257	93	12	special	special	ADJ
fcis-29257	93	13	characters	character	NOUN
fcis-29257	93	14	,	,	PUNCT
fcis-29257	93	15	punctuation	punctuation	NOUN
fcis-29257	93	16	,	,	PUNCT
fcis-29257	93	17	numbers	number	NOUN
fcis-29257	93	18	,	,	PUNCT
fcis-29257	93	19	etc	etc	X
fcis-29257	93	20	.	.	X
fcis-29257	93	21	)	)	PUNCT
fcis-29257	93	22	.	.	PUNCT
fcis-29257	94	1	use	use	VERB
fcis-29257	94	2	the	the	DET
fcis-29257	94	3	jieba	jieba	NOUN
fcis-29257	94	4	tool	tool	NOUN
fcis-29257	94	5	to	to	PART
fcis-29257	94	6	tokenize	tokenize	VERB
fcis-29257	94	7	text	text	NOUN
fcis-29257	94	8	fragments	fragment	NOUN
fcis-29257	94	9	and	and	CCONJ
fcis-29257	94	10	split	split	VERB
fcis-29257	94	11	each	each	DET
fcis-29257	94	12	text	text	NOUN
fcis-29257	94	13	fragment	fragment	NOUN
fcis-29257	94	14	into	into	ADP
fcis-29257	94	15	a	a	DET
fcis-29257	94	16	word	word	NOUN
fcis-29257	94	17	list	list	NOUN
fcis-29257	94	18	.	.	PUNCT
fcis-29257	95	1	converts	convert	VERB
fcis-29257	95	2	the	the	DET
fcis-29257	95	3	segmented	segment	VERB
fcis-29257	95	4	text	text	NOUN
fcis-29257	95	5	into	into	ADP
fcis-29257	95	6	numeric	numeric	ADJ
fcis-29257	95	7	form	form	NOUN
fcis-29257	95	8	.	.	PUNCT
fcis-29257	96	1	using	use	VERB
fcis-29257	96	2	a	a	DET
fcis-29257	96	3	word	word	NOUN
fcis-29257	96	4	vector	vector	NOUN
fcis-29257	96	5	model	model	NOUN
fcis-29257	96	6	more	more	ADV
fcis-29257	96	7	suitable	suitable	ADJ
fcis-29257	96	8	for	for	ADP
fcis-29257	96	9	the	the	DET
fcis-29257	96	10	chinese	chinese	ADJ
fcis-29257	96	11	context	context	NOUN
fcis-29257	96	12	,	,	PUNCT
fcis-29257	96	13	word2vec	word2vec	PROPN
fcis-29257	96	14	,	,	PUNCT
fcis-29257	96	15	the	the	DET
fcis-29257	96	16	word	word	NOUN
fcis-29257	96	17	sequence	sequence	NOUN
fcis-29257	96	18	is	be	AUX
fcis-29257	96	19	mapped	map	VERB
fcis-29257	96	20	to	to	ADP
fcis-29257	96	21	a	a	DET
fcis-29257	96	22	high	high	ADV
fcis-29257	96	23	-	-	PUNCT
fcis-29257	96	24	dimensional	dimensional	ADJ
fcis-29257	96	25	vector	vector	NOUN
fcis-29257	96	26	to	to	PART
fcis-29257	96	27	obtain	obtain	VERB
fcis-29257	96	28	a	a	DET
fcis-29257	96	29	text	text	NOUN
fcis-29257	96	30	vector	vector	NOUN
fcis-29257	96	31	.	.	PUNCT
fcis-29257	97	1	∈	∈	PROPN
fcis-29257	97	2	r	r	NOUN
fcis-29257	97	3	(	(	PUNCT
fcis-29257	97	4	10	10	NUM
fcis-29257	97	5	)	)	PUNCT
fcis-29257	97	6	where	where	SCONJ
fcis-29257	97	7	n	n	PRON
fcis-29257	97	8	is	be	AUX
fcis-29257	97	9	the	the	DET
fcis-29257	97	10	length	length	NOUN
fcis-29257	97	11	of	of	ADP
fcis-29257	97	12	the	the	DET
fcis-29257	97	13	text	text	NOUN
fcis-29257	97	14	sequence	sequence	NOUN
fcis-29257	97	15	(	(	PUNCT
fcis-29257	97	16	i.e.	i.e.	X
fcis-29257	97	17	,	,	PUNCT
fcis-29257	97	18	the	the	DET
fcis-29257	97	19	number	number	NOUN
fcis-29257	97	20	of	of	ADP
fcis-29257	97	21	words	word	NOUN
fcis-29257	97	22	in	in	ADP
fcis-29257	97	23	the	the	DET
fcis-29257	97	24	text	text	NOUN
fcis-29257	97	25	)	)	PUNCT
fcis-29257	97	26	and	and	CCONJ
fcis-29257	97	27	d	d	PROPN
fcis-29257	97	28	is	be	AUX
fcis-29257	97	29	the	the	DET
fcis-29257	97	30	embedding	embed	VERB
fcis-29257	97	31	dimension	dimension	NOUN
fcis-29257	97	32	.	.	PUNCT
fcis-29257	98	1	4.2.2	4.2.2	X
fcis-29257	98	2	.	.	PUNCT
fcis-29257	98	3	feature	feature	NOUN
fcis-29257	98	4	extraction	extraction	NOUN
fcis-29257	98	5	in	in	ADP
fcis-29257	98	6	the	the	DET
fcis-29257	98	7	text	text	NOUN
fcis-29257	98	8	classification	classification	NOUN
fcis-29257	98	9	task	task	NOUN
fcis-29257	98	10	,	,	PUNCT
fcis-29257	98	11	text	text	NOUN
fcis-29257	98	12	-	-	PUNCT
fcis-29257	98	13	cnn	cnn	PROPN
fcis-29257	98	14	(	(	PUNCT
fcis-29257	98	15	text	text	NOUN
fcis-29257	98	16	convolutional	convolutional	ADJ
fcis-29257	98	17	neural	neural	ADJ
fcis-29257	98	18	network	network	NOUN
fcis-29257	98	19	)	)	PUNCT
fcis-29257	98	20	effectively	effectively	ADV
fcis-29257	98	21	extracts	extract	VERB
fcis-29257	98	22	features	feature	NOUN
fcis-29257	98	23	from	from	ADP
fcis-29257	98	24	text	text	NOUN
fcis-29257	98	25	data	datum	NOUN
fcis-29257	98	26	through	through	ADP
fcis-29257	98	27	a	a	DET
fcis-29257	98	28	series	series	NOUN
fcis-29257	98	29	of	of	ADP
fcis-29257	98	30	convolution	convolution	NOUN
fcis-29257	98	31	and	and	CCONJ
fcis-29257	98	32	pooling	pool	VERB
fcis-29257	98	33	operations	operation	NOUN
fcis-29257	98	34	.	.	PUNCT
fcis-29257	99	1	the	the	DET
fcis-29257	99	2	core	core	NOUN
fcis-29257	99	3	mechanism	mechanism	NOUN
fcis-29257	99	4	lies	lie	VERB
fcis-29257	99	5	in	in	ADP
fcis-29257	99	6	the	the	DET
fcis-29257	99	7	use	use	NOUN
fcis-29257	99	8	of	of	ADP
fcis-29257	99	9	convolutional	convolutional	ADJ
fcis-29257	99	10	layers	layer	NOUN
fcis-29257	99	11	,	,	PUNCT
fcis-29257	99	12	which	which	PRON
fcis-29257	99	13	are	be	AUX
fcis-29257	99	14	able	able	ADJ
fcis-29257	99	15	to	to	PART
fcis-29257	99	16	slide	slide	VERB
fcis-29257	99	17	filters	filter	NOUN
fcis-29257	99	18	(	(	PUNCT
fcis-29257	99	19	or	or	CCONJ
fcis-29257	99	20	convolutional	convolutional	ADJ
fcis-29257	99	21	kernels	kernel	NOUN
fcis-29257	99	22	)	)	PUNCT
fcis-29257	99	23	of	of	ADP
fcis-29257	99	24	various	various	ADJ
fcis-29257	99	25	sizes	size	NOUN
fcis-29257	99	26	over	over	ADP
fcis-29257	99	27	the	the	DET
fcis-29257	99	28	input	input	NOUN
fcis-29257	99	29	word	word	NOUN
fcis-29257	99	30	embedding	embed	VERB
fcis-29257	99	31	matrix	matrix	NOUN
fcis-29257	99	32	to	to	PART
fcis-29257	99	33	capture	capture	VERB
fcis-29257	99	34	local	local	ADJ
fcis-29257	99	35	semantic	semantic	ADJ
fcis-29257	99	36	features	feature	NOUN
fcis-29257	99	37	in	in	ADP
fcis-29257	99	38	the	the	DET
fcis-29257	99	39	text	text	NOUN
fcis-29257	99	40	.	.	PUNCT
fcis-29257	100	1	specifically	specifically	ADV
fcis-29257	100	2	,	,	PUNCT
fcis-29257	100	3	the	the	DET
fcis-29257	100	4	convolutional	convolutional	ADJ
fcis-29257	100	5	layer	layer	NOUN
fcis-29257	100	6	of	of	ADP
fcis-29257	100	7	text	text	NOUN
fcis-29257	100	8	-	-	PUNCT
fcis-29257	100	9	cnn	cnn	PROPN
fcis-29257	100	10	is	be	AUX
fcis-29257	100	11	configured	configure	VERB
fcis-29257	100	12	with	with	ADP
fcis-29257	100	13	multiple	multiple	ADJ
fcis-29257	100	14	filters	filter	NOUN
fcis-29257	100	15	of	of	ADP
fcis-29257	100	16	different	different	ADJ
fcis-29257	100	17	sizes	size	NOUN
fcis-29257	100	18	,	,	PUNCT
fcis-29257	100	19	using	use	VERB
fcis-29257	100	20	filters	filter	NOUN
fcis-29257	100	21	of	of	ADP
fcis-29257	100	22	3	3	NUM
fcis-29257	100	23	,	,	PUNCT
fcis-29257	100	24	4	4	NUM
fcis-29257	100	25	,	,	PUNCT
fcis-29257	100	26	and	and	CCONJ
fcis-29257	100	27	5	5	NUM
fcis-29257	100	28	word	word	NOUN
fcis-29257	100	29	lengths	length	NOUN
fcis-29257	100	30	,	,	PUNCT
fcis-29257	100	31	which	which	PRON
fcis-29257	100	32	are	be	AUX
fcis-29257	100	33	specifically	specifically	ADV
fcis-29257	100	34	used	use	VERB
fcis-29257	100	35	to	to	PART
fcis-29257	100	36	identify	identify	VERB
fcis-29257	100	37	and	and	CCONJ
fcis-29257	100	38	extract	extract	VERB
fcis-29257	100	39	the	the	DET
fcis-29257	100	40	combined	combine	VERB
fcis-29257	100	41	features	feature	NOUN
fcis-29257	100	42	of	of	ADP
fcis-29257	100	43	consecutive	consecutive	ADJ
fcis-29257	100	44	n	n	CCONJ
fcis-29257	100	45	words	word	NOUN
fcis-29257	100	46	.	.	PUNCT
fcis-29257	101	1	in	in	ADP
fcis-29257	101	2	this	this	DET
fcis-29257	101	3	way	way	NOUN
fcis-29257	101	4	,	,	PUNCT
fcis-29257	101	5	the	the	DET
fcis-29257	101	6	model	model	NOUN
fcis-29257	101	7	has	have	VERB
fcis-29257	101	8	the	the	DET
fcis-29257	101	9	flexibility	flexibility	NOUN
fcis-29257	101	10	to	to	PART
fcis-29257	101	11	capture	capture	VERB
fcis-29257	101	12	phrases	phrase	NOUN
fcis-29257	101	13	and	and	CCONJ
fcis-29257	101	14	structures	structure	NOUN
fcis-29257	101	15	of	of	ADP
fcis-29257	101	16	different	different	ADJ
fcis-29257	101	17	lengths	length	NOUN
fcis-29257	101	18	,	,	PUNCT
fcis-29257	101	19	which	which	PRON
fcis-29257	101	20	is	be	AUX
fcis-29257	101	21	essential	essential	ADJ
fcis-29257	101	22	for	for	ADP
fcis-29257	101	23	understanding	understand	VERB
fcis-29257	101	24	the	the	DET
fcis-29257	101	25	semantics	semantic	NOUN
fcis-29257	101	26	of	of	ADP
fcis-29257	101	27	the	the	DET
fcis-29257	101	28	text	text	NOUN
fcis-29257	101	29	.	.	PUNCT
fcis-29257	102	1	in	in	ADP
fcis-29257	102	2	the	the	DET
fcis-29257	102	3	feature	feature	NOUN
fcis-29257	102	4	extraction	extraction	NOUN
fcis-29257	102	5	process	process	NOUN
fcis-29257	102	6	,	,	PUNCT
fcis-29257	102	7	the	the	DET
fcis-29257	102	8	convolution	convolution	NOUN
fcis-29257	102	9	operation	operation	NOUN
fcis-29257	102	10	first	first	ADV
fcis-29257	102	11	slides	slide	VERB
fcis-29257	102	12	these	these	DET
fcis-29257	102	13	filters	filter	NOUN
fcis-29257	102	14	across	across	ADP
fcis-29257	102	15	the	the	DET
fcis-29257	102	16	word	word	NOUN
fcis-29257	102	17	embedding	embed	VERB
fcis-29257	102	18	matrix	matrix	NOUN
fcis-29257	102	19	to	to	PART
fcis-29257	102	20	generate	generate	VERB
fcis-29257	102	21	a	a	DET
fcis-29257	102	22	series	series	NOUN
fcis-29257	102	23	of	of	ADP
fcis-29257	102	24	feature	feature	NOUN
fcis-29257	102	25	maps	map	NOUN
fcis-29257	102	26	,	,	PUNCT
fcis-29257	102	27	each	each	PRON
fcis-29257	102	28	corresponding	correspond	VERB
fcis-29257	102	29	to	to	ADP
fcis-29257	102	30	a	a	DET
fcis-29257	102	31	specific	specific	ADJ
fcis-29257	102	32	type	type	NOUN
fcis-29257	102	33	of	of	ADP
fcis-29257	102	34	local	local	ADJ
fcis-29257	102	35	feature	feature	NOUN
fcis-29257	102	36	captured	capture	VERB
fcis-29257	102	37	by	by	ADP
fcis-29257	102	38	one	one	NUM
fcis-29257	102	39	filter	filter	NOUN
fcis-29257	102	40	.	.	PUNCT
fcis-29257	103	1	after	after	SCONJ
fcis-29257	103	2	the	the	DET
fcis-29257	103	3	nonlinear	nonlinear	ADJ
fcis-29257	103	4	activation	activation	NOUN
fcis-29257	103	5	function	function	NOUN
fcis-29257	103	6	(	(	PUNCT
fcis-29257	103	7	relu	relu	NOUN
fcis-29257	103	8	)	)	PUNCT
fcis-29257	103	9	is	be	AUX
fcis-29257	103	10	processed	process	VERB
fcis-29257	103	11	,	,	PUNCT
fcis-29257	103	12	these	these	DET
fcis-29257	103	13	feature	feature	NOUN
fcis-29257	103	14	maps	map	NOUN
fcis-29257	103	15	are	be	AUX
fcis-29257	103	16	passed	pass	VERB
fcis-29257	103	17	to	to	ADP
fcis-29257	103	18	the	the	DET
fcis-29257	103	19	pooling	pool	VERB
fcis-29257	103	20	layer	layer	NOUN
fcis-29257	103	21	to	to	PART
fcis-29257	103	22	reduce	reduce	VERB
fcis-29257	103	23	the	the	DET
fcis-29257	103	24	dimensionality	dimensionality	NOUN
fcis-29257	103	25	and	and	CCONJ
fcis-29257	103	26	select	select	VERB
fcis-29257	103	27	the	the	DET
fcis-29257	103	28	features	feature	NOUN
fcis-29257	103	29	generated	generate	VERB
fcis-29257	103	30	by	by	ADP
fcis-29257	103	31	the	the	DET
fcis-29257	103	32	convolution	convolution	NOUN
fcis-29257	103	33	.	.	PUNCT
fcis-29257	104	1	for	for	ADP
fcis-29257	104	2	the	the	DET
fcis-29257	104	3	text	text	NOUN
fcis-29257	104	4	transcription	transcription	NOUN
fcis-29257	104	5	information	information	NOUN
fcis-29257	104	6	of	of	ADP
fcis-29257	104	7	a	a	DET
fcis-29257	104	8	given	give	VERB
fcis-29257	104	9	speech	speech	NOUN
fcis-29257	104	10	segment	segment	NOUN
fcis-29257	104	11	,	,	PUNCT
fcis-29257	104	12	jieba	jieba	NOUN
fcis-29257	104	13	tool	tool	NOUN
fcis-29257	104	14	is	be	AUX
fcis-29257	104	15	used	use	VERB
fcis-29257	104	16	for	for	ADP
fcis-29257	104	17	preprocessing	preprocesse	VERB
fcis-29257	104	18	such	such	ADJ
fcis-29257	104	19	as	as	ADP
fcis-29257	104	20	segmentation	segmentation	NOUN
fcis-29257	104	21	and	and	CCONJ
fcis-29257	104	22	cleaning	cleaning	NOUN
fcis-29257	104	23	of	of	ADP
fcis-29257	104	24	the	the	DET
fcis-29257	104	25	text	text	NOUN
fcis-29257	104	26	segment	segment	NOUN
fcis-29257	104	27	.	.	PUNCT
fcis-29257	105	1	each	each	DET
fcis-29257	105	2	text	text	NOUN
fcis-29257	105	3	segment	segment	NOUN
fcis-29257	105	4	is	be	AUX
fcis-29257	105	5	divided	divide	VERB
fcis-29257	105	6	into	into	ADP
fcis-29257	105	7	a	a	DET
fcis-29257	105	8	word	word	NOUN
fcis-29257	105	9	list	list	NOUN
fcis-29257	105	10	,	,	PUNCT
fcis-29257	105	11	,	,	PUNCT
fcis-29257	105	12	⋯	⋯	PROPN
fcis-29257	105	13	,	,	PUNCT
fcis-29257	105	14	,	,	PUNCT
fcis-29257	105	15	and	and	CCONJ
fcis-29257	105	16	the	the	DET
fcis-29257	105	17	maximum	maximum	ADJ
fcis-29257	105	18	word	word	NOUN
fcis-29257	105	19	length	length	NOUN
fcis-29257	105	20	of	of	ADP
fcis-29257	105	21	the	the	DET
fcis-29257	105	22	text	text	NOUN
fcis-29257	105	23	is	be	AUX
fcis-29257	105	24	set	set	VERB
fcis-29257	105	25	to	to	PART
fcis-29257	105	26	n.	n.	VERB
fcis-29257	105	27	the	the	DET
fcis-29257	105	28	excess	excess	ADJ
fcis-29257	105	29	part	part	NOUN
fcis-29257	105	30	of	of	ADP
fcis-29257	105	31	the	the	DET
fcis-29257	105	32	text	text	NOUN
fcis-29257	105	33	longer	long	ADV
fcis-29257	105	34	than	than	ADP
fcis-29257	105	35	n	n	PRON
fcis-29257	105	36	words	word	NOUN
fcis-29257	105	37	will	will	AUX
fcis-29257	105	38	be	be	AUX
fcis-29257	105	39	discarded	discard	VERB
fcis-29257	105	40	,	,	PUNCT
fcis-29257	105	41	and	and	CCONJ
fcis-29257	105	42	the	the	DET
fcis-29257	105	43	text	text	NOUN
fcis-29257	105	44	less	less	ADJ
fcis-29257	105	45	than	than	ADP
fcis-29257	105	46	n	n	PRON
fcis-29257	105	47	words	word	NOUN
fcis-29257	105	48	will	will	AUX
fcis-29257	105	49	be	be	AUX
fcis-29257	105	50	filled	fill	VERB
fcis-29257	105	51	with	with	ADP
fcis-29257	105	52	zeros	zero	NOUN
fcis-29257	105	53	.	.	PUNCT
fcis-29257	106	1	then	then	ADV
fcis-29257	106	2	,	,	PUNCT
fcis-29257	106	3	a	a	DET
fcis-29257	106	4	300	300	NUM
fcis-29257	106	5	dimensional	dimensional	ADJ
fcis-29257	106	6	word	word	NOUN
fcis-29257	106	7	vector	vector	NOUN
fcis-29257	106	8	is	be	AUX
fcis-29257	106	9	used	use	VERB
fcis-29257	106	10	for	for	ADP
fcis-29257	106	11	representation	representation	NOUN
fcis-29257	106	12	embedding	embed	VERB
fcis-29257	106	13	,	,	PUNCT
fcis-29257	106	14	resulting	result	VERB
fcis-29257	106	15	in	in	ADP
fcis-29257	106	16	a	a	DET
fcis-29257	106	17	text	text	NOUN
fcis-29257	106	18	vector	vector	NOUN
fcis-29257	106	19	,	,	PUNCT
fcis-29257	106	20	,	,	PUNCT
fcis-29257	106	21	⋯	⋯	PROPN
fcis-29257	106	22	,	,	PUNCT
fcis-29257	106	23	.	.	PUNCT
fcis-29257	107	1	then	then	ADV
fcis-29257	107	2	,	,	PUNCT
fcis-29257	107	3	by	by	ADP
fcis-29257	107	4	encoding	encode	VERB
fcis-29257	107	5	the	the	DET
fcis-29257	107	6	text	text	NOUN
fcis-29257	107	7	data	datum	NOUN
fcis-29257	107	8	,	,	PUNCT
fcis-29257	107	9	the	the	DET
fcis-29257	107	10	text	text	NOUN
fcis-29257	107	11	feature	feature	NOUN
fcis-29257	107	12	is	be	AUX
fcis-29257	107	13	obtained	obtain	VERB
fcis-29257	107	14	.	.	PUNCT
fcis-29257	108	1	4.3	4.3	NUM
fcis-29257	108	2	.	.	PUNCT
fcis-29257	108	3	multimodal	multimodal	NOUN
fcis-29257	108	4	fusion	fusion	NOUN
fcis-29257	108	5	the	the	DET
fcis-29257	108	6	text	text	NOUN
fcis-29257	108	7	features	feature	NOUN
fcis-29257	108	8	and	and	CCONJ
fcis-29257	108	9	acoustic	acoustic	ADJ
fcis-29257	108	10	features	feature	NOUN
fcis-29257	108	11	were	be	AUX
fcis-29257	108	12	fused	fuse	VERB
fcis-29257	108	13	at	at	ADP
fcis-29257	108	14	the	the	DET
fcis-29257	108	15	feature	feature	NOUN
fcis-29257	108	16	layer	layer	NOUN
fcis-29257	108	17	,	,	PUNCT
fcis-29257	108	18	firstly	firstly	ADV
fcis-29257	108	19	,	,	PUNCT
fcis-29257	108	20	the	the	DET
fcis-29257	108	21	acoustic	acoustic	ADJ
fcis-29257	108	22	features	feature	NOUN
fcis-29257	108	23	obtained	obtain	VERB
fcis-29257	108	24	by	by	ADP
fcis-29257	108	25	the	the	DET
fcis-29257	108	26	acoustic	acoustic	ADJ
fcis-29257	108	27	composite	composite	ADJ
fcis-29257	108	28	features	feature	NOUN
fcis-29257	108	29	encoded	encode	VERB
fcis-29257	108	30	by	by	ADP
fcis-29257	108	31	the	the	DET
fcis-29257	108	32	model	model	NOUN
fcis-29257	108	33	and	and	CCONJ
fcis-29257	108	34	the	the	DET
fcis-29257	108	35	text	text	NOUN
fcis-29257	108	36	features	feature	NOUN
fcis-29257	108	37	obtained	obtain	VERB
fcis-29257	108	38	by	by	ADP
fcis-29257	108	39	word	word	NOUN
fcis-29257	108	40	embedding	embed	VERB
fcis-29257	108	41	and	and	CCONJ
fcis-29257	108	42	network	network	NOUN
fcis-29257	108	43	encoding	encoding	NOUN
fcis-29257	108	44	were	be	AUX
fcis-29257	108	45	fused	fuse	VERB
fcis-29257	108	46	.	.	PUNCT
fcis-29257	109	1	perform	perform	NOUN
fcis-29257	109	2	series	series	PROPN
fcis-29257	109	3	connection	connection	NOUN
fcis-29257	109	4	to	to	PART
fcis-29257	109	5	obtain	obtain	VERB
fcis-29257	109	6	features	feature	NOUN
fcis-29257	109	7	.	.	PUNCT
fcis-29257	110	1	concat	concat	NOUN
fcis-29257	110	2	.	.	PUNCT
fcis-29257	111	1	(	(	PUNCT
fcis-29257	111	2	11	11	NUM
fcis-29257	111	3	)	)	PUNCT
fcis-29257	111	4	where	where	SCONJ
fcis-29257	111	5	is	be	AUX
fcis-29257	111	6	the	the	DET
fcis-29257	111	7	concatenation	concatenation	NOUN
fcis-29257	111	8	function.concat	function.concat	PROPN
fcis-29257	111	9	then	then	ADV
fcis-29257	111	10	,	,	PUNCT
fcis-29257	111	11	the	the	DET
fcis-29257	111	12	function	function	NOUN
fcis-29257	111	13	is	be	AUX
fcis-29257	111	14	applied	apply	VERB
fcis-29257	111	15	to	to	PART
fcis-29257	111	16	predict	predict	VERB
fcis-29257	111	17	sentiment	sentiment	NOUN
fcis-29257	111	18	classification	classification	NOUN
fcis-29257	111	19	,	,	PUNCT
fcis-29257	111	20	and	and	CCONJ
fcis-29257	111	21	the	the	DET
fcis-29257	111	22	distribution	distribution	NOUN
fcis-29257	111	23	probability	probability	NOUN
fcis-29257	111	24	of	of	ADP
fcis-29257	111	25	the	the	DET
fcis-29257	111	26	prediction	prediction	NOUN
fcis-29257	111	27	target	target	NOUN
fcis-29257	111	28	is	be	AUX
fcis-29257	111	29	calculated	calculate	VERB
fcis-29257	111	30	as	as	ADP
fcis-29257	111	31	follows.softmax	follows.softmax	PROPN
fcis-29257	111	32	softmax	softmax	X
fcis-29257	111	33	(	(	PUNCT
fcis-29257	111	34	12	12	NUM
fcis-29257	111	35	)	)	PUNCT
fcis-29257	111	36	where	where	SCONJ
fcis-29257	111	37	sum	sum	NOUN
fcis-29257	111	38	is	be	AUX
fcis-29257	111	39	the	the	DET
fcis-29257	111	40	learned	learn	VERB
fcis-29257	111	41	parameter	parameter	NOUN
fcis-29257	111	42	matrix	matrix	NOUN
fcis-29257	111	43	and	and	CCONJ
fcis-29257	111	44	bias	bias	NOUN
fcis-29257	111	45	vector	vector	NOUN
fcis-29257	111	46	5	5	NUM
fcis-29257	111	47	.	.	PUNCT
fcis-29257	111	48	experimental	experimental	ADJ
fcis-29257	111	49	results	result	NOUN
fcis-29257	111	50	and	and	CCONJ
fcis-29257	111	51	evaluation	evaluation	NOUN
fcis-29257	111	52	indicators	indicator	NOUN
fcis-29257	111	53	in	in	ADP
fcis-29257	111	54	this	this	DET
fcis-29257	111	55	study	study	NOUN
fcis-29257	111	56	,	,	PUNCT
fcis-29257	111	57	we	we	PRON
fcis-29257	111	58	implemented	implement	VERB
fcis-29257	111	59	a	a	DET
fcis-29257	111	60	prototype	prototype	NOUN
fcis-29257	111	61	of	of	ADP
fcis-29257	111	62	a	a	DET
fcis-29257	111	63	multimodal	multimodal	ADJ
fcis-29257	111	64	emotion	emotion	NOUN
fcis-29257	111	65	recognition	recognition	NOUN
fcis-29257	111	66	algorithm	algorithm	NOUN
fcis-29257	111	67	and	and	CCONJ
fcis-29257	111	68	evaluated	evaluate	VERB
fcis-29257	111	69	the	the	DET
fcis-29257	111	70	performance	performance	NOUN
fcis-29257	111	71	of	of	ADP
fcis-29257	111	72	the	the	DET
fcis-29257	111	73	dataset	dataset	NOUN
fcis-29257	111	74	.	.	PUNCT
fcis-29257	112	1	to	to	PART
fcis-29257	112	2	train	train	VERB
fcis-29257	112	3	this	this	DET
fcis-29257	112	4	dataset	dataset	NOUN
fcis-29257	112	5	,	,	PUNCT
fcis-29257	112	6	the	the	DET
fcis-29257	112	7	model	model	NOUN
fcis-29257	112	8	went	go	VERB
fcis-29257	112	9	through	through	ADP
fcis-29257	112	10	50	50	NUM
fcis-29257	112	11	steps	step	NOUN
fcis-29257	112	12	of	of	ADP
fcis-29257	112	13	training	training	NOUN
fcis-29257	112	14	.	.	PUNCT
fcis-29257	113	1	the	the	DET
fcis-29257	113	2	optimizer	optimizer	NOUN
fcis-29257	113	3	used	use	VERB
fcis-29257	113	4	is	be	AUX
fcis-29257	113	5	adam	adam	PROPN
fcis-29257	113	6	.	.	PUNCT
fcis-29257	114	1	we	we	PRON
fcis-29257	114	2	set	set	VERB
fcis-29257	114	3	the	the	DET
fcis-29257	114	4	learning	learning	NOUN
fcis-29257	114	5	rate	rate	NOUN
fcis-29257	114	6	to	to	ADP
fcis-29257	114	7	1e-5	1e-5	NUM
fcis-29257	114	8	and	and	CCONJ
fcis-29257	114	9	the	the	DET
fcis-29257	114	10	weight	weight	NOUN
fcis-29257	114	11	decay	decay	NOUN
fcis-29257	114	12	to	to	ADP
fcis-29257	114	13	1e-3	1e-3	NUM
fcis-29257	114	14	.	.	PUNCT
fcis-29257	115	1	we	we	PRON
fcis-29257	115	2	used	use	VERB
fcis-29257	115	3	all	all	DET
fcis-29257	115	4	samples	sample	NOUN
fcis-29257	115	5	for	for	ADP
fcis-29257	115	6	6	6	NUM
fcis-29257	115	7	categories	category	NOUN
fcis-29257	115	8	,	,	PUNCT
fcis-29257	115	9	including	include	VERB
fcis-29257	115	10	ang	ang	PROPN
fcis-29257	115	11	,	,	PUNCT
fcis-29257	115	12	happy	happy	ADJ
fcis-29257	115	13	,	,	PUNCT
fcis-29257	115	14	neutral	neutral	ADJ
fcis-29257	115	15	,	,	PUNCT
fcis-29257	115	16	and	and	CCONJ
fcis-29257	115	17	sad	sad	ADJ
fcis-29257	115	18	,	,	PUNCT
fcis-29257	115	19	fear	fear	NOUN
fcis-29257	115	20	,	,	PUNCT
fcis-29257	115	21	surprise	surprise	NOUN
fcis-29257	115	22	.	.	PUNCT
fcis-29257	116	1	this	this	PRON
fcis-29257	116	2	is	be	AUX
fcis-29257	116	3	a	a	DET
fcis-29257	116	4	common	common	ADJ
fcis-29257	116	5	setting	setting	NOUN
fcis-29257	116	6	for	for	ADP
fcis-29257	116	7	emotion	emotion	NOUN
fcis-29257	116	8	recognition	recognition	NOUN
fcis-29257	116	9	.	.	PUNCT
fcis-29257	117	1	we	we	PRON
fcis-29257	117	2	use	use	VERB
fcis-29257	117	3	accuracy	accuracy	NOUN
fcis-29257	117	4	to	to	PART
fcis-29257	117	5	comprehensively	comprehensively	ADV
fcis-29257	117	6	measure	measure	VERB
fcis-29257	117	7	the	the	DET
fcis-29257	117	8	performance	performance	NOUN
fcis-29257	117	9	of	of	ADP
fcis-29257	117	10	the	the	DET
fcis-29257	117	11	model	model	NOUN
fcis-29257	117	12	.	.	PUNCT
fcis-29257	118	1	the	the	DET
fcis-29257	118	2	confusion	confusion	NOUN
fcis-29257	118	3	matrix	matrix	NOUN
fcis-29257	118	4	is	be	AUX
fcis-29257	118	5	a	a	DET
fcis-29257	118	6	table	table	NOUN
fcis-29257	118	7	used	use	VERB
fcis-29257	118	8	to	to	PART
fcis-29257	118	9	evaluate	evaluate	VERB
fcis-29257	118	10	the	the	DET
fcis-29257	118	11	performance	performance	NOUN
fcis-29257	118	12	of	of	ADP
fcis-29257	118	13	classification	classification	NOUN
fcis-29257	118	14	models	model	NOUN
fcis-29257	118	15	.	.	PUNCT
fcis-29257	119	1	it	it	PRON
fcis-29257	119	2	displays	display	VERB
fcis-29257	119	3	the	the	DET
fcis-29257	119	4	correspondence	correspondence	NOUN
fcis-29257	119	5	between	between	ADP
fcis-29257	119	6	the	the	DET
fcis-29257	119	7	predicted	predict	VERB
fcis-29257	119	8	results	result	NOUN
fcis-29257	119	9	of	of	ADP
fcis-29257	119	10	the	the	DET
fcis-29257	119	11	model	model	NOUN
fcis-29257	119	12	and	and	CCONJ
fcis-29257	119	13	the	the	DET
fcis-29257	119	14	actual	actual	ADJ
fcis-29257	119	15	labels	label	NOUN
fcis-29257	119	16	,	,	PUNCT
fcis-29257	119	17	helping	help	VERB
fcis-29257	119	18	us	we	PRON
fcis-29257	119	19	to	to	PART
fcis-29257	119	20	gain	gain	VERB
fcis-29257	119	21	a	a	DET
fcis-29257	119	22	detailed	detailed	ADJ
fcis-29257	119	23	understanding	understanding	NOUN
fcis-29257	119	24	of	of	ADP
fcis-29257	119	25	the	the	DET
fcis-29257	119	26	model	model	NOUN
fcis-29257	119	27	's	's	PART
fcis-29257	119	28	performance	performance	NOUN
fcis-29257	119	29	in	in	ADP
fcis-29257	119	30	different	different	ADJ
fcis-29257	119	31	categories	category	NOUN
fcis-29257	119	32	.	.	PUNCT
fcis-29257	120	1	we	we	PRON
fcis-29257	120	2	split	split	VERB
fcis-29257	120	3	the	the	DET
fcis-29257	120	4	dataset	dataset	NOUN
fcis-29257	120	5	into	into	ADP
fcis-29257	120	6	8/1/1	8/1/1	NUM
fcis-29257	120	7	for	for	ADP
fcis-29257	120	8	training	training	NOUN
fcis-29257	120	9	/	/	SYM
fcis-29257	120	10	val	val	ADJ
fcis-29257	120	11	/	/	SYM
fcis-29257	120	12	test	test	NOUN
fcis-29257	120	13	settings	setting	NOUN
fcis-29257	120	14	.	.	PUNCT
fcis-29257	121	1	we	we	PRON
fcis-29257	121	2	trained	train	VERB
fcis-29257	121	3	our	our	PRON
fcis-29257	121	4	model	model	NOUN
fcis-29257	121	5	in	in	ADP
fcis-29257	121	6	the	the	DET
fcis-29257	121	7	training	training	NOUN
fcis-29257	121	8	section	section	NOUN
fcis-29257	121	9	,	,	PUNCT
fcis-29257	121	10	which	which	PRON
fcis-29257	121	11	includes	include	VERB
fcis-29257	121	12	80	80	NUM
fcis-29257	121	13	%	%	NOUN
fcis-29257	121	14	of	of	ADP
fcis-29257	121	15	the	the	DET
fcis-29257	121	16	data	datum	NOUN
fcis-29257	121	17	in	in	ADP
fcis-29257	121	18	the	the	DET
fcis-29257	121	19	dataset	dataset	NOUN
fcis-29257	121	20	.	.	PUNCT
fcis-29257	122	1	the	the	DET
fcis-29257	122	2	final	final	ADJ
fcis-29257	122	3	model	model	NOUN
fcis-29257	122	4	was	be	AUX
fcis-29257	122	5	selected	select	VERB
fcis-29257	122	6	based	base	VERB
fcis-29257	122	7	on	on	ADP
fcis-29257	122	8	their	their	PRON
fcis-29257	122	9	performance	performance	NOUN
fcis-29257	122	10	in	in	ADP
fcis-29257	122	11	the	the	DET
fcis-29257	122	12	10	10	NUM
fcis-29257	122	13	%	%	NOUN
fcis-29257	122	14	validation	validation	NOUN
fcis-29257	122	15	section	section	NOUN
fcis-29257	122	16	.	.	PUNCT
fcis-29257	123	1	to	to	PART
fcis-29257	123	2	demonstrate	demonstrate	VERB
fcis-29257	123	3	the	the	DET
fcis-29257	123	4	effectiveness	effectiveness	NOUN
fcis-29257	123	5	of	of	ADP
fcis-29257	123	6	the	the	DET
fcis-29257	123	7	dataset	dataset	NOUN
fcis-29257	123	8	and	and	CCONJ
fcis-29257	123	9	evaluate	evaluate	VERB
fcis-29257	123	10	the	the	DET
fcis-29257	123	11	performance	performance	NOUN
fcis-29257	123	12	of	of	ADP
fcis-29257	123	13	the	the	DET
fcis-29257	123	14	model	model	NOUN
fcis-29257	123	15	,	,	PUNCT
fcis-29257	123	16	we	we	PRON
fcis-29257	123	17	implemented	implement	VERB
fcis-29257	123	18	three	three	NUM
fcis-29257	123	19	other	other	ADJ
fcis-29257	123	20	models	model	NOUN
fcis-29257	123	21	for	for	ADP
fcis-29257	123	22	comparison	comparison	NOUN
fcis-29257	123	23	(	(	PUNCT
fcis-29257	123	24	i.e.	i.e.	X
fcis-29257	123	25	table	table	NOUN
fcis-29257	123	26	2	2	NUM
fcis-29257	123	27	)	)	PUNCT
fcis-29257	123	28	.	.	PUNCT
fcis-29257	124	1	table	table	NOUN
fcis-29257	124	2	2	2	NUM
fcis-29257	124	3	shows	show	VERB
fcis-29257	124	4	the	the	DET
fcis-29257	124	5	results	result	NOUN
fcis-29257	124	6	of	of	ADP
fcis-29257	124	7	different	different	ADJ
fcis-29257	124	8	models	model	NOUN
fcis-29257	124	9	,	,	PUNCT
fcis-29257	124	10	compared	compare	VERB
fcis-29257	124	11	with	with	ADP
fcis-29257	124	12	our	our	PRON
fcis-29257	124	13	model	model	NOUN
fcis-29257	124	14	,	,	PUNCT
fcis-29257	124	15	which	which	PRON
fcis-29257	124	16	achieves	achieve	VERB
fcis-29257	124	17	improved	improve	VERB
fcis-29257	124	18	multimodal	multimodal	NOUN
fcis-29257	124	19	fusion	fusion	NOUN
fcis-29257	124	20	through	through	ADP
fcis-29257	124	21	enhanced	enhance	VERB
fcis-29257	124	22	attention	attention	NOUN
fcis-29257	124	23	mechanism	mechanism	NOUN
fcis-29257	124	24	.	.	PUNCT
fcis-29257	125	1	below	below	ADV
fcis-29257	125	2	are	be	AUX
fcis-29257	125	3	our	our	PRON
fcis-29257	125	4	proposed	propose	VERB
fcis-29257	125	5	models	model	NOUN
fcis-29257	125	6	(	(	PUNCT
fcis-29257	125	7	custom	custom	NOUN
fcis-29257	125	8	cnn	cnn	PROPN
fcis-29257	125	9	)	)	PUNCT
fcis-29257	125	10	and	and	CCONJ
fcis-29257	125	11	text	text	NOUN
fcis-29257	125	12	cnn	cnn	PROPN
fcis-29257	125	13	,	,	PUNCT
fcis-29257	125	14	as	as	ADV
fcis-29257	125	15	well	well	ADV
fcis-29257	125	16	as	as	ADP
fcis-29257	125	17	the	the	DET
fcis-29257	125	18	accuracy	accuracy	NOUN
fcis-29257	125	19	after	after	ADP
fcis-29257	125	20	modal	modal	ADJ
fcis-29257	125	21	fusion	fusion	NOUN
fcis-29257	125	22	.	.	PUNCT
fcis-29257	126	1	figure	figure	NOUN
fcis-29257	126	2	5	5	NUM
fcis-29257	126	3	shows	show	VERB
fcis-29257	126	4	the	the	DET
fcis-29257	126	5	loss	loss	NOUN
fcis-29257	126	6	of	of	ADP
fcis-29257	126	7	two	two	NUM
fcis-29257	126	8	models	model	NOUN
fcis-29257	126	9	on	on	ADP
fcis-29257	126	10	the	the	DET
fcis-29257	126	11	training	training	NOUN
fcis-29257	126	12	set	set	NOUN
fcis-29257	126	13	,	,	PUNCT
fcis-29257	126	14	and	and	CCONJ
fcis-29257	126	15	figure	figure	VERB
fcis-29257	126	16	6	6	NUM
fcis-29257	126	17	shows	show	VERB
fcis-29257	126	18	the	the	DET
fcis-29257	126	19	training	training	NOUN
fcis-29257	126	20	and	and	CCONJ
fcis-29257	126	21	testing	testing	NOUN
fcis-29257	126	22	performance	performance	NOUN
fcis-29257	126	23	of	of	ADP
fcis-29257	126	24	the	the	DET
fcis-29257	126	25	model	model	NOUN
fcis-29257	126	26	after	after	ADP
fcis-29257	126	27	modal	modal	ADJ
fcis-29257	126	28	fusion	fusion	NOUN
fcis-29257	126	29	.	.	PUNCT
fcis-29257	127	1	6	6	NUM
fcis-29257	127	2	.	.	X
fcis-29257	127	3	conclusion	conclusion	NOUN
fcis-29257	127	4	in	in	ADP
fcis-29257	127	5	this	this	DET
fcis-29257	127	6	paper	paper	NOUN
fcis-29257	127	7	,	,	PUNCT
fcis-29257	127	8	we	we	PRON
fcis-29257	127	9	propose	propose	VERB
fcis-29257	127	10	a	a	DET
fcis-29257	127	11	sentiment	sentiment	NOUN
fcis-29257	127	12	recognition	recognition	NOUN
fcis-29257	127	13	dataset	dataset	VERB
fcis-29257	127	14	for	for	ADP
fcis-29257	127	15	sichuan	sichuan	PROPN
fcis-29257	127	16	dialects	dialect	NOUN
fcis-29257	127	17	.	.	PUNCT
fcis-29257	128	1	compared	compare	VERB
fcis-29257	128	2	with	with	ADP
fcis-29257	128	3	the	the	DET
fcis-29257	128	4	existing	exist	VERB
fcis-29257	128	5	chinese	chinese	ADJ
fcis-29257	128	6	datasets	dataset	NOUN
fcis-29257	128	7	,	,	PUNCT
fcis-29257	128	8	the	the	DET
fcis-29257	128	9	proposed	propose	VERB
fcis-29257	128	10	datasets	dataset	NOUN
fcis-29257	128	11	are	be	AUX
fcis-29257	128	12	richer	rich	ADJ
fcis-29257	128	13	and	and	CCONJ
fcis-29257	128	14	more	more	ADV
fcis-29257	128	15	diverse	diverse	ADJ
fcis-29257	128	16	in	in	ADP
fcis-29257	128	17	terms	term	NOUN
fcis-29257	128	18	of	of	ADP
fcis-29257	128	19	content	content	NOUN
fcis-29257	128	20	,	,	PUNCT
fcis-29257	128	21	and	and	CCONJ
fcis-29257	128	22	these	these	DET
fcis-29257	128	23	samples	sample	NOUN
fcis-29257	128	24	are	be	AUX
fcis-29257	128	25	all	all	ADV
fcis-29257	128	26	from	from	ADP
fcis-29257	128	27	everyday	everyday	ADJ
fcis-29257	128	28	conversations	conversation	NOUN
fcis-29257	128	29	and	and	CCONJ
fcis-29257	128	30	are	be	AUX
fcis-29257	128	31	closer	close	ADJ
fcis-29257	128	32	to	to	ADP
fcis-29257	128	33	real	real	ADJ
fcis-29257	128	34	-	-	PUNCT
fcis-29257	128	35	life	life	NOUN
fcis-29257	128	36	scenarios	scenario	NOUN
fcis-29257	128	37	.	.	PUNCT
fcis-29257	129	1	in	in	ADP
fcis-29257	129	2	addition	addition	NOUN
fcis-29257	129	3	,	,	PUNCT
fcis-29257	129	4	this	this	DET
fcis-29257	129	5	paper	paper	NOUN
fcis-29257	129	6	proposes	propose	VERB
fcis-29257	129	7	a	a	DET
fcis-29257	129	8	multimodal	multimodal	ADJ
fcis-29257	129	9	emotion	emotion	NOUN
fcis-29257	129	10	recognition	recognition	NOUN
fcis-29257	129	11	model	model	NOUN
fcis-29257	129	12	,	,	PUNCT
fcis-29257	129	13	which	which	PRON
fcis-29257	129	14	uses	use	VERB
fcis-29257	129	15	dynamic	dynamic	ADJ
fcis-29257	129	16	convolution	convolution	NOUN
fcis-29257	129	17	and	and	CCONJ
fcis-29257	129	18	attention	attention	NOUN
fcis-29257	129	19	structure	structure	NOUN
fcis-29257	129	20	for	for	ADP
fcis-29257	129	21	multimodal	multimodal	ADJ
fcis-29257	129	22	fusion	fusion	NOUN
fcis-29257	129	23	.	.	PUNCT
fcis-29257	130	1	in	in	ADP
fcis-29257	130	2	the	the	DET
fcis-29257	130	3	future	future	NOUN
fcis-29257	130	4	,	,	PUNCT
fcis-29257	130	5	we	we	PRON
fcis-29257	130	6	will	will	AUX
fcis-29257	130	7	try	try	VERB
fcis-29257	130	8	to	to	PART
fcis-29257	130	9	improve	improve	VERB
fcis-29257	130	10	the	the	DET
fcis-29257	130	11	quality	quality	NOUN
fcis-29257	130	12	of	of	ADP
fcis-29257	130	13	the	the	DET
fcis-29257	130	14	model	model	NOUN
fcis-29257	130	15	and	and	CCONJ
fcis-29257	130	16	improve	improve	VERB
fcis-29257	130	17	the	the	DET
fcis-29257	130	18	accuracy	accuracy	NOUN
fcis-29257	130	19	,	,	PUNCT
fcis-29257	130	20	and	and	CCONJ
fcis-29257	130	21	we	we	PRON
fcis-29257	130	22	will	will	AUX
fcis-29257	130	23	also	also	ADV
fcis-29257	130	24	recognize	recognize	VERB
fcis-29257	130	25	audio	audio	ADJ
fcis-29257	130	26	fragments	fragment	NOUN
fcis-29257	130	27	without	without	ADP
fcis-29257	130	28	noise	noise	NOUN
fcis-29257	130	29	or	or	CCONJ
fcis-29257	130	30	distortion	distortion	NOUN
fcis-29257	130	31	,	,	PUNCT
fcis-29257	130	32	and	and	CCONJ
fcis-29257	130	33	identify	identify	VERB
fcis-29257	130	34	emotional	emotional	ADJ
fcis-29257	130	35	data	datum	NOUN
fcis-29257	130	36	without	without	ADP
fcis-29257	130	37	language	language	NOUN
fcis-29257	130	38	barriers	barrier	NOUN
fcis-29257	130	39	,	,	PUNCT
fcis-29257	130	40	and	and	CCONJ
fcis-29257	130	41	contribute	contribute	VERB
fcis-29257	130	42	to	to	ADP
fcis-29257	130	43	improving	improve	VERB
fcis-29257	130	44	sound	sound	ADJ
fcis-29257	130	45	quality	quality	NOUN
fcis-29257	130	46	.	.	PUNCT
fcis-29257	131	1	63	63	NUM
fcis-29257	131	2	figure	figure	NOUN
fcis-29257	131	3	5	5	NUM
fcis-29257	131	4	.	.	PUNCT
fcis-29257	131	5	training	training	NOUN
fcis-29257	131	6	loss	loss	NOUN
fcis-29257	131	7	of	of	ADP
fcis-29257	131	8	two	two	NUM
fcis-29257	131	9	modal	modal	NOUN
fcis-29257	131	10	models	model	NOUN
fcis-29257	131	11	figure	figure	VERB
fcis-29257	131	12	6	6	NUM
fcis-29257	131	13	.	.	PUNCT
fcis-29257	132	1	performance	performance	NOUN
fcis-29257	132	2	on	on	ADP
fcis-29257	132	3	test	test	NOUN
fcis-29257	132	4	set	set	VERB
fcis-29257	132	5	after	after	ADP
fcis-29257	132	6	modal	modal	ADJ
fcis-29257	132	7	fusion	fusion	NOUN
fcis-29257	132	8	references	reference	NOUN
fcis-29257	132	9	[	[	X
fcis-29257	132	10	1	1	NUM
fcis-29257	132	11	]	]	PUNCT
fcis-29257	132	12	xie	xie	PROPN
fcis-29257	132	13	jinhong	jinhong	PROPN
fcis-29257	132	14	,	,	PUNCT
fcis-29257	132	15	wei	wei	PROPN
fcis-29257	132	16	xia	xia	PROPN
fcis-29257	132	17	.	.	PUNCT
fcis-29257	133	1	sichuan	sichuan	PROPN
fcis-29257	133	2	dialect	dialect	PROPN
fcis-29257	133	3	speech	speech	NOUN
fcis-29257	133	4	recognition	recognition	NOUN
fcis-29257	133	5	based	base	VERB
fcis-29257	133	6	on	on	ADP
fcis-29257	133	7	rescnn	rescnn	NOUN
fcis-29257	133	8	-	-	NOUN
fcis-29257	133	9	bigru	bigru	NOUN
fcis-29257	133	10	[	[	X
fcis-29257	133	11	j	j	X
fcis-29257	133	12	]	]	X
fcis-29257	133	13	.	.	PUNCT
fcis-29257	134	1	modern	modern	ADJ
fcis-29257	134	2	electronic	electronic	ADJ
fcis-29257	134	3	technology	technology	NOUN
fcis-29257	134	4	,	,	PUNCT
fcis-29257	134	5	2024	2024	NUM
fcis-29257	134	6	,	,	PUNCT
fcis-29257	134	7	47	47	NUM
fcis-29257	134	8	(	(	PUNCT
fcis-29257	134	9	01	01	NUM
fcis-29257	134	10	):	):	PUNCT
fcis-29257	134	11	89	89	NUM
fcis-29257	134	12	-	-	SYM
fcis-29257	134	13	93	93	NUM
fcis-29257	134	14	.	.	PUNCT
fcis-29257	135	1	doi	doi	NOUN
fcis-29257	135	2	:	:	PUNCT
fcis-29257	135	3	10.16652	10.16652	NUM
fcis-29257	135	4	/	/	SYM
fcis-29257	135	5	j.issn.1004	j.issn.1004	NOUN
fcis-29257	135	6	-	-	PUNCT
fcis-29257	135	7	373x.2024	373x.2024	NUM
fcis-29257	135	8	.	.	NOUN
fcis-29257	135	9	01	01	NUM
fcis-29257	135	10	.	.	PUNCT
fcis-29257	135	11	016	016	NUM
fcis-29257	135	12	.	.	PUNCT
fcis-29257	136	1	[	[	X
fcis-29257	136	2	2	2	NUM
fcis-29257	136	3	]	]	X
fcis-29257	136	4	chen	chen	PROPN
fcis-29257	136	5	,	,	PUNCT
fcis-29257	136	6	y.	y.	PROPN
fcis-29257	136	7	,	,	PUNCT
fcis-29257	136	8	dai	dai	PROPN
fcis-29257	136	9	,	,	PUNCT
fcis-29257	136	10	x.	x.	PROPN
fcis-29257	136	11	,	,	PUNCT
fcis-29257	136	12	liu	liu	PROPN
fcis-29257	136	13	,	,	PUNCT
fcis-29257	136	14	m.	m.	NOUN
fcis-29257	136	15	,	,	PUNCT
fcis-29257	136	16	chen	chen	PROPN
fcis-29257	136	17	,	,	PUNCT
fcis-29257	136	18	d.	d.	PROPN
fcis-29257	136	19	,	,	PUNCT
fcis-29257	136	20	yuan	yuan	PROPN
fcis-29257	136	21	,	,	PUNCT
fcis-29257	136	22	l.	l.	PROPN
fcis-29257	136	23	,	,	PUNCT
fcis-29257	136	24	&	&	CCONJ
fcis-29257	136	25	liu	liu	PROPN
fcis-29257	136	26	,	,	PUNCT
fcis-29257	136	27	z.	z.	PROPN
fcis-29257	136	28	(	(	PUNCT
fcis-29257	136	29	2020	2020	NUM
fcis-29257	136	30	)	)	PUNCT
fcis-29257	136	31	.	.	PUNCT
fcis-29257	137	1	dynamic	dynamic	ADJ
fcis-29257	137	2	convolution	convolution	NOUN
fcis-29257	137	3	:	:	PUNCT
fcis-29257	137	4	attention	attention	NOUN
fcis-29257	137	5	over	over	ADP
fcis-29257	137	6	convolution	convolution	NOUN
fcis-29257	137	7	kernels	kernel	NOUN
fcis-29257	137	8	.	.	PUNCT
fcis-29257	138	1	in	in	ADP
fcis-29257	138	2	proceedings	proceeding	NOUN
fcis-29257	138	3	of	of	ADP
fcis-29257	138	4	the	the	DET
fcis-29257	138	5	ieee	ieee	NOUN
fcis-29257	138	6	/	/	SYM
fcis-29257	138	7	cvf	cvf	NOUN
fcis-29257	138	8	conference	conference	NOUN
fcis-29257	138	9	on	on	ADP
fcis-29257	138	10	computer	computer	NOUN
fcis-29257	138	11	vision	vision	NOUN
fcis-29257	138	12	and	and	CCONJ
fcis-29257	138	13	pattern	pattern	NOUN
fcis-29257	138	14	recognition	recognition	NOUN
fcis-29257	138	15	(	(	PUNCT
fcis-29257	138	16	pp	pp	ADJ
fcis-29257	138	17	.	.	PUNCT
fcis-29257	138	18	11030	11030	NUM
fcis-29257	138	19	11039	11039	NUM
fcis-29257	138	20	)	)	PUNCT
fcis-29257	138	21	.	.	PUNCT
fcis-29257	139	1	[	[	X
fcis-29257	139	2	3	3	NUM
fcis-29257	139	3	]	]	X
fcis-29257	139	4	jinghua	jinghua	PROPN
fcis-29257	139	5	tang	tang	PROPN
fcis-29257	139	6	,	,	PUNCT
fcis-29257	139	7	liyun	liyun	PROPN
fcis-29257	139	8	zhang	zhang	PROPN
fcis-29257	139	9	,	,	PUNCT
fcis-29257	139	10	yu	yu	PROPN
fcis-29257	139	11	lu	lu	AUX
fcis-29257	139	12	.	.	PUNCT
fcis-29257	139	13	vcemo	vcemo	NOUN
fcis-29257	139	14	:	:	PUNCT
fcis-29257	139	15	multi	multi	ADJ
fcis-29257	139	16	-	-	ADJ
fcis-29257	139	17	modal	modal	ADJ
fcis-29257	139	18	emotion	emotion	NOUN
fcis-29257	139	19	recognition	recognition	NOUN
fcis-29257	139	20	for	for	ADP
fcis-29257	139	21	chinese	chinese	ADJ
fcis-29257	139	22	voiceprints	voiceprint	NOUN
fcis-29257	139	23	.	.	PUNCT
fcis-29257	140	1	arxiv	arxiv	PROPN
fcis-29257	140	2	preprint	preprint	NOUN
fcis-29257	140	3	arxiv:2408.13019	arxiv:2408.13019	ADV
fcis-29257	140	4	(	(	PUNCT
fcis-29257	140	5	2024	2024	NUM
fcis-29257	140	6	)	)	PUNCT
fcis-29257	140	7	.	.	PUNCT
fcis-29257	141	1	[	[	X
fcis-29257	141	2	4	4	NUM
fcis-29257	141	3	]	]	X
fcis-29257	141	4	woo	woo	NOUN
fcis-29257	141	5	,	,	PUNCT
fcis-29257	141	6	s.	s.	PROPN
fcis-29257	141	7	,	,	PUNCT
fcis-29257	141	8	park	park	PROPN
fcis-29257	141	9	,	,	PUNCT
fcis-29257	141	10	j.	j.	PROPN
fcis-29257	141	11	,	,	PUNCT
fcis-29257	141	12	lee	lee	PROPN
fcis-29257	141	13	,	,	PUNCT
fcis-29257	141	14	j.	j.	PROPN
fcis-29257	141	15	y.	y.	PROPN
fcis-29257	141	16	,	,	PUNCT
fcis-29257	141	17	&	&	CCONJ
fcis-29257	141	18	kweon	kweon	PROPN
fcis-29257	141	19	,	,	PUNCT
fcis-29257	141	20	i.	i.	PROPN
fcis-29257	141	21	s.	s.	PROPN
fcis-29257	141	22	(	(	PUNCT
fcis-29257	141	23	2018	2018	NUM
fcis-29257	141	24	)	)	PUNCT
fcis-29257	141	25	.	.	PUNCT
fcis-29257	142	1	cbam	cbam	NOUN
fcis-29257	142	2	:	:	PUNCT
fcis-29257	142	3	convolutional	convolutional	ADJ
fcis-29257	142	4	block	block	NOUN
fcis-29257	142	5	attention	attention	NOUN
fcis-29257	142	6	module	module	NOUN
fcis-29257	142	7	.	.	PUNCT
fcis-29257	143	1	in	in	ADP
fcis-29257	143	2	proceedings	proceeding	NOUN
fcis-29257	143	3	of	of	ADP
fcis-29257	143	4	the	the	DET
fcis-29257	143	5	european	european	PROPN
fcis-29257	143	6	conference	conference	PROPN
fcis-29257	143	7	on	on	ADP
fcis-29257	143	8	computer	computer	NOUN
fcis-29257	143	9	vision	vision	NOUN
fcis-29257	143	10	(	(	PUNCT
fcis-29257	143	11	eccv	eccv	ADV
fcis-29257	143	12	)	)	PUNCT
fcis-29257	143	13	(	(	PUNCT
fcis-29257	143	14	pp	pp	X
fcis-29257	143	15	.	.	PUNCT
fcis-29257	144	1	3	3	NUM
fcis-29257	144	2	-	-	SYM
fcis-29257	144	3	19	19	NUM
fcis-29257	144	4	)	)	PUNCT
fcis-29257	144	5	.	.	PUNCT
fcis-29257	145	1	[	[	X
fcis-29257	145	2	5	5	NUM
fcis-29257	145	3	]	]	X
fcis-29257	145	4	quang	quang	PROPN
fcis-29257	145	5	-	-	PUNCT
fcis-29257	145	6	anh	anh	NOUN
fcis-29257	145	7	n.d	n.d	PROPN
fcis-29257	145	8	.	.	PROPN
fcis-29257	145	9	,	,	PUNCT
fcis-29257	145	10	manh	manh	PROPN
fcis-29257	145	11	-	-	PUNCT
fcis-29257	145	12	hung	hung	PROPN
fcis-29257	145	13	ha	ha	INTJ
fcis-29257	145	14	,	,	PUNCT
fcis-29257	145	15	thai	thai	PROPN
fcis-29257	145	16	kim	kim	PROPN
fcis-29257	145	17	dinh	dinh	PROPN
fcis-29257	145	18	.	.	PUNCT
fcis-29257	146	1	emotional	emotional	ADJ
fcis-29257	146	2	vietnamese	vietnamese	ADJ
fcis-29257	146	3	speech	speech	NOUN
fcis-29257	146	4	-	-	PUNCT
fcis-29257	146	5	based	base	VERB
fcis-29257	146	6	depressiondiagnosis	depressiondiagnosis	NOUN
fcis-29257	146	7	using	use	VERB
fcis-29257	146	8	dynamic	dynamic	ADJ
fcis-29257	146	9	attention	attention	NOUN
fcis-29257	146	10	mechanism	mechanism	NOUN
fcis-29257	146	11	.	.	PUNCT
fcis-29257	147	1	arxiv	arxiv	PROPN
fcis-29257	147	2	preprint	preprint	PROPN
fcis-29257	147	3	arxiv:2412	arxiv:2412	PROPN
fcis-29257	147	4	.	.	PROPN
fcis-29257	147	5	08683	08683	NUM
fcis-29257	147	6	(	(	PUNCT
fcis-29257	147	7	2024	2024	NUM
fcis-29257	147	8	)	)	PUNCT
fcis-29257	147	9	.	.	PUNCT
fcis-29257	148	1	[	[	X
fcis-29257	148	2	6	6	NUM
fcis-29257	148	3	]	]	SYM
fcis-29257	148	4	li	li	PROPN
fcis-29257	148	5	,	,	PUNCT
fcis-29257	148	6	y.	y.	PROPN
fcis-29257	148	7	,	,	PUNCT
fcis-29257	148	8	xin	xin	PROPN
fcis-29257	148	9	,	,	PUNCT
fcis-29257	148	10	y.	y.	PROPN
fcis-29257	148	11	,	,	PUNCT
fcis-29257	148	12	li	li	PROPN
fcis-29257	148	13	,	,	PUNCT
fcis-29257	148	14	x.	x.	PROPN
fcis-29257	148	15	,	,	PUNCT
fcis-29257	148	16	zhang	zhang	PROPN
fcis-29257	148	17	,	,	PUNCT
fcis-29257	148	18	y.	y.	PROPN
fcis-29257	148	19	,	,	PUNCT
fcis-29257	148	20	liu	liu	PROPN
fcis-29257	148	21	,	,	PUNCT
fcis-29257	148	22	c.	c.	PROPN
fcis-29257	148	23	,	,	PUNCT
fcis-29257	148	24	cao	cao	PROPN
fcis-29257	148	25	,	,	PUNCT
fcis-29257	148	26	z.	z.	PROPN
fcis-29257	148	27	,	,	PUNCT
fcis-29257	148	28	...	...	PUNCT
fcis-29257	148	29	&	&	CCONJ
fcis-29257	148	30	wang	wang	PROPN
fcis-29257	148	31	,	,	PUNCT
fcis-29257	148	32	l.	l.	PROPN
fcis-29257	148	33	(	(	PUNCT
fcis-29257	148	34	2024	2024	NUM
fcis-29257	148	35	)	)	PUNCT
fcis-29257	148	36	.	.	PUNCT
fcis-29257	149	1	omni	omni	ADJ
fcis-29257	149	2	-	-	PUNCT
fcis-29257	149	3	dimensional	dimensional	ADJ
fcis-29257	149	4	dynamic	dynamic	ADJ
fcis-29257	149	5	convolution	convolution	NOUN
fcis-29257	149	6	feature	feature	NOUN
fcis-29257	149	7	coordinate	coordinate	NOUN
fcis-29257	149	8	attention	attention	NOUN
fcis-29257	149	9	network	network	NOUN
fcis-29257	149	10	for	for	ADP
fcis-29257	149	11	pneumonia	pneumonia	NOUN
fcis-29257	149	12	classification	classification	NOUN
fcis-29257	149	13	.	.	PUNCT
fcis-29257	150	1	visual	visual	ADJ
fcis-29257	150	2	computing	computing	NOUN
fcis-29257	150	3	for	for	ADP
fcis-29257	150	4	industry	industry	NOUN
fcis-29257	150	5	,	,	PUNCT
fcis-29257	150	6	biomedicine	biomedicine	NOUN
fcis-29257	150	7	,	,	PUNCT
fcis-29257	150	8	and	and	CCONJ
fcis-29257	150	9	art	art	NOUN
fcis-29257	150	10	,	,	PUNCT
fcis-29257	150	11	7(1	7(1	NUM
fcis-29257	150	12	)	)	PUNCT
fcis-29257	150	13	,	,	PUNCT
fcis-29257	150	14	17	17	NUM
fcis-29257	150	15	.	.	PUNCT
fcis-29257	151	1	[	[	X
fcis-29257	151	2	7	7	NUM
fcis-29257	151	3	]	]	SYM
fcis-29257	151	4	li	li	PROPN
fcis-29257	151	5	,	,	PUNCT
fcis-29257	151	6	c.	c.	PROPN
fcis-29257	151	7	,	,	PUNCT
fcis-29257	151	8	zhou	zhou	PROPN
fcis-29257	151	9	,	,	PUNCT
fcis-29257	151	10	a.	a.	PROPN
fcis-29257	151	11	,	,	PUNCT
fcis-29257	151	12	&	&	CCONJ
fcis-29257	151	13	yao	yao	PROPN
fcis-29257	151	14	,	,	PUNCT
fcis-29257	151	15	a.	a.	NOUN
fcis-29257	151	16	omni	omni	ADJ
fcis-29257	151	17	-	-	PUNCT
fcis-29257	151	18	dimensional	dimensional	ADJ
fcis-29257	151	19	dynamic	dynamic	ADJ
fcis-29257	151	20	convolution	convolution	NOUN
fcis-29257	151	21	.	.	PUNCT
fcis-29257	152	1	arxiv	arxiv	PROPN
fcis-29257	152	2	preprint	preprint	VERB
fcis-29257	152	3	arxiv:2209.07947	arxiv:2209.07947	NUM
fcis-29257	152	4	(	(	PUNCT
fcis-29257	152	5	2022	2022	NUM
fcis-29257	152	6	)	)	PUNCT
fcis-29257	152	7	.	.	PUNCT
fcis-29257	153	1	[	[	X
fcis-29257	153	2	8	8	NUM
fcis-29257	153	3	]	]	SYM
fcis-29257	153	4	yang	yang	PROPN
fcis-29257	153	5	,	,	PUNCT
fcis-29257	153	6	b.	b.	PROPN
fcis-29257	153	7	,	,	PUNCT
fcis-29257	153	8	bender	bender	PROPN
fcis-29257	153	9	,	,	PUNCT
fcis-29257	153	10	g.	g.	PROPN
fcis-29257	153	11	,	,	PUNCT
fcis-29257	153	12	le	le	X
fcis-29257	153	13	,	,	PUNCT
fcis-29257	153	14	q.	q.	PROPN
fcis-29257	153	15	v.	v.	PROPN
fcis-29257	153	16	,	,	PUNCT
fcis-29257	153	17	&	&	CCONJ
fcis-29257	153	18	ngiam	ngiam	PROPN
fcis-29257	153	19	,	,	PUNCT
fcis-29257	153	20	j.	j.	PROPN
fcis-29257	153	21	(	(	PUNCT
fcis-29257	153	22	2019	2019	NUM
fcis-29257	153	23	)	)	PUNCT
fcis-29257	153	24	.	.	PUNCT
fcis-29257	154	1	condconv	condconv	NOUN
fcis-29257	154	2	:	:	PUNCT
fcis-29257	154	3	conditionally	conditionally	ADV
fcis-29257	154	4	parameterized	parameterized	ADJ
fcis-29257	154	5	convolutions	convolution	NOUN
fcis-29257	154	6	for	for	ADP
fcis-29257	154	7	efficient	efficient	ADJ
fcis-29257	154	8	inference	inference	NOUN
fcis-29257	154	9	.	.	PUNCT
fcis-29257	155	1	advances	advance	NOUN
fcis-29257	155	2	in	in	ADP
fcis-29257	155	3	neural	neural	ADJ
fcis-29257	155	4	information	information	NOUN
fcis-29257	155	5	processing	processing	NOUN
fcis-29257	155	6	systems	system	NOUN
fcis-29257	155	7	,	,	PUNCT
fcis-29257	155	8	32	32	NUM
fcis-29257	155	9	.	.	PUNCT
fcis-29257	156	1	[	[	X
fcis-29257	156	2	9	9	NUM
fcis-29257	156	3	]	]	SYM
fcis-29257	156	4	mao	mao	NOUN
fcis-29257	157	1	xueli	xueli	PROPN
fcis-29257	157	2	research	research	NOUN
fcis-29257	157	3	on	on	ADP
fcis-29257	157	4	language	language	NOUN
fcis-29257	157	5	recognition	recognition	NOUN
fcis-29257	157	6	methods	method	NOUN
fcis-29257	157	7	based	base	VERB
fcis-29257	157	8	on	on	ADP
fcis-29257	157	9	convolutional	convolutional	ADJ
fcis-29257	157	10	networks	network	NOUN
fcis-29257	157	11	and	and	CCONJ
fcis-29257	157	12	attention	attention	NOUN
fcis-29257	157	13	mechanisms	mechanism	NOUN
fcis-29257	157	14	[	[	X
fcis-29257	157	15	d	d	X
fcis-29257	157	16	]	]	X
fcis-29257	157	17	.	.	PUNCT
fcis-29257	158	1	xinjiang	xinjiang	PROPN
fcis-29257	158	2	university	university	PROPN
fcis-29257	158	3	,	,	PUNCT
fcis-29257	158	4	2021	2021	NUM
fcis-29257	158	5	.	.	PUNCT
fcis-29257	159	1	doi	doi	NOUN
fcis-29257	159	2	:	:	PUNCT
fcis-29257	159	3	10.27429	10.27429	NUM
fcis-29257	159	4	/	/	SYM
fcis-29257	159	5	d.cnki	d.cnki	NOUN
fcis-29257	159	6	.	.	PUNCT
fcis-29257	160	1	gxjdu	gxjdu	NOUN
fcis-29257	160	2	.	.	PUNCT
fcis-29257	160	3	2021	2021	NUM
fcis-29257	160	4	.	.	PUNCT
fcis-29257	161	1	000429	000429	NUM
fcis-29257	161	2	.	.	PUNCT
fcis-29257	162	1	[	[	X
fcis-29257	162	2	10	10	NUM
fcis-29257	162	3	]	]	X
fcis-29257	162	4	wang	wang	PROPN
fcis-29257	162	5	mingtian	mingtian	PROPN
fcis-29257	162	6	research	research	PROPN
fcis-29257	162	7	on	on	ADP
fcis-29257	162	8	speech	speech	NOUN
fcis-29257	162	9	emotion	emotion	NOUN
fcis-29257	162	10	recognition	recognition	NOUN
fcis-29257	162	11	based	base	VERB
fcis-29257	162	12	on	on	ADP
fcis-29257	162	13	text	text	NOUN
fcis-29257	162	14	and	and	CCONJ
fcis-29257	162	15	acoustic	acoustic	ADJ
fcis-29257	162	16	features	feature	NOUN
fcis-29257	162	17	[	[	X
fcis-29257	162	18	d	d	X
fcis-29257	162	19	]	]	X
fcis-29257	162	20	.	.	PUNCT
fcis-29257	163	1	shandong	shandong	PROPN
fcis-29257	163	2	university	university	PROPN
fcis-29257	163	3	,	,	PUNCT
fcis-29257	163	4	2022	2022	NUM
fcis-29257	163	5	.	.	PUNCT
fcis-29257	164	1	doi	doi	NOUN
fcis-29257	164	2	:	:	PUNCT
fcis-29257	164	3	10.27272	10.27272	NUM
fcis-29257	164	4	/	/	SYM
fcis-29257	164	5	d.cnki.gshdu.2022.001740	d.cnki.gshdu.2022.001740	NOUN
fcis-29257	164	6	.	.	PUNCT
fcis-29257	165	1	research	research	NOUN
fcis-29257	165	2	on	on	ADP
fcis-29257	165	3	language	language	NOUN
fcis-29257	165	4	recognition	recognition	NOUN
fcis-29257	165	5	methods	method	NOUN
fcis-29257	165	6	based	base	VERB
fcis-29257	165	7	on	on	ADP
fcis-29257	165	8	convolutional	convolutional	ADJ
fcis-29257	165	9	networks	network	NOUN
fcis-29257	165	10	and	and	CCONJ
fcis-29257	165	11	attention	attention	NOUN
fcis-29257	165	12	mechanisms	mechanism	NOUN
fcis-29257	165	13	.	.	PUNCT