id	sid	tid	token	lemma	pos
ajst-20193	1	1	academic	academic	ADJ
ajst-20193	1	2	journal	journal	NOUN
ajst-20193	1	3	of	of	ADP
ajst-20193	1	4	science	science	NOUN
ajst-20193	1	5	and	and	CCONJ
ajst-20193	1	6	technology	technology	NOUN
ajst-20193	1	7	issn	issn	NOUN
ajst-20193	1	8	:	:	PUNCT
ajst-20193	1	9	2771	2771	NUM
ajst-20193	1	10	-	-	SYM
ajst-20193	1	11	3032	3032	NUM
ajst-20193	1	12	|	|	NOUN
ajst-20193	1	13	vol	vol	NOUN
ajst-20193	1	14	.	.	PROPN
ajst-20193	2	1	10	10	NUM
ajst-20193	2	2	,	,	PUNCT
ajst-20193	2	3	no	no	INTJ
ajst-20193	2	4	.	.	NOUN
ajst-20193	2	5	2	2	NUM
ajst-20193	2	6	,	,	PUNCT
ajst-20193	2	7	2024	2024	NUM
ajst-20193	2	8	91	91	NUM
ajst-20193	2	9	anomaly	anomaly	NOUN
ajst-20193	2	10	detection	detection	NOUN
ajst-20193	2	11	for	for	ADP
ajst-20193	2	12	landslide	landslide	NOUN
ajst-20193	2	13	displacement	displacement	NOUN
ajst-20193	2	14	monitoring	monitoring	NOUN
ajst-20193	2	15	data	datum	NOUN
ajst-20193	2	16	based	base	VERB
ajst-20193	2	17	on	on	ADP
ajst-20193	2	18	tcn‐transformer	tcn‐transformer	PROPN
ajst-20193	2	19	hao	hao	PROPN
ajst-20193	2	20	jiang	jiang	PROPN
ajst-20193	2	21	,	,	PUNCT
ajst-20193	2	22	lin	lin	PROPN
ajst-20193	2	23	li	li	PROPN
ajst-20193	2	24	chengdu	chengdu	PROPN
ajst-20193	2	25	university	university	PROPN
ajst-20193	2	26	of	of	ADP
ajst-20193	2	27	technology	technology	PROPN
ajst-20193	2	28	,	,	PUNCT
ajst-20193	2	29	chengdu	chengdu	PROPN
ajst-20193	2	30	,	,	PUNCT
ajst-20193	2	31	sichuan	sichuan	PROPN
ajst-20193	2	32	,	,	PUNCT
ajst-20193	2	33	china	china	PROPN
ajst-20193	2	34	abstract	abstract	PROPN
ajst-20193	2	35	:	:	PUNCT
ajst-20193	2	36	in	in	ADP
ajst-20193	2	37	view	view	NOUN
ajst-20193	2	38	of	of	ADP
ajst-20193	2	39	the	the	DET
ajst-20193	2	40	high	high	ADJ
ajst-20193	2	41	false	false	ADJ
ajst-20193	2	42	alarm	alarm	NOUN
ajst-20193	2	43	rate	rate	NOUN
ajst-20193	2	44	of	of	ADP
ajst-20193	2	45	landslide	landslide	NOUN
ajst-20193	2	46	early	early	ADJ
ajst-20193	2	47	warning	warning	NOUN
ajst-20193	2	48	,	,	PUNCT
ajst-20193	2	49	this	this	DET
ajst-20193	2	50	paper	paper	NOUN
ajst-20193	2	51	proposes	propose	VERB
ajst-20193	2	52	a	a	DET
ajst-20193	2	53	classification	classification	NOUN
ajst-20193	2	54	method	method	NOUN
ajst-20193	2	55	of	of	ADP
ajst-20193	2	56	landslide	landslide	NOUN
ajst-20193	2	57	displacement	displacement	NOUN
ajst-20193	2	58	monitoring	monitoring	NOUN
ajst-20193	2	59	data	datum	NOUN
ajst-20193	2	60	based	base	VERB
ajst-20193	2	61	on	on	ADP
ajst-20193	2	62	tcn	tcn	NOUN
ajst-20193	2	63	and	and	CCONJ
ajst-20193	2	64	attention	attention	NOUN
ajst-20193	2	65	mechanism	mechanism	NOUN
ajst-20193	2	66	to	to	PART
ajst-20193	2	67	identify	identify	VERB
ajst-20193	2	68	normal	normal	ADJ
ajst-20193	2	69	data	datum	NOUN
ajst-20193	2	70	and	and	CCONJ
ajst-20193	2	71	abnormal	abnormal	ADJ
ajst-20193	2	72	data	datum	NOUN
ajst-20193	2	73	.	.	PUNCT
ajst-20193	3	1	first	first	ADV
ajst-20193	3	2	,	,	PUNCT
ajst-20193	3	3	the	the	DET
ajst-20193	3	4	original	original	ADJ
ajst-20193	3	5	monitoring	monitoring	NOUN
ajst-20193	3	6	data	datum	NOUN
ajst-20193	3	7	is	be	AUX
ajst-20193	3	8	processed	process	VERB
ajst-20193	3	9	and	and	CCONJ
ajst-20193	3	10	denoised	denoise	VERB
ajst-20193	3	11	to	to	PART
ajst-20193	3	12	improve	improve	VERB
ajst-20193	3	13	the	the	DET
ajst-20193	3	14	data	datum	NOUN
ajst-20193	3	15	quality	quality	NOUN
ajst-20193	3	16	.	.	PUNCT
ajst-20193	4	1	then	then	ADV
ajst-20193	4	2	,	,	PUNCT
ajst-20193	4	3	tcn	tcn	PROPN
ajst-20193	4	4	is	be	AUX
ajst-20193	4	5	used	use	VERB
ajst-20193	4	6	to	to	PART
ajst-20193	4	7	capture	capture	VERB
ajst-20193	4	8	the	the	DET
ajst-20193	4	9	long	long	ADJ
ajst-20193	4	10	-	-	PUNCT
ajst-20193	4	11	term	term	NOUN
ajst-20193	4	12	dependence	dependence	NOUN
ajst-20193	4	13	of	of	ADP
ajst-20193	4	14	time	time	NOUN
ajst-20193	4	15	series	series	PROPN
ajst-20193	4	16	data	data	PROPN
ajst-20193	4	17	and	and	CCONJ
ajst-20193	4	18	highlight	highlight	VERB
ajst-20193	4	19	key	key	ADJ
ajst-20193	4	20	features	feature	NOUN
ajst-20193	4	21	in	in	ADP
ajst-20193	4	22	combination	combination	NOUN
ajst-20193	4	23	with	with	ADP
ajst-20193	4	24	attention	attention	NOUN
ajst-20193	4	25	mechanism	mechanism	NOUN
ajst-20193	4	26	.	.	PUNCT
ajst-20193	5	1	finally	finally	ADV
ajst-20193	5	2	,	,	PUNCT
ajst-20193	5	3	the	the	DET
ajst-20193	5	4	landslide	landslide	NOUN
ajst-20193	5	5	displacement	displacement	NOUN
ajst-20193	5	6	monitoring	monitoring	NOUN
ajst-20193	5	7	data	datum	NOUN
ajst-20193	5	8	can	can	AUX
ajst-20193	5	9	be	be	AUX
ajst-20193	5	10	accurately	accurately	ADV
ajst-20193	5	11	classified	classify	VERB
ajst-20193	5	12	by	by	ADP
ajst-20193	5	13	building	build	VERB
ajst-20193	5	14	a	a	DET
ajst-20193	5	15	classification	classification	NOUN
ajst-20193	5	16	model	model	NOUN
ajst-20193	5	17	to	to	PART
ajst-20193	5	18	find	find	VERB
ajst-20193	5	19	out	out	ADP
ajst-20193	5	20	the	the	DET
ajst-20193	5	21	abnormal	abnormal	ADJ
ajst-20193	5	22	data	datum	NOUN
ajst-20193	5	23	and	and	CCONJ
ajst-20193	5	24	reduce	reduce	VERB
ajst-20193	5	25	the	the	DET
ajst-20193	5	26	false	false	ADJ
ajst-20193	5	27	alarm	alarm	NOUN
ajst-20193	5	28	rate	rate	NOUN
ajst-20193	5	29	of	of	ADP
ajst-20193	5	30	early	early	ADJ
ajst-20193	5	31	warning	warning	NOUN
ajst-20193	5	32	.	.	PUNCT
ajst-20193	6	1	experiments	experiment	NOUN
ajst-20193	6	2	show	show	VERB
ajst-20193	6	3	that	that	SCONJ
ajst-20193	6	4	this	this	DET
ajst-20193	6	5	method	method	NOUN
ajst-20193	6	6	can	can	AUX
ajst-20193	6	7	recognize	recognize	VERB
ajst-20193	6	8	abnormal	abnormal	ADJ
ajst-20193	6	9	displacement	displacement	NOUN
ajst-20193	6	10	monitoring	monitoring	NOUN
ajst-20193	6	11	data	datum	NOUN
ajst-20193	6	12	well	well	ADV
ajst-20193	6	13	while	while	SCONJ
ajst-20193	6	14	ensuring	ensure	VERB
ajst-20193	6	15	a	a	DET
ajst-20193	6	16	high	high	ADJ
ajst-20193	6	17	recall	recall	NOUN
ajst-20193	6	18	rate	rate	NOUN
ajst-20193	6	19	,	,	PUNCT
ajst-20193	6	20	and	and	CCONJ
ajst-20193	6	21	reduce	reduce	VERB
ajst-20193	6	22	the	the	DET
ajst-20193	6	23	false	false	ADJ
ajst-20193	6	24	alarm	alarm	NOUN
ajst-20193	6	25	rate	rate	NOUN
ajst-20193	6	26	of	of	ADP
ajst-20193	6	27	early	early	ADJ
ajst-20193	6	28	warning	warning	NOUN
ajst-20193	6	29	.	.	PUNCT
ajst-20193	7	1	keywords	keyword	NOUN
ajst-20193	7	2	:	:	PUNCT
ajst-20193	7	3	time	time	NOUN
ajst-20193	7	4	convolutional	convolutional	ADJ
ajst-20193	7	5	network	network	NOUN
ajst-20193	7	6	;	;	PUNCT
ajst-20193	7	7	attention	attention	NOUN
ajst-20193	7	8	mechanism	mechanism	NOUN
ajst-20193	7	9	;	;	PUNCT
ajst-20193	7	10	landslide	landslide	NOUN
ajst-20193	7	11	warning	warning	NOUN
ajst-20193	7	12	;	;	PUNCT
ajst-20193	7	13	anomaly	anomaly	NOUN
ajst-20193	7	14	detection	detection	NOUN
ajst-20193	7	15	.	.	PUNCT
ajst-20193	8	1	1	1	X
ajst-20193	8	2	.	.	X
ajst-20193	8	3	introduction	introduction	NOUN
ajst-20193	8	4	with	with	ADP
ajst-20193	8	5	the	the	DET
ajst-20193	8	6	global	global	ADJ
ajst-20193	8	7	climate	climate	NOUN
ajst-20193	8	8	change	change	NOUN
ajst-20193	8	9	and	and	CCONJ
ajst-20193	8	10	the	the	DET
ajst-20193	8	11	intensification	intensification	NOUN
ajst-20193	8	12	of	of	ADP
ajst-20193	8	13	human	human	ADJ
ajst-20193	8	14	activities	activity	NOUN
ajst-20193	8	15	,	,	PUNCT
ajst-20193	8	16	the	the	DET
ajst-20193	8	17	frequency	frequency	NOUN
ajst-20193	8	18	and	and	CCONJ
ajst-20193	8	19	scope	scope	NOUN
ajst-20193	8	20	of	of	ADP
ajst-20193	8	21	landslide	landslide	NOUN
ajst-20193	8	22	disasters	disaster	NOUN
ajst-20193	8	23	are	be	AUX
ajst-20193	8	24	on	on	ADP
ajst-20193	8	25	the	the	DET
ajst-20193	8	26	rise	rise	NOUN
ajst-20193	8	27	,	,	PUNCT
ajst-20193	8	28	posing	pose	VERB
ajst-20193	8	29	a	a	DET
ajst-20193	8	30	serious	serious	ADJ
ajst-20193	8	31	threat	threat	NOUN
ajst-20193	8	32	to	to	ADP
ajst-20193	8	33	the	the	DET
ajst-20193	8	34	safety	safety	NOUN
ajst-20193	8	35	of	of	ADP
ajst-20193	8	36	people	people	NOUN
ajst-20193	8	37	's	's	PART
ajst-20193	8	38	lives	life	NOUN
ajst-20193	8	39	and	and	CCONJ
ajst-20193	8	40	property	property	NOUN
ajst-20193	8	41	and	and	CCONJ
ajst-20193	8	42	the	the	DET
ajst-20193	8	43	ecological	ecological	ADJ
ajst-20193	8	44	environment	environment	NOUN
ajst-20193	8	45	.	.	PUNCT
ajst-20193	9	1	landslide	landslide	NOUN
ajst-20193	9	2	displacement	displacement	ADJ
ajst-20193	9	3	monitoring	monitoring	NOUN
ajst-20193	9	4	is	be	AUX
ajst-20193	9	5	an	an	DET
ajst-20193	9	6	important	important	ADJ
ajst-20193	9	7	means	mean	NOUN
ajst-20193	9	8	of	of	ADP
ajst-20193	9	9	landslide	landslide	NOUN
ajst-20193	9	10	disaster	disaster	NOUN
ajst-20193	9	11	early	early	ADJ
ajst-20193	9	12	warning	warning	NOUN
ajst-20193	9	13	and	and	CCONJ
ajst-20193	9	14	prevention	prevention	NOUN
ajst-20193	9	15	.	.	PUNCT
ajst-20193	10	1	through	through	ADP
ajst-20193	10	2	real	real	ADJ
ajst-20193	10	3	-	-	PUNCT
ajst-20193	10	4	time	time	NOUN
ajst-20193	10	5	monitoring	monitoring	NOUN
ajst-20193	10	6	and	and	CCONJ
ajst-20193	10	7	analysis	analysis	NOUN
ajst-20193	10	8	of	of	ADP
ajst-20193	10	9	landslide	landslide	NOUN
ajst-20193	10	10	displacement	displacement	NOUN
ajst-20193	10	11	,	,	PUNCT
ajst-20193	10	12	we	we	PRON
ajst-20193	10	13	can	can	AUX
ajst-20193	10	14	timely	timely	ADV
ajst-20193	10	15	grasp	grasp	VERB
ajst-20193	10	16	the	the	DET
ajst-20193	10	17	dynamic	dynamic	ADJ
ajst-20193	10	18	changes	change	NOUN
ajst-20193	10	19	of	of	ADP
ajst-20193	10	20	landslide	landslide	NOUN
ajst-20193	10	21	and	and	CCONJ
ajst-20193	10	22	provide	provide	VERB
ajst-20193	10	23	scientific	scientific	ADJ
ajst-20193	10	24	basis	basis	NOUN
ajst-20193	10	25	for	for	ADP
ajst-20193	10	26	landslide	landslide	NOUN
ajst-20193	10	27	disaster	disaster	NOUN
ajst-20193	10	28	early	early	ADJ
ajst-20193	10	29	warning[1]and	warning[1]and	NOUN
ajst-20193	10	30	emergency	emergency	NOUN
ajst-20193	10	31	response	response	NOUN
ajst-20193	10	32	.	.	PUNCT
ajst-20193	11	1	however	however	ADV
ajst-20193	11	2	,	,	PUNCT
ajst-20193	11	3	the	the	DET
ajst-20193	11	4	traditional	traditional	ADJ
ajst-20193	11	5	landslide	landslide	NOUN
ajst-20193	11	6	displacement	displacement	NOUN
ajst-20193	11	7	monitoring	monitoring	NOUN
ajst-20193	11	8	methods	method	NOUN
ajst-20193	11	9	often	often	ADV
ajst-20193	11	10	rely	rely	VERB
ajst-20193	11	11	on	on	ADP
ajst-20193	11	12	manual	manual	ADJ
ajst-20193	11	13	observation	observation	NOUN
ajst-20193	11	14	and	and	CCONJ
ajst-20193	11	15	empirical	empirical	ADJ
ajst-20193	11	16	judgment	judgment	NOUN
ajst-20193	11	17	,	,	PUNCT
ajst-20193	11	18	and	and	CCONJ
ajst-20193	11	19	there	there	PRON
ajst-20193	11	20	are	be	VERB
ajst-20193	11	21	problems	problem	NOUN
ajst-20193	11	22	such	such	ADJ
ajst-20193	11	23	as	as	ADP
ajst-20193	11	24	low	low	ADJ
ajst-20193	11	25	monitoring	monitoring	NOUN
ajst-20193	11	26	efficiency	efficiency	NOUN
ajst-20193	11	27	,	,	PUNCT
ajst-20193	11	28	poor	poor	ADJ
ajst-20193	11	29	data	datum	NOUN
ajst-20193	11	30	accuracy	accuracy	NOUN
ajst-20193	11	31	[	[	X
ajst-20193	11	32	2]and	2]and	ADJ
ajst-20193	11	33	poor	poor	ADJ
ajst-20193	11	34	realtime	realtime	NOUN
ajst-20193	11	35	performance	performance	NOUN
ajst-20193	11	36	.	.	PUNCT
ajst-20193	12	1	therefore	therefore	ADV
ajst-20193	12	2	,	,	PUNCT
ajst-20193	12	3	the	the	DET
ajst-20193	12	4	research	research	NOUN
ajst-20193	12	5	and	and	CCONJ
ajst-20193	12	6	development	development	NOUN
ajst-20193	12	7	of	of	ADP
ajst-20193	12	8	new	new	ADJ
ajst-20193	12	9	landslide	landslide	NOUN
ajst-20193	12	10	displacement	displacement	NOUN
ajst-20193	12	11	monitoring	monitoring	NOUN
ajst-20193	12	12	methods	method	NOUN
ajst-20193	12	13	have	have	VERB
ajst-20193	12	14	important	important	ADJ
ajst-20193	12	15	practical	practical	ADJ
ajst-20193	12	16	significance	significance	NOUN
ajst-20193	12	17	and	and	CCONJ
ajst-20193	12	18	application	application	NOUN
ajst-20193	12	19	value	value	NOUN
ajst-20193	12	20	.	.	PUNCT
ajst-20193	13	1	in	in	ADP
ajst-20193	13	2	recent	recent	ADJ
ajst-20193	13	3	years	year	NOUN
ajst-20193	13	4	,	,	PUNCT
ajst-20193	13	5	with	with	ADP
ajst-20193	13	6	the	the	DET
ajst-20193	13	7	rapid	rapid	ADJ
ajst-20193	13	8	development	development	NOUN
ajst-20193	13	9	of	of	ADP
ajst-20193	13	10	artificial	artificial	ADJ
ajst-20193	13	11	intelligence	intelligence	NOUN
ajst-20193	13	12	and	and	CCONJ
ajst-20193	13	13	deep	deep	ADJ
ajst-20193	13	14	learning	learning	NOUN
ajst-20193	13	15	technology	technology	NOUN
ajst-20193	13	16	,	,	PUNCT
ajst-20193	13	17	time	time	NOUN
ajst-20193	13	18	series	series	PROPN
ajst-20193	13	19	analysis	analysis	NOUN
ajst-20193	13	20	method	method	NOUN
ajst-20193	13	21	has	have	AUX
ajst-20193	13	22	been	be	AUX
ajst-20193	13	23	widely	widely	ADV
ajst-20193	13	24	used	use	VERB
ajst-20193	13	25	in	in	ADP
ajst-20193	13	26	the	the	DET
ajst-20193	13	27	classification	classification	NOUN
ajst-20193	13	28	and	and	CCONJ
ajst-20193	13	29	prediction	prediction	NOUN
ajst-20193	13	30	of	of	ADP
ajst-20193	13	31	landslide	landslide	NOUN
ajst-20193	13	32	displacement	displacement	NOUN
ajst-20193	13	33	monitoring	monitoring	NOUN
ajst-20193	13	34	data	datum	NOUN
ajst-20193	13	35	.	.	PUNCT
ajst-20193	14	1	among	among	ADP
ajst-20193	14	2	them	they	PRON
ajst-20193	14	3	,	,	PUNCT
ajst-20193	14	4	time	time	NOUN
ajst-20193	14	5	convolution	convolution	NOUN
ajst-20193	14	6	network	network	NOUN
ajst-20193	14	7	(	(	PUNCT
ajst-20193	14	8	tcn	tcn	PROPN
ajst-20193	14	9	)	)	PUNCT
ajst-20193	14	10	,	,	PUNCT
ajst-20193	14	11	as	as	ADP
ajst-20193	14	12	a	a	DET
ajst-20193	14	13	deep	deep	ADJ
ajst-20193	14	14	learning	learning	NOUN
ajst-20193	14	15	method	method	NOUN
ajst-20193	14	16	specially	specially	ADV
ajst-20193	14	17	used	use	VERB
ajst-20193	14	18	to	to	PART
ajst-20193	14	19	process	process	VERB
ajst-20193	14	20	time	time	NOUN
ajst-20193	14	21	series	series	PROPN
ajst-20193	14	22	data[3	data[3	PROPN
ajst-20193	14	23	]	]	PUNCT
ajst-20193	14	24	,	,	PUNCT
ajst-20193	14	25	has	have	VERB
ajst-20193	14	26	strong	strong	ADJ
ajst-20193	14	27	temporal	temporal	ADJ
ajst-20193	14	28	feature	feature	NOUN
ajst-20193	14	29	extraction	extraction	NOUN
ajst-20193	14	30	and	and	CCONJ
ajst-20193	14	31	classification	classification	NOUN
ajst-20193	14	32	capabilities	capability	NOUN
ajst-20193	14	33	.	.	PUNCT
ajst-20193	15	1	however	however	ADV
ajst-20193	15	2	,	,	PUNCT
ajst-20193	15	3	traditional	traditional	ADJ
ajst-20193	15	4	tcn	tcn	NOUN
ajst-20193	15	5	still	still	ADV
ajst-20193	15	6	has	have	VERB
ajst-20193	15	7	some	some	DET
ajst-20193	15	8	limitations[4	limitations[4	NOUN
ajst-20193	15	9	]	]	X
ajst-20193	15	10	when	when	SCONJ
ajst-20193	15	11	dealing	deal	VERB
ajst-20193	15	12	with	with	ADP
ajst-20193	15	13	complex	complex	ADJ
ajst-20193	15	14	time	time	NOUN
ajst-20193	15	15	series	series	NOUN
ajst-20193	15	16	data.in	data.in	PART
ajst-20193	15	17	order	order	VERB
ajst-20193	15	18	to	to	PART
ajst-20193	15	19	improve	improve	VERB
ajst-20193	15	20	the	the	DET
ajst-20193	15	21	performance	performance	NOUN
ajst-20193	15	22	of	of	ADP
ajst-20193	15	23	tcn	tcn	NOUN
ajst-20193	15	24	in	in	ADP
ajst-20193	15	25	the	the	DET
ajst-20193	15	26	classification	classification	NOUN
ajst-20193	15	27	and	and	CCONJ
ajst-20193	15	28	early	early	ADJ
ajst-20193	15	29	warning	warning	NOUN
ajst-20193	15	30	of	of	ADP
ajst-20193	15	31	landslide	landslide	NOUN
ajst-20193	15	32	displacement	displacement	NOUN
ajst-20193	15	33	monitoring	monitoring	NOUN
ajst-20193	15	34	data	datum	NOUN
ajst-20193	15	35	,	,	PUNCT
ajst-20193	15	36	this	this	DET
ajst-20193	15	37	study	study	NOUN
ajst-20193	15	38	introduces	introduce	VERB
ajst-20193	15	39	the	the	DET
ajst-20193	15	40	tcn	tcn	NOUN
ajst-20193	15	41	attention	attention	NOUN
ajst-20193	15	42	model[5	model[5	PROPN
ajst-20193	15	43	]	]	PUNCT
ajst-20193	15	44	based	base	VERB
ajst-20193	15	45	on	on	ADP
ajst-20193	15	46	attention	attention	NOUN
ajst-20193	15	47	mechanism	mechanism	NOUN
ajst-20193	15	48	.	.	PUNCT
ajst-20193	16	1	tcn	tcn	NOUN
ajst-20193	16	2	attention	attention	NOUN
ajst-20193	16	3	model	model	NOUN
ajst-20193	16	4	combines	combine	VERB
ajst-20193	16	5	the	the	DET
ajst-20193	16	6	advantages	advantage	NOUN
ajst-20193	16	7	of	of	ADP
ajst-20193	16	8	convolutional	convolutional	ADJ
ajst-20193	16	9	neural	neural	ADJ
ajst-20193	16	10	network	network	NOUN
ajst-20193	16	11	(	(	PUNCT
ajst-20193	16	12	cnn	cnn	PROPN
ajst-20193	16	13	)	)	PUNCT
ajst-20193	16	14	and	and	CCONJ
ajst-20193	16	15	attention	attention	NOUN
ajst-20193	16	16	mechanism	mechanism	NOUN
ajst-20193	16	17	,	,	PUNCT
ajst-20193	16	18	enhances	enhance	VERB
ajst-20193	16	19	the	the	DET
ajst-20193	16	20	extraction	extraction	NOUN
ajst-20193	16	21	of	of	ADP
ajst-20193	16	22	key	key	ADJ
ajst-20193	16	23	time	time	NOUN
ajst-20193	16	24	features	feature	NOUN
ajst-20193	16	25	and	and	CCONJ
ajst-20193	16	26	importance	importance	NOUN
ajst-20193	16	27	weighting	weight	VERB
ajst-20193	16	28	through	through	ADP
ajst-20193	16	29	attention	attention	NOUN
ajst-20193	16	30	mechanism	mechanism	NOUN
ajst-20193	16	31	,	,	PUNCT
ajst-20193	16	32	thus	thus	ADV
ajst-20193	16	33	improving	improve	VERB
ajst-20193	16	34	the	the	DET
ajst-20193	16	35	classification	classification	NOUN
ajst-20193	16	36	and	and	CCONJ
ajst-20193	16	37	prediction	prediction	NOUN
ajst-20193	16	38	ability	ability	NOUN
ajst-20193	16	39	of	of	ADP
ajst-20193	16	40	the	the	DET
ajst-20193	16	41	model	model	NOUN
ajst-20193	16	42	for	for	ADP
ajst-20193	16	43	landslide	landslide	NOUN
ajst-20193	16	44	displacement	displacement	ADJ
ajst-20193	16	45	data	datum	NOUN
ajst-20193	16	46	.	.	PUNCT
ajst-20193	17	1	the	the	DET
ajst-20193	17	2	purpose	purpose	NOUN
ajst-20193	17	3	of	of	ADP
ajst-20193	17	4	this	this	DET
ajst-20193	17	5	study	study	NOUN
ajst-20193	17	6	is	be	AUX
ajst-20193	17	7	to	to	PART
ajst-20193	17	8	explore	explore	VERB
ajst-20193	17	9	the	the	DET
ajst-20193	17	10	method	method	NOUN
ajst-20193	17	11	of	of	ADP
ajst-20193	17	12	classification	classification	NOUN
ajst-20193	17	13	and	and	CCONJ
ajst-20193	17	14	early	early	ADJ
ajst-20193	17	15	warning	warning	NOUN
ajst-20193	17	16	of	of	ADP
ajst-20193	17	17	landslide	landslide	NOUN
ajst-20193	17	18	displacement	displacement	NOUN
ajst-20193	17	19	monitoring	monitoring	NOUN
ajst-20193	17	20	data	datum	NOUN
ajst-20193	17	21	using	use	VERB
ajst-20193	17	22	tcn	tcn	PROPN
ajst-20193	17	23	attention	attention	NOUN
ajst-20193	17	24	model	model	NOUN
ajst-20193	17	25	,	,	PUNCT
ajst-20193	17	26	and	and	CCONJ
ajst-20193	17	27	to	to	PART
ajst-20193	17	28	verify	verify	VERB
ajst-20193	17	29	its	its	PRON
ajst-20193	17	30	effectiveness	effectiveness	NOUN
ajst-20193	17	31	and	and	CCONJ
ajst-20193	17	32	feasibility	feasibility	NOUN
ajst-20193	17	33	through	through	ADP
ajst-20193	17	34	experiments	experiment	NOUN
ajst-20193	17	35	.	.	PUNCT
ajst-20193	18	1	by	by	ADP
ajst-20193	18	2	introducing	introduce	VERB
ajst-20193	18	3	deep	deep	ADJ
ajst-20193	18	4	learning	learning	NOUN
ajst-20193	18	5	technology	technology	NOUN
ajst-20193	18	6	and	and	CCONJ
ajst-20193	18	7	attention	attention	NOUN
ajst-20193	18	8	mechanism	mechanism	NOUN
ajst-20193	18	9	,	,	PUNCT
ajst-20193	18	10	the	the	DET
ajst-20193	18	11	processing	processing	NOUN
ajst-20193	18	12	efficiency	efficiency	NOUN
ajst-20193	18	13	and	and	CCONJ
ajst-20193	18	14	classification	classification	NOUN
ajst-20193	18	15	accuracy	accuracy	NOUN
ajst-20193	18	16	of	of	ADP
ajst-20193	18	17	landslide	landslide	NOUN
ajst-20193	18	18	displacement	displacement	NOUN
ajst-20193	18	19	monitoring	monitoring	NOUN
ajst-20193	18	20	data	datum	NOUN
ajst-20193	18	21	can	can	AUX
ajst-20193	18	22	be	be	AUX
ajst-20193	18	23	improved	improve	VERB
ajst-20193	18	24	,	,	PUNCT
ajst-20193	18	25	providing	provide	VERB
ajst-20193	18	26	a	a	DET
ajst-20193	18	27	more	more	ADV
ajst-20193	18	28	scientific	scientific	ADJ
ajst-20193	18	29	and	and	CCONJ
ajst-20193	18	30	effective	effective	ADJ
ajst-20193	18	31	means	mean	NOUN
ajst-20193	18	32	for	for	ADP
ajst-20193	18	33	landslide	landslide	NOUN
ajst-20193	18	34	disaster	disaster	NOUN
ajst-20193	18	35	early	early	ADJ
ajst-20193	18	36	warning	warning	NOUN
ajst-20193	18	37	and	and	CCONJ
ajst-20193	18	38	prevention	prevention	NOUN
ajst-20193	18	39	.	.	PUNCT
ajst-20193	19	1	2	2	X
ajst-20193	19	2	.	.	X
ajst-20193	19	3	relevant	relevant	ADJ
ajst-20193	19	4	theoretical	theoretical	ADJ
ajst-20193	19	5	work	work	NOUN
ajst-20193	19	6	2.1	2.1	NUM
ajst-20193	19	7	.	.	PUNCT
ajst-20193	19	8	landslide	landslide	NOUN
ajst-20193	19	9	monitoring	monitoring	NOUN
ajst-20193	19	10	technology	technology	NOUN
ajst-20193	19	11	the	the	DET
ajst-20193	19	12	development	development	NOUN
ajst-20193	19	13	of	of	ADP
ajst-20193	19	14	landslide	landslide	NOUN
ajst-20193	19	15	monitoring	monitoring	NOUN
ajst-20193	19	16	technology	technology	NOUN
ajst-20193	19	17	can	can	AUX
ajst-20193	19	18	be	be	AUX
ajst-20193	19	19	traced	trace	VERB
ajst-20193	19	20	back	back	ADV
ajst-20193	19	21	to	to	ADP
ajst-20193	19	22	early	early	ADJ
ajst-20193	19	23	manual	manual	ADJ
ajst-20193	19	24	inspection	inspection	NOUN
ajst-20193	19	25	and	and	CCONJ
ajst-20193	19	26	simple	simple	ADJ
ajst-20193	19	27	physical	physical	ADJ
ajst-20193	19	28	sensor	sensor	NOUN
ajst-20193	19	29	application	application	NOUN
ajst-20193	19	30	.	.	PUNCT
ajst-20193	20	1	at	at	ADP
ajst-20193	20	2	this	this	DET
ajst-20193	20	3	stage	stage	NOUN
ajst-20193	20	4	,	,	PUNCT
ajst-20193	20	5	landslide	landslide	NOUN
ajst-20193	20	6	monitoring	monitoring	NOUN
ajst-20193	20	7	relies	rely	VERB
ajst-20193	20	8	on	on	ADP
ajst-20193	20	9	manual	manual	ADJ
ajst-20193	20	10	observation	observation	NOUN
ajst-20193	20	11	and	and	CCONJ
ajst-20193	20	12	ground	ground	NOUN
ajst-20193	20	13	survey	survey	NOUN
ajst-20193	20	14	,	,	PUNCT
ajst-20193	20	15	which	which	PRON
ajst-20193	20	16	is	be	AUX
ajst-20193	20	17	not	not	PART
ajst-20193	20	18	only	only	ADV
ajst-20193	20	19	inefficient	inefficient	ADJ
ajst-20193	20	20	,	,	PUNCT
ajst-20193	20	21	but	but	CCONJ
ajst-20193	20	22	also	also	ADV
ajst-20193	20	23	limited	limit	VERB
ajst-20193	20	24	by	by	ADP
ajst-20193	20	25	the	the	DET
ajst-20193	20	26	experience	experience	NOUN
ajst-20193	20	27	of	of	ADP
ajst-20193	20	28	observers	observer	NOUN
ajst-20193	20	29	and	and	CCONJ
ajst-20193	20	30	geographical	geographical	ADJ
ajst-20193	20	31	conditions	condition	NOUN
ajst-20193	20	32	,	,	PUNCT
ajst-20193	20	33	so	so	SCONJ
ajst-20193	20	34	it	it	PRON
ajst-20193	20	35	is	be	AUX
ajst-20193	20	36	difficult	difficult	ADJ
ajst-20193	20	37	to	to	PART
ajst-20193	20	38	achieve	achieve	VERB
ajst-20193	20	39	comprehensive	comprehensive	ADJ
ajst-20193	20	40	and	and	CCONJ
ajst-20193	20	41	continuous	continuous	ADJ
ajst-20193	20	42	monitoring	monitoring	NOUN
ajst-20193	20	43	.	.	PUNCT
ajst-20193	21	1	subsequently	subsequently	ADV
ajst-20193	21	2	,	,	PUNCT
ajst-20193	21	3	with	with	ADP
ajst-20193	21	4	the	the	DET
ajst-20193	21	5	rapid	rapid	ADJ
ajst-20193	21	6	development	development	NOUN
ajst-20193	21	7	of	of	ADP
ajst-20193	21	8	sensor	sensor	NOUN
ajst-20193	21	9	technology	technology	NOUN
ajst-20193	21	10	,	,	PUNCT
ajst-20193	21	11	landslide	landslide	NOUN
ajst-20193	21	12	monitoring	monitoring	NOUN
ajst-20193	21	13	systems	system	NOUN
ajst-20193	21	14	based	base	VERB
ajst-20193	21	15	on	on	ADP
ajst-20193	21	16	geological	geological	ADJ
ajst-20193	21	17	radar	radar	NOUN
ajst-20193	21	18	,	,	PUNCT
ajst-20193	21	19	gps	gps	PROPN
ajst-20193	21	20	,	,	PUNCT
ajst-20193	21	21	inclinometer	inclinometer	NOUN
ajst-20193	21	22	and	and	CCONJ
ajst-20193	21	23	other	other	ADJ
ajst-20193	21	24	physical	physical	ADJ
ajst-20193	21	25	sensors	sensor	NOUN
ajst-20193	21	26	gradually	gradually	ADV
ajst-20193	21	27	emerge[6	emerge[6	VERB
ajst-20193	21	28	]	]	PUNCT
ajst-20193	21	29	.	.	PUNCT
ajst-20193	22	1	these	these	DET
ajst-20193	22	2	advanced	advanced	ADJ
ajst-20193	22	3	monitoring	monitoring	NOUN
ajst-20193	22	4	systems	system	NOUN
ajst-20193	22	5	can	can	AUX
ajst-20193	22	6	obtain	obtain	VERB
ajst-20193	22	7	real	real	ADJ
ajst-20193	22	8	-	-	PUNCT
ajst-20193	22	9	time	time	NOUN
ajst-20193	22	10	landslide	landslide	NOUN
ajst-20193	22	11	displacement	displacement	NOUN
ajst-20193	22	12	data	datum	NOUN
ajst-20193	22	13	,	,	PUNCT
ajst-20193	22	14	providing	provide	VERB
ajst-20193	22	15	important	important	ADJ
ajst-20193	22	16	support	support	NOUN
ajst-20193	22	17	for	for	ADP
ajst-20193	22	18	landslide	landslide	NOUN
ajst-20193	22	19	early	early	ADJ
ajst-20193	22	20	warning	warning	NOUN
ajst-20193	22	21	and	and	CCONJ
ajst-20193	22	22	emergency	emergency	NOUN
ajst-20193	22	23	response	response	NOUN
ajst-20193	22	24	.	.	PUNCT
ajst-20193	23	1	however	however	ADV
ajst-20193	23	2	,	,	PUNCT
ajst-20193	23	3	although	although	SCONJ
ajst-20193	23	4	these	these	DET
ajst-20193	23	5	systems	system	NOUN
ajst-20193	23	6	have	have	AUX
ajst-20193	23	7	made	make	VERB
ajst-20193	23	8	significant	significant	ADJ
ajst-20193	23	9	breakthroughs	breakthrough	NOUN
ajst-20193	23	10	in	in	ADP
ajst-20193	23	11	technology	technology	NOUN
ajst-20193	23	12	,	,	PUNCT
ajst-20193	23	13	they	they	PRON
ajst-20193	23	14	still	still	ADV
ajst-20193	23	15	face	face	VERB
ajst-20193	23	16	many	many	ADJ
ajst-20193	23	17	challenges	challenge	NOUN
ajst-20193	23	18	in	in	ADP
ajst-20193	23	19	practical	practical	ADJ
ajst-20193	23	20	applications	application	NOUN
ajst-20193	23	21	.	.	PUNCT
ajst-20193	24	1	high	high	ADJ
ajst-20193	24	2	equipment	equipment	NOUN
ajst-20193	24	3	cost	cost	NOUN
ajst-20193	24	4	,	,	PUNCT
ajst-20193	24	5	complex	complex	ADJ
ajst-20193	24	6	installation	installation	NOUN
ajst-20193	24	7	and	and	CCONJ
ajst-20193	24	8	maintenance	maintenance	NOUN
ajst-20193	24	9	process	process	NOUN
ajst-20193	24	10	and	and	CCONJ
ajst-20193	24	11	sensitivity	sensitivity	NOUN
ajst-20193	24	12	to	to	ADP
ajst-20193	24	13	environmental	environmental	ADJ
ajst-20193	24	14	conditions	condition	NOUN
ajst-20193	24	15	limit	limit	VERB
ajst-20193	24	16	its	its	PRON
ajst-20193	24	17	wide	wide	ADJ
ajst-20193	24	18	application	application	NOUN
ajst-20193	24	19	.	.	PUNCT
ajst-20193	25	1	however	however	ADV
ajst-20193	25	2	,	,	PUNCT
ajst-20193	25	3	the	the	DET
ajst-20193	25	4	landslide	landslide	NOUN
ajst-20193	25	5	monitoring	monitor	VERB
ajst-20193	25	6	data	datum	NOUN
ajst-20193	25	7	may	may	AUX
ajst-20193	25	8	be	be	AUX
ajst-20193	25	9	affected	affect	VERB
ajst-20193	25	10	by	by	ADP
ajst-20193	25	11	many	many	ADJ
ajst-20193	25	12	factors	factor	NOUN
ajst-20193	25	13	,	,	PUNCT
ajst-20193	25	14	such	such	ADJ
ajst-20193	25	15	as	as	ADP
ajst-20193	25	16	equipment	equipment	NOUN
ajst-20193	25	17	error	error	NOUN
ajst-20193	25	18	,	,	PUNCT
ajst-20193	25	19	environmental	environmental	ADJ
ajst-20193	25	20	interference	interference	NOUN
ajst-20193	25	21	,	,	PUNCT
ajst-20193	25	22	etc	etc	X
ajst-20193	25	23	.	.	X
ajst-20193	25	24	,	,	PUNCT
ajst-20193	25	25	resulting	result	VERB
ajst-20193	25	26	in	in	ADP
ajst-20193	25	27	missing	missing	ADJ
ajst-20193	25	28	or	or	CCONJ
ajst-20193	25	29	inaccurate	inaccurate	ADJ
ajst-20193	25	30	data	datum	NOUN
ajst-20193	25	31	,	,	PUNCT
ajst-20193	25	32	which	which	PRON
ajst-20193	25	33	ultimately	ultimately	ADV
ajst-20193	25	34	leads	lead	VERB
ajst-20193	25	35	to	to	ADP
ajst-20193	25	36	a	a	DET
ajst-20193	25	37	high	high	ADJ
ajst-20193	25	38	false	false	ADJ
ajst-20193	25	39	alarm	alarm	NOUN
ajst-20193	25	40	rate	rate	NOUN
ajst-20193	25	41	of	of	ADP
ajst-20193	25	42	the	the	DET
ajst-20193	25	43	actual	actual	ADJ
ajst-20193	25	44	landslide	landslide	NOUN
ajst-20193	25	45	early	early	ADJ
ajst-20193	25	46	warning	warning	NOUN
ajst-20193	25	47	work	work	NOUN
ajst-20193	25	48	.	.	PUNCT
ajst-20193	26	1	2.2	2.2	NUM
ajst-20193	26	2	.	.	PUNCT
ajst-20193	27	1	data	datum	NOUN
ajst-20193	27	2	anomaly	anomaly	NOUN
ajst-20193	27	3	detection	detection	NOUN
ajst-20193	27	4	method	method	NOUN
ajst-20193	27	5	after	after	ADP
ajst-20193	27	6	years	year	NOUN
ajst-20193	27	7	of	of	ADP
ajst-20193	27	8	development	development	NOUN
ajst-20193	27	9	,	,	PUNCT
ajst-20193	27	10	data	datum	NOUN
ajst-20193	27	11	anomaly	anomaly	NOUN
ajst-20193	27	12	detection	detection	NOUN
ajst-20193	27	13	technology	technology	NOUN
ajst-20193	27	14	has	have	AUX
ajst-20193	27	15	gradually	gradually	ADV
ajst-20193	27	16	evolved	evolve	VERB
ajst-20193	27	17	from	from	ADP
ajst-20193	27	18	early	early	ADJ
ajst-20193	27	19	statistical	statistical	ADJ
ajst-20193	27	20	based	base	VERB
ajst-20193	27	21	methods	method	NOUN
ajst-20193	27	22	to	to	ADP
ajst-20193	27	23	complex	complex	ADJ
ajst-20193	27	24	models	model	NOUN
ajst-20193	27	25	based	base	VERB
ajst-20193	27	26	on	on	ADP
ajst-20193	27	27	machine	machine	NOUN
ajst-20193	27	28	learning[7	learning[7	PROPN
ajst-20193	27	29	]	]	PUNCT
ajst-20193	27	30	and	and	CCONJ
ajst-20193	27	31	deep	deep	ADJ
ajst-20193	27	32	learning	learning	NOUN
ajst-20193	27	33	.	.	PUNCT
ajst-20193	28	1	early	early	ADJ
ajst-20193	28	2	anomaly	anomaly	NOUN
ajst-20193	28	3	detection	detection	NOUN
ajst-20193	28	4	methods	method	NOUN
ajst-20193	28	5	mainly	mainly	ADV
ajst-20193	28	6	rely	rely	VERB
ajst-20193	28	7	on	on	ADP
ajst-20193	28	8	simple	simple	ADJ
ajst-20193	28	9	statistical	statistical	ADJ
ajst-20193	28	10	models	model	NOUN
ajst-20193	28	11	,	,	PUNCT
ajst-20193	28	12	such	such	ADJ
ajst-20193	28	13	as	as	ADP
ajst-20193	28	14	methods	method	NOUN
ajst-20193	28	15	based	base	VERB
ajst-20193	28	16	on	on	ADP
ajst-20193	28	17	standard	standard	ADJ
ajst-20193	28	18	deviation	deviation	NOUN
ajst-20193	28	19	and	and	CCONJ
ajst-20193	28	20	mean	mean	VERB
ajst-20193	28	21	,	,	PUNCT
ajst-20193	28	22	which	which	PRON
ajst-20193	28	23	are	be	AUX
ajst-20193	28	24	inefficient	inefficient	ADJ
ajst-20193	28	25	in	in	ADP
ajst-20193	28	26	dealing	deal	VERB
ajst-20193	28	27	with	with	ADP
ajst-20193	28	28	large	large	ADJ
ajst-20193	28	29	-	-	PUNCT
ajst-20193	28	30	scale	scale	NOUN
ajst-20193	28	31	data	datum	NOUN
ajst-20193	28	32	sets	set	NOUN
ajst-20193	28	33	and	and	CCONJ
ajst-20193	28	34	insensitive	insensitive	ADJ
ajst-20193	28	35	to	to	ADP
ajst-20193	28	36	complex	complex	ADJ
ajst-20193	28	37	data	datum	NOUN
ajst-20193	28	38	distribution	distribution	NOUN
ajst-20193	28	39	.	.	PUNCT
ajst-20193	29	1	with	with	ADP
ajst-20193	29	2	the	the	DET
ajst-20193	29	3	rise	rise	NOUN
ajst-20193	29	4	of	of	ADP
ajst-20193	29	5	machine	machine	NOUN
ajst-20193	29	6	learning	learn	VERB
ajst-20193	29	7	technology	technology	NOUN
ajst-20193	29	8	,	,	PUNCT
ajst-20193	29	9	clustering	cluster	VERB
ajst-20193	29	10	analysis	analysis	NOUN
ajst-20193	29	11	,	,	PUNCT
ajst-20193	29	12	classification	classification	NOUN
ajst-20193	29	13	algorithms	algorithm	NOUN
ajst-20193	29	14	,	,	PUNCT
ajst-20193	29	15	association	association	NOUN
ajst-20193	29	16	92	92	NUM
ajst-20193	29	17	rules	rule	NOUN
ajst-20193	29	18	and	and	CCONJ
ajst-20193	29	19	other	other	ADJ
ajst-20193	29	20	technologies	technology	NOUN
ajst-20193	29	21	have	have	AUX
ajst-20193	29	22	been	be	AUX
ajst-20193	29	23	introduced	introduce	VERB
ajst-20193	29	24	into	into	ADP
ajst-20193	29	25	the	the	DET
ajst-20193	29	26	anomaly	anomaly	NOUN
ajst-20193	29	27	detection	detection	NOUN
ajst-20193	29	28	field	field	NOUN
ajst-20193	29	29	.	.	PUNCT
ajst-20193	30	1	these	these	DET
ajst-20193	30	2	methods	method	NOUN
ajst-20193	30	3	can	can	AUX
ajst-20193	30	4	better	well	ADV
ajst-20193	30	5	handle	handle	VERB
ajst-20193	30	6	nonlinear	nonlinear	ADJ
ajst-20193	30	7	relationships	relationship	NOUN
ajst-20193	30	8	and	and	CCONJ
ajst-20193	30	9	complex	complex	ADJ
ajst-20193	30	10	patterns[8	patterns[8	PROPN
ajst-20193	30	11	]	]	PUNCT
ajst-20193	30	12	,	,	PUNCT
ajst-20193	30	13	and	and	CCONJ
ajst-20193	30	14	improve	improve	VERB
ajst-20193	30	15	the	the	DET
ajst-20193	30	16	accuracy	accuracy	NOUN
ajst-20193	30	17	of	of	ADP
ajst-20193	30	18	anomaly	anomaly	NOUN
ajst-20193	30	19	detection	detection	NOUN
ajst-20193	30	20	.	.	PUNCT
ajst-20193	31	1	in	in	ADP
ajst-20193	31	2	recent	recent	ADJ
ajst-20193	31	3	years	year	NOUN
ajst-20193	31	4	,	,	PUNCT
ajst-20193	31	5	the	the	DET
ajst-20193	31	6	rapid	rapid	ADJ
ajst-20193	31	7	development	development	NOUN
ajst-20193	31	8	of	of	ADP
ajst-20193	31	9	deep	deep	ADJ
ajst-20193	31	10	learning	learning	NOUN
ajst-20193	31	11	technology	technology	NOUN
ajst-20193	31	12	has	have	AUX
ajst-20193	31	13	brought	bring	VERB
ajst-20193	31	14	a	a	DET
ajst-20193	31	15	new	new	ADJ
ajst-20193	31	16	breakthrough	breakthrough	NOUN
ajst-20193	31	17	for	for	ADP
ajst-20193	31	18	data	datum	NOUN
ajst-20193	31	19	anomaly	anomaly	NOUN
ajst-20193	31	20	detection	detection	NOUN
ajst-20193	31	21	.	.	PUNCT
ajst-20193	32	1	anomaly	anomaly	NOUN
ajst-20193	32	2	detection	detection	NOUN
ajst-20193	32	3	methods	method	NOUN
ajst-20193	32	4	based	base	VERB
ajst-20193	32	5	on	on	ADP
ajst-20193	32	6	deep	deep	ADJ
ajst-20193	32	7	learning	learning	NOUN
ajst-20193	32	8	models	model	NOUN
ajst-20193	32	9	such	such	ADJ
ajst-20193	32	10	as	as	ADP
ajst-20193	32	11	self	self	NOUN
ajst-20193	32	12	encoders	encoder	NOUN
ajst-20193	32	13	,	,	PUNCT
ajst-20193	32	14	generated	generate	VERB
ajst-20193	32	15	countermeasures	countermeasure	NOUN
ajst-20193	32	16	networks	network	NOUN
ajst-20193	32	17	and	and	CCONJ
ajst-20193	32	18	variational	variational	ADJ
ajst-20193	32	19	self	self	NOUN
ajst-20193	32	20	encoders	encoder	NOUN
ajst-20193	32	21	show	show	VERB
ajst-20193	32	22	excellent	excellent	ADJ
ajst-20193	32	23	performance	performance	NOUN
ajst-20193	32	24	in	in	ADP
ajst-20193	32	25	processing	process	VERB
ajst-20193	32	26	highdimensional	highdimensional	ADJ
ajst-20193	32	27	data	datum	NOUN
ajst-20193	32	28	and	and	CCONJ
ajst-20193	32	29	complex	complex	ADJ
ajst-20193	32	30	patterns[9	patterns[9	NOUN
ajst-20193	32	31	]	]	PUNCT
ajst-20193	32	32	.	.	PUNCT
ajst-20193	33	1	these	these	DET
ajst-20193	33	2	models	model	NOUN
ajst-20193	33	3	can	can	AUX
ajst-20193	33	4	learn	learn	VERB
ajst-20193	33	5	the	the	DET
ajst-20193	33	6	internal	internal	ADJ
ajst-20193	33	7	feature	feature	NOUN
ajst-20193	33	8	representation	representation	NOUN
ajst-20193	33	9	of	of	ADP
ajst-20193	33	10	data	datum	NOUN
ajst-20193	33	11	from	from	ADP
ajst-20193	33	12	a	a	DET
ajst-20193	33	13	large	large	ADJ
ajst-20193	33	14	number	number	NOUN
ajst-20193	33	15	of	of	ADP
ajst-20193	33	16	normal	normal	ADJ
ajst-20193	33	17	data	datum	NOUN
ajst-20193	33	18	,	,	PUNCT
ajst-20193	33	19	thus	thus	ADV
ajst-20193	33	20	effectively	effectively	ADV
ajst-20193	33	21	identifying	identify	VERB
ajst-20193	33	22	outliers	outlier	NOUN
ajst-20193	33	23	.	.	PUNCT
ajst-20193	34	1	as	as	SCONJ
ajst-20193	34	2	the	the	DET
ajst-20193	34	3	landslide	landslide	NOUN
ajst-20193	34	4	displacement	displacement	NOUN
ajst-20193	34	5	monitoring	monitoring	NOUN
ajst-20193	34	6	data	datum	NOUN
ajst-20193	34	7	is	be	AUX
ajst-20193	34	8	time	time	NOUN
ajst-20193	34	9	series	series	PROPN
ajst-20193	34	10	data	data	PROPN
ajst-20193	34	11	,	,	PUNCT
ajst-20193	34	12	this	this	DET
ajst-20193	34	13	paper	paper	NOUN
ajst-20193	34	14	considers	consider	VERB
ajst-20193	34	15	the	the	DET
ajst-20193	34	16	use	use	NOUN
ajst-20193	34	17	of	of	ADP
ajst-20193	34	18	deep	deep	ADJ
ajst-20193	34	19	learning	learning	NOUN
ajst-20193	34	20	algorithm	algorithm	NOUN
ajst-20193	34	21	to	to	PART
ajst-20193	34	22	process	process	NOUN
ajst-20193	34	23	time	time	NOUN
ajst-20193	34	24	series	series	NOUN
ajst-20193	34	25	tasks	task	NOUN
ajst-20193	34	26	for	for	ADP
ajst-20193	34	27	anomaly	anomaly	NOUN
ajst-20193	34	28	detection[10	detection[10	NUM
ajst-20193	34	29	]	]	PUNCT
ajst-20193	34	30	,	,	PUNCT
ajst-20193	34	31	and	and	CCONJ
ajst-20193	34	32	considering	consider	VERB
ajst-20193	34	33	the	the	DET
ajst-20193	34	34	real	real	ADJ
ajst-20193	34	35	-	-	PUNCT
ajst-20193	34	36	time	time	NOUN
ajst-20193	34	37	requirements	requirement	NOUN
ajst-20193	34	38	of	of	ADP
ajst-20193	34	39	landslide	landslide	NOUN
ajst-20193	34	40	monitoring	monitoring	NOUN
ajst-20193	34	41	and	and	CCONJ
ajst-20193	34	42	early	early	ADJ
ajst-20193	34	43	warning	warning	NOUN
ajst-20193	34	44	work	work	NOUN
ajst-20193	34	45	,	,	PUNCT
ajst-20193	34	46	it	it	PRON
ajst-20193	34	47	decides	decide	VERB
ajst-20193	34	48	to	to	PART
ajst-20193	34	49	use	use	VERB
ajst-20193	34	50	time	time	NOUN
ajst-20193	34	51	convolution	convolution	NOUN
ajst-20193	34	52	network	network	NOUN
ajst-20193	34	53	to	to	PART
ajst-20193	34	54	identify	identify	VERB
ajst-20193	34	55	abnormal	abnormal	ADJ
ajst-20193	34	56	data	datum	NOUN
ajst-20193	34	57	.	.	PUNCT
ajst-20193	35	1	2.3	2.3	NUM
ajst-20193	35	2	.	.	PUNCT
ajst-20193	35	3	application	application	NOUN
ajst-20193	35	4	of	of	ADP
ajst-20193	35	5	tcn	tcn	NOUN
ajst-20193	35	6	and	and	CCONJ
ajst-20193	35	7	attention	attention	NOUN
ajst-20193	35	8	mechanism	mechanism	NOUN
ajst-20193	35	9	time	time	NOUN
ajst-20193	35	10	convolution	convolution	NOUN
ajst-20193	35	11	network	network	NOUN
ajst-20193	35	12	(	(	PUNCT
ajst-20193	35	13	tcn	tcn	PROPN
ajst-20193	35	14	)	)	PUNCT
ajst-20193	35	15	is	be	AUX
ajst-20193	35	16	a	a	DET
ajst-20193	35	17	neural	neural	ADJ
ajst-20193	35	18	network	network	NOUN
ajst-20193	35	19	architecture	architecture	NOUN
ajst-20193	35	20	designed	design	VERB
ajst-20193	35	21	specifically	specifically	ADV
ajst-20193	35	22	for	for	ADP
ajst-20193	35	23	serial	serial	ADJ
ajst-20193	35	24	data	datum	NOUN
ajst-20193	35	25	.	.	PUNCT
ajst-20193	36	1	it	it	PRON
ajst-20193	36	2	can	can	AUX
ajst-20193	36	3	effectively	effectively	ADV
ajst-20193	36	4	process	process	VERB
ajst-20193	36	5	long	long	ADJ
ajst-20193	36	6	series	series	NOUN
ajst-20193	36	7	data	datum	NOUN
ajst-20193	36	8	and	and	CCONJ
ajst-20193	36	9	capture	capture	VERB
ajst-20193	36	10	long	long	ADJ
ajst-20193	36	11	-	-	PUNCT
ajst-20193	36	12	term	term	NOUN
ajst-20193	36	13	dependencies	dependency	NOUN
ajst-20193	36	14	in	in	ADP
ajst-20193	36	15	time	time	NOUN
ajst-20193	36	16	series	series	NOUN
ajst-20193	36	17	.	.	PUNCT
ajst-20193	37	1	tcn	tcn	PROPN
ajst-20193	37	2	has	have	AUX
ajst-20193	37	3	achieved	achieve	VERB
ajst-20193	37	4	remarkable	remarkable	ADJ
ajst-20193	37	5	success	success	NOUN
ajst-20193	37	6	in	in	ADP
ajst-20193	37	7	natural	natural	ADJ
ajst-20193	37	8	language	language	NOUN
ajst-20193	37	9	processing	processing	NOUN
ajst-20193	37	10	,	,	PUNCT
ajst-20193	37	11	speech	speech	NOUN
ajst-20193	37	12	recognition	recognition	NOUN
ajst-20193	37	13	and	and	CCONJ
ajst-20193	37	14	time	time	NOUN
ajst-20193	37	15	series	series	PROPN
ajst-20193	37	16	prediction	prediction	PROPN
ajst-20193	37	17	.	.	PUNCT
ajst-20193	38	1	for	for	ADP
ajst-20193	38	2	example	example	NOUN
ajst-20193	38	3	,	,	PUNCT
ajst-20193	38	4	google	google	PROPN
ajst-20193	38	5	's	's	PART
ajst-20193	38	6	wavenet	wavenet	ADJ
ajst-20193	38	7	model	model	NOUN
ajst-20193	38	8	uses	use	VERB
ajst-20193	38	9	tcn	tcn	NOUN
ajst-20193	38	10	to	to	PART
ajst-20193	38	11	generate	generate	VERB
ajst-20193	38	12	natural	natural	ADJ
ajst-20193	38	13	speech	speech	NOUN
ajst-20193	38	14	waveforms	waveform	NOUN
ajst-20193	38	15	.	.	PUNCT
ajst-20193	39	1	the	the	DET
ajst-20193	39	2	attention	attention	NOUN
ajst-20193	39	3	mechanism	mechanism	NOUN
ajst-20193	39	4	allows	allow	VERB
ajst-20193	39	5	the	the	DET
ajst-20193	39	6	model	model	NOUN
ajst-20193	39	7	to	to	PART
ajst-20193	39	8	focus	focus	VERB
ajst-20193	39	9	on	on	ADP
ajst-20193	39	10	the	the	DET
ajst-20193	39	11	key	key	ADJ
ajst-20193	39	12	information	information	NOUN
ajst-20193	39	13	in	in	ADP
ajst-20193	39	14	the	the	DET
ajst-20193	39	15	input	input	NOUN
ajst-20193	39	16	data	datum	NOUN
ajst-20193	39	17	,	,	PUNCT
ajst-20193	39	18	thus	thus	ADV
ajst-20193	39	19	improving	improve	VERB
ajst-20193	39	20	the	the	DET
ajst-20193	39	21	interpretation	interpretation	NOUN
ajst-20193	39	22	ability	ability	NOUN
ajst-20193	39	23	and	and	CCONJ
ajst-20193	39	24	performance	performance	NOUN
ajst-20193	39	25	of	of	ADP
ajst-20193	39	26	the	the	DET
ajst-20193	39	27	model	model	NOUN
ajst-20193	39	28	.	.	PUNCT
ajst-20193	40	1	for	for	ADP
ajst-20193	40	2	example	example	NOUN
ajst-20193	40	3	,	,	PUNCT
ajst-20193	40	4	openai	openai	PROPN
ajst-20193	40	5	's	's	PART
ajst-20193	40	6	gpt	gpt	NOUN
ajst-20193	40	7	series	series	NOUN
ajst-20193	40	8	models	model	NOUN
ajst-20193	40	9	use	use	VERB
ajst-20193	40	10	the	the	DET
ajst-20193	40	11	attention	attention	NOUN
ajst-20193	40	12	mechanism	mechanism	NOUN
ajst-20193	40	13	to	to	PART
ajst-20193	40	14	generate	generate	VERB
ajst-20193	40	15	coherent	coherent	ADJ
ajst-20193	40	16	text	text	NOUN
ajst-20193	40	17	.	.	PUNCT
ajst-20193	41	1	in	in	ADP
ajst-20193	41	2	the	the	DET
ajst-20193	41	3	abnormal	abnormal	ADJ
ajst-20193	41	4	detection	detection	NOUN
ajst-20193	41	5	task	task	NOUN
ajst-20193	41	6	of	of	ADP
ajst-20193	41	7	landslide	landslide	NOUN
ajst-20193	41	8	monitoring	monitoring	NOUN
ajst-20193	41	9	data	datum	NOUN
ajst-20193	41	10	,	,	PUNCT
ajst-20193	41	11	attention	attention	NOUN
ajst-20193	41	12	mechanism	mechanism	NOUN
ajst-20193	41	13	can	can	AUX
ajst-20193	41	14	fully	fully	ADV
ajst-20193	41	15	learn	learn	VERB
ajst-20193	41	16	the	the	DET
ajst-20193	41	17	overall	overall	ADJ
ajst-20193	41	18	trend	trend	NOUN
ajst-20193	41	19	of	of	ADP
ajst-20193	41	20	data	datum	NOUN
ajst-20193	41	21	,	,	PUNCT
ajst-20193	41	22	thus	thus	ADV
ajst-20193	41	23	improving	improve	VERB
ajst-20193	41	24	the	the	DET
ajst-20193	41	25	accuracy	accuracy	NOUN
ajst-20193	41	26	of	of	ADP
ajst-20193	41	27	abnormal	abnormal	ADJ
ajst-20193	41	28	monitoring	monitoring	NOUN
ajst-20193	41	29	and	and	CCONJ
ajst-20193	41	30	reducing	reduce	VERB
ajst-20193	41	31	the	the	DET
ajst-20193	41	32	rate	rate	NOUN
ajst-20193	41	33	of	of	ADP
ajst-20193	41	34	early	early	ADJ
ajst-20193	41	35	warning	warn	VERB
ajst-20193	41	36	false	false	ADJ
ajst-20193	41	37	alarm	alarm	NOUN
ajst-20193	41	38	.	.	PUNCT
ajst-20193	42	1	3	3	X
ajst-20193	42	2	.	.	X
ajst-20193	42	3	construction	construction	NOUN
ajst-20193	42	4	of	of	ADP
ajst-20193	42	5	anomaly	anomaly	NOUN
ajst-20193	42	6	detection	detection	NOUN
ajst-20193	42	7	algorithm	algorithm	NOUN
ajst-20193	42	8	3.1	3.1	NUM
ajst-20193	42	9	.	.	PUNCT
ajst-20193	43	1	tcn	tcn	PROPN
ajst-20193	43	2	principle	principle	ADJ
ajst-20193	43	3	time	time	NOUN
ajst-20193	43	4	convolution	convolution	NOUN
ajst-20193	43	5	network	network	NOUN
ajst-20193	43	6	is	be	AUX
ajst-20193	43	7	a	a	DET
ajst-20193	43	8	kind	kind	NOUN
ajst-20193	43	9	of	of	ADP
ajst-20193	43	10	deep	deep	ADJ
ajst-20193	43	11	learning	learning	NOUN
ajst-20193	43	12	model	model	NOUN
ajst-20193	43	13	specially	specially	ADV
ajst-20193	43	14	designed	design	VERB
ajst-20193	43	15	for	for	ADP
ajst-20193	43	16	processing	processing	NOUN
ajst-20193	43	17	time	time	NOUN
ajst-20193	43	18	series	series	PROPN
ajst-20193	43	19	data	data	PROPN
ajst-20193	43	20	.	.	PUNCT
ajst-20193	44	1	it	it	PRON
ajst-20193	44	2	ensures	ensure	VERB
ajst-20193	44	3	that	that	SCONJ
ajst-20193	44	4	the	the	DET
ajst-20193	44	5	output	output	NOUN
ajst-20193	44	6	only	only	ADV
ajst-20193	44	7	depends	depend	VERB
ajst-20193	44	8	on	on	ADP
ajst-20193	44	9	the	the	DET
ajst-20193	44	10	current	current	ADJ
ajst-20193	44	11	and	and	CCONJ
ajst-20193	44	12	past	past	ADJ
ajst-20193	44	13	inputs	input	NOUN
ajst-20193	44	14	through	through	ADP
ajst-20193	44	15	causal	causal	ADJ
ajst-20193	44	16	convolution	convolution	NOUN
ajst-20193	44	17	,	,	PUNCT
ajst-20193	44	18	thus	thus	ADV
ajst-20193	44	19	maintaining	maintain	VERB
ajst-20193	44	20	the	the	DET
ajst-20193	44	21	causal	causal	ADJ
ajst-20193	44	22	relationship	relationship	NOUN
ajst-20193	44	23	of	of	ADP
ajst-20193	44	24	time	time	NOUN
ajst-20193	44	25	series	series	NOUN
ajst-20193	44	26	.	.	PUNCT
ajst-20193	45	1	at	at	ADP
ajst-20193	45	2	the	the	DET
ajst-20193	45	3	same	same	ADJ
ajst-20193	45	4	time	time	NOUN
ajst-20193	45	5	,	,	PUNCT
ajst-20193	45	6	it	it	PRON
ajst-20193	45	7	uses	use	VERB
ajst-20193	45	8	expansion	expansion	NOUN
ajst-20193	45	9	convolution	convolution	NOUN
ajst-20193	45	10	to	to	PART
ajst-20193	45	11	expand	expand	VERB
ajst-20193	45	12	the	the	DET
ajst-20193	45	13	receptive	receptive	ADJ
ajst-20193	45	14	field	field	NOUN
ajst-20193	45	15	,	,	PUNCT
ajst-20193	45	16	so	so	SCONJ
ajst-20193	45	17	that	that	SCONJ
ajst-20193	45	18	the	the	DET
ajst-20193	45	19	network	network	NOUN
ajst-20193	45	20	can	can	AUX
ajst-20193	45	21	capture	capture	VERB
ajst-20193	45	22	a	a	DET
ajst-20193	45	23	longer	long	ADJ
ajst-20193	45	24	time	time	NOUN
ajst-20193	45	25	dependency	dependency	NOUN
ajst-20193	45	26	without	without	ADP
ajst-20193	45	27	adding	add	VERB
ajst-20193	45	28	additional	additional	ADJ
ajst-20193	45	29	parameters	parameter	NOUN
ajst-20193	45	30	,	,	PUNCT
ajst-20193	45	31	solving	solve	VERB
ajst-20193	45	32	the	the	DET
ajst-20193	45	33	problem	problem	NOUN
ajst-20193	45	34	of	of	ADP
ajst-20193	45	35	gradient	gradient	ADJ
ajst-20193	45	36	disappearance	disappearance	NOUN
ajst-20193	45	37	or	or	CCONJ
ajst-20193	45	38	explosion	explosion	NOUN
ajst-20193	45	39	encountered	encounter	VERB
ajst-20193	45	40	by	by	ADP
ajst-20193	45	41	traditional	traditional	ADJ
ajst-20193	45	42	recurrent	recurrent	ADJ
ajst-20193	45	43	neural	neural	ADJ
ajst-20193	45	44	networks	network	NOUN
ajst-20193	45	45	when	when	SCONJ
ajst-20193	45	46	processing	process	VERB
ajst-20193	45	47	long	long	ADJ
ajst-20193	45	48	series	series	NOUN
ajst-20193	45	49	.	.	PUNCT
ajst-20193	46	1	tcn	tcn	PROPN
ajst-20193	46	2	also	also	ADV
ajst-20193	46	3	combines	combine	VERB
ajst-20193	46	4	residual	residual	ADJ
ajst-20193	46	5	connection	connection	NOUN
ajst-20193	46	6	and	and	CCONJ
ajst-20193	46	7	batch	batch	VERB
ajst-20193	46	8	normalization	normalization	NOUN
ajst-20193	46	9	to	to	PART
ajst-20193	46	10	improve	improve	VERB
ajst-20193	46	11	the	the	DET
ajst-20193	46	12	stability	stability	NOUN
ajst-20193	46	13	and	and	CCONJ
ajst-20193	46	14	efficiency	efficiency	NOUN
ajst-20193	46	15	of	of	ADP
ajst-20193	46	16	training	training	NOUN
ajst-20193	46	17	.	.	PUNCT
ajst-20193	47	1	this	this	DET
ajst-20193	47	2	structure	structure	NOUN
ajst-20193	47	3	makes	make	VERB
ajst-20193	47	4	it	it	PRON
ajst-20193	47	5	perform	perform	VERB
ajst-20193	47	6	well	well	ADV
ajst-20193	47	7	in	in	ADP
ajst-20193	47	8	processing	process	VERB
ajst-20193	47	9	the	the	DET
ajst-20193	47	10	classification	classification	NOUN
ajst-20193	47	11	,	,	PUNCT
ajst-20193	47	12	prediction	prediction	NOUN
ajst-20193	47	13	and	and	CCONJ
ajst-20193	47	14	regression	regression	NOUN
ajst-20193	47	15	tasks	task	NOUN
ajst-20193	47	16	of	of	ADP
ajst-20193	47	17	time	time	NOUN
ajst-20193	47	18	series	series	PROPN
ajst-20193	47	19	data	data	PROPN
ajst-20193	47	20	,	,	PUNCT
ajst-20193	47	21	especially	especially	ADV
ajst-20193	47	22	in	in	ADP
ajst-20193	47	23	the	the	DET
ajst-20193	47	24	scenarios	scenario	NOUN
ajst-20193	47	25	that	that	PRON
ajst-20193	47	26	need	need	VERB
ajst-20193	47	27	to	to	PART
ajst-20193	47	28	process	process	VERB
ajst-20193	47	29	long	long	ADJ
ajst-20193	47	30	series	series	NOUN
ajst-20193	47	31	and	and	CCONJ
ajst-20193	47	32	real	real	ADJ
ajst-20193	47	33	-	-	PUNCT
ajst-20193	47	34	time	time	NOUN
ajst-20193	47	35	prediction	prediction	NOUN
ajst-20193	47	36	.	.	PUNCT
ajst-20193	48	1	tcn	tcn	PROPN
ajst-20193	48	2	is	be	AUX
ajst-20193	48	3	widely	widely	ADV
ajst-20193	48	4	used	use	VERB
ajst-20193	48	5	.	.	PUNCT
ajst-20193	49	1	tcn	tcn	PROPN
ajst-20193	49	2	has	have	VERB
ajst-20193	49	3	an	an	DET
ajst-20193	49	4	extended	extended	ADJ
ajst-20193	49	5	causal	causal	ADJ
ajst-20193	49	6	convolution	convolution	NOUN
ajst-20193	49	7	structure	structure	NOUN
ajst-20193	49	8	,	,	PUNCT
ajst-20193	49	9	which	which	PRON
ajst-20193	49	10	can	can	AUX
ajst-20193	49	11	expand	expand	VERB
ajst-20193	49	12	the	the	DET
ajst-20193	49	13	sampling	sampling	NOUN
ajst-20193	49	14	of	of	ADP
ajst-20193	49	15	the	the	DET
ajst-20193	49	16	input	input	NOUN
ajst-20193	49	17	data	datum	NOUN
ajst-20193	49	18	of	of	ADP
ajst-20193	49	19	the	the	DET
ajst-20193	49	20	upper	upper	ADJ
ajst-20193	49	21	layer	layer	NOUN
ajst-20193	49	22	and	and	CCONJ
ajst-20193	49	23	extract	extract	VERB
ajst-20193	49	24	the	the	DET
ajst-20193	49	25	characteristics	characteristic	NOUN
ajst-20193	49	26	of	of	ADP
ajst-20193	49	27	various	various	ADJ
ajst-20193	49	28	time	time	NOUN
ajst-20193	49	29	series	series	PROPN
ajst-20193	49	30	data	data	PROPN
ajst-20193	49	31	.	.	PUNCT
ajst-20193	50	1	causal	causal	ADJ
ajst-20193	50	2	convolution	convolution	NOUN
ajst-20193	50	3	can	can	AUX
ajst-20193	50	4	ensure	ensure	VERB
ajst-20193	50	5	the	the	DET
ajst-20193	50	6	causality	causality	NOUN
ajst-20193	50	7	of	of	ADP
ajst-20193	50	8	extracted	extract	VERB
ajst-20193	50	9	feature	feature	NOUN
ajst-20193	50	10	information	information	NOUN
ajst-20193	50	11	.	.	PUNCT
ajst-20193	51	1	for	for	ADP
ajst-20193	51	2	tcn	tcn	NOUN
ajst-20193	51	3	with	with	ADP
ajst-20193	51	4	convolution	convolution	NOUN
ajst-20193	51	5	core	core	NOUN
ajst-20193	51	6	of	of	ADP
ajst-20193	51	7	2	2	NUM
ajst-20193	51	8	and	and	CCONJ
ajst-20193	51	9	expansion	expansion	NOUN
ajst-20193	51	10	coefficients	coefficient	NOUN
ajst-20193	51	11	of	of	ADP
ajst-20193	51	12	1	1	NUM
ajst-20193	51	13	,	,	PUNCT
ajst-20193	51	14	2	2	NUM
ajst-20193	51	15	,	,	PUNCT
ajst-20193	51	16	and	and	CCONJ
ajst-20193	51	17	4	4	NUM
ajst-20193	51	18	,	,	PUNCT
ajst-20193	51	19	the	the	DET
ajst-20193	51	20	expansion	expansion	NOUN
ajst-20193	51	21	causal	causal	NOUN
ajst-20193	51	22	convolution	convolution	NOUN
ajst-20193	51	23	structure	structure	NOUN
ajst-20193	51	24	is	be	AUX
ajst-20193	51	25	shown	show	VERB
ajst-20193	51	26	in	in	ADP
ajst-20193	51	27	figure	figure	NOUN
ajst-20193	51	28	1	1	NUM
ajst-20193	51	29	.	.	PUNCT
ajst-20193	52	1	figure	figure	NOUN
ajst-20193	52	2	1	1	NUM
ajst-20193	52	3	.	.	PUNCT
ajst-20193	53	1	tcn	tcn	NOUN
ajst-20193	53	2	expansion	expansion	NOUN
ajst-20193	53	3	causal	causal	NOUN
ajst-20193	53	4	structure	structure	NOUN
ajst-20193	53	5	(	(	PUNCT
ajst-20193	53	6	1	1	X
ajst-20193	53	7	)	)	PUNCT
ajst-20193	53	8	causal	causal	ADJ
ajst-20193	53	9	convolution	convolution	NOUN
ajst-20193	53	10	:	:	PUNCT
ajst-20193	53	11	this	this	DET
ajst-20193	53	12	convolution	convolution	NOUN
ajst-20193	53	13	ensures	ensure	VERB
ajst-20193	53	14	that	that	SCONJ
ajst-20193	53	15	the	the	DET
ajst-20193	53	16	output	output	NOUN
ajst-20193	53	17	of	of	ADP
ajst-20193	53	18	the	the	DET
ajst-20193	53	19	model	model	NOUN
ajst-20193	53	20	only	only	ADV
ajst-20193	53	21	depends	depend	VERB
ajst-20193	53	22	on	on	ADP
ajst-20193	53	23	current	current	ADJ
ajst-20193	53	24	and	and	CCONJ
ajst-20193	53	25	past	past	ADJ
ajst-20193	53	26	inputs	input	NOUN
ajst-20193	53	27	,	,	PUNCT
ajst-20193	53	28	and	and	CCONJ
ajst-20193	53	29	will	will	AUX
ajst-20193	53	30	not	not	PART
ajst-20193	53	31	reveal	reveal	VERB
ajst-20193	53	32	future	future	ADJ
ajst-20193	53	33	information	information	NOUN
ajst-20193	53	34	.	.	PUNCT
ajst-20193	54	1	(	(	PUNCT
ajst-20193	54	2	2	2	X
ajst-20193	54	3	)	)	PUNCT
ajst-20193	54	4	causal	causal	ADJ
ajst-20193	54	5	convolution	convolution	NOUN
ajst-20193	54	6	:	:	PUNCT
ajst-20193	54	7	expansion	expansion	NOUN
ajst-20193	54	8	convolution	convolution	NOUN
ajst-20193	54	9	allows	allow	VERB
ajst-20193	54	10	the	the	DET
ajst-20193	54	11	network	network	NOUN
ajst-20193	54	12	to	to	PART
ajst-20193	54	13	expand	expand	VERB
ajst-20193	54	14	its	its	PRON
ajst-20193	54	15	receptive	receptive	ADJ
ajst-20193	54	16	field	field	NOUN
ajst-20193	54	17	while	while	SCONJ
ajst-20193	54	18	maintaining	maintain	VERB
ajst-20193	54	19	the	the	DET
ajst-20193	54	20	same	same	ADJ
ajst-20193	54	21	number	number	NOUN
ajst-20193	54	22	of	of	ADP
ajst-20193	54	23	parameters	parameter	NOUN
ajst-20193	54	24	.	.	PUNCT
ajst-20193	55	1	by	by	ADP
ajst-20193	55	2	using	use	VERB
ajst-20193	55	3	different	different	ADJ
ajst-20193	55	4	expansion	expansion	NOUN
ajst-20193	55	5	rates	rate	NOUN
ajst-20193	55	6	in	in	ADP
ajst-20193	55	7	different	different	ADJ
ajst-20193	55	8	layers	layer	NOUN
ajst-20193	55	9	,	,	PUNCT
ajst-20193	55	10	it	it	PRON
ajst-20193	55	11	can	can	AUX
ajst-20193	55	12	effectively	effectively	ADV
ajst-20193	55	13	capture	capture	VERB
ajst-20193	55	14	long	long	ADJ
ajst-20193	55	15	-	-	PUNCT
ajst-20193	55	16	term	term	NOUN
ajst-20193	55	17	time	time	NOUN
ajst-20193	55	18	dependence	dependence	NOUN
ajst-20193	55	19	.	.	PUNCT
ajst-20193	56	1	(	(	PUNCT
ajst-20193	56	2	3	3	X
ajst-20193	56	3	)	)	PUNCT
ajst-20193	56	4	residual	residual	ADJ
ajst-20193	56	5	connections	connection	NOUN
ajst-20193	56	6	:	:	PUNCT
ajst-20193	56	7	residual	residual	ADJ
ajst-20193	56	8	connection	connection	NOUN
ajst-20193	56	9	helps	help	VERB
ajst-20193	56	10	to	to	PART
ajst-20193	56	11	solve	solve	VERB
ajst-20193	56	12	the	the	DET
ajst-20193	56	13	problem	problem	NOUN
ajst-20193	56	14	of	of	ADP
ajst-20193	56	15	gradient	gradient	ADJ
ajst-20193	56	16	disappearance	disappearance	NOUN
ajst-20193	56	17	in	in	ADP
ajst-20193	56	18	the	the	DET
ajst-20193	56	19	deep	deep	ADJ
ajst-20193	56	20	network	network	NOUN
ajst-20193	56	21	,	,	PUNCT
ajst-20193	56	22	making	make	VERB
ajst-20193	56	23	the	the	DET
ajst-20193	56	24	network	network	NOUN
ajst-20193	56	25	deeper	deeply	ADV
ajst-20193	56	26	,	,	PUNCT
ajst-20193	56	27	while	while	SCONJ
ajst-20193	56	28	maintaining	maintain	VERB
ajst-20193	56	29	the	the	DET
ajst-20193	56	30	stability	stability	NOUN
ajst-20193	56	31	of	of	ADP
ajst-20193	56	32	training	training	NOUN
ajst-20193	56	33	.	.	PUNCT
ajst-20193	57	1	the	the	DET
ajst-20193	57	2	network	network	NOUN
ajst-20193	57	3	can	can	AUX
ajst-20193	57	4	learn	learn	VERB
ajst-20193	57	5	residual	residual	ADJ
ajst-20193	57	6	mapping	mapping	NOUN
ajst-20193	57	7	by	by	ADP
ajst-20193	57	8	adding	add	VERB
ajst-20193	57	9	the	the	DET
ajst-20193	57	10	input	input	NOUN
ajst-20193	57	11	directly	directly	ADV
ajst-20193	57	12	to	to	ADP
ajst-20193	57	13	the	the	DET
ajst-20193	57	14	following	follow	VERB
ajst-20193	57	15	layer	layer	NOUN
ajst-20193	57	16	through	through	ADP
ajst-20193	57	17	jump	jump	NOUN
ajst-20193	57	18	connection	connection	NOUN
ajst-20193	57	19	.	.	PUNCT
ajst-20193	58	1	(	(	PUNCT
ajst-20193	58	2	4	4	X
ajst-20193	58	3	)	)	PUNCT
ajst-20193	58	4	batch	batch	NOUN
ajst-20193	58	5	normalization	normalization	NOUN
ajst-20193	58	6	:	:	PUNCT
ajst-20193	58	7	batch	batch	NOUN
ajst-20193	58	8	normalization	normalization	NOUN
ajst-20193	58	9	helps	help	VERB
ajst-20193	58	10	stabilize	stabilize	VERB
ajst-20193	58	11	the	the	DET
ajst-20193	58	12	training	training	NOUN
ajst-20193	58	13	process	process	NOUN
ajst-20193	58	14	,	,	PUNCT
ajst-20193	58	15	reduce	reduce	VERB
ajst-20193	58	16	internal	internal	ADJ
ajst-20193	58	17	covariate	covariate	ADJ
ajst-20193	58	18	deviation	deviation	NOUN
ajst-20193	58	19	,	,	PUNCT
ajst-20193	58	20	accelerate	accelerate	VERB
ajst-20193	58	21	convergence	convergence	NOUN
ajst-20193	58	22	,	,	PUNCT
ajst-20193	58	23	and	and	CCONJ
ajst-20193	58	24	improve	improve	VERB
ajst-20193	58	25	the	the	DET
ajst-20193	58	26	generalization	generalization	NOUN
ajst-20193	58	27	ability	ability	NOUN
ajst-20193	58	28	of	of	ADP
ajst-20193	58	29	the	the	DET
ajst-20193	58	30	model	model	NOUN
ajst-20193	58	31	.	.	PUNCT
ajst-20193	59	1	it	it	PRON
ajst-20193	59	2	makes	make	VERB
ajst-20193	59	3	the	the	DET
ajst-20193	59	4	distribution	distribution	NOUN
ajst-20193	59	5	of	of	ADP
ajst-20193	59	6	input	input	NOUN
ajst-20193	59	7	at	at	ADP
ajst-20193	59	8	each	each	DET
ajst-20193	59	9	level	level	NOUN
ajst-20193	59	10	more	more	ADV
ajst-20193	59	11	stable	stable	ADJ
ajst-20193	59	12	by	by	ADP
ajst-20193	59	13	normalizing	normalize	VERB
ajst-20193	59	14	each	each	DET
ajst-20193	59	15	small	small	ADJ
ajst-20193	59	16	batch	batch	NOUN
ajst-20193	59	17	of	of	ADP
ajst-20193	59	18	data	datum	NOUN
ajst-20193	59	19	.	.	PUNCT
ajst-20193	60	1	figure	figure	NOUN
ajst-20193	60	2	2	2	NUM
ajst-20193	60	3	.	.	PUNCT
ajst-20193	61	1	tcn	tcn	VERB
ajst-20193	61	2	residual	residual	ADJ
ajst-20193	61	3	unit	unit	NOUN
ajst-20193	61	4	3.2	3.2	NUM
ajst-20193	61	5	.	.	PUNCT
ajst-20193	62	1	transformer	transformer	NOUN
ajst-20193	62	2	transformer	transformer	NOUN
ajst-20193	62	3	is	be	AUX
ajst-20193	62	4	a	a	DET
ajst-20193	62	5	deep	deep	ADJ
ajst-20193	62	6	learning	learning	NOUN
ajst-20193	62	7	model	model	NOUN
ajst-20193	62	8	based	base	VERB
ajst-20193	62	9	on	on	ADP
ajst-20193	62	10	self	self	NOUN
ajst-20193	62	11	attention	attention	NOUN
ajst-20193	62	12	mechanism	mechanism	NOUN
ajst-20193	62	13	,	,	PUNCT
ajst-20193	62	14	which	which	PRON
ajst-20193	62	15	was	be	AUX
ajst-20193	62	16	proposed	propose	VERB
ajst-20193	62	17	by	by	ADP
ajst-20193	62	18	vaswani	vaswani	PROPN
ajst-20193	62	19	et	et	PROPN
ajst-20193	62	20	al	al	PROPN
ajst-20193	62	21	.	.	PROPN
ajst-20193	63	1	in	in	ADP
ajst-20193	63	2	2017	2017	NUM
ajst-20193	63	3	and	and	CCONJ
ajst-20193	63	4	is	be	AUX
ajst-20193	63	5	mainly	mainly	ADV
ajst-20193	63	6	used	use	VERB
ajst-20193	63	7	in	in	ADP
ajst-20193	63	8	the	the	DET
ajst-20193	63	9	field	field	NOUN
ajst-20193	63	10	of	of	ADP
ajst-20193	63	11	natural	natural	ADJ
ajst-20193	63	12	language	language	NOUN
ajst-20193	63	13	93	93	NUM
ajst-20193	63	14	processing	processing	NOUN
ajst-20193	63	15	.	.	PUNCT
ajst-20193	64	1	through	through	ADP
ajst-20193	64	2	the	the	DET
ajst-20193	64	3	self	self	NOUN
ajst-20193	64	4	attention	attention	NOUN
ajst-20193	64	5	mechanism	mechanism	NOUN
ajst-20193	64	6	,	,	PUNCT
ajst-20193	64	7	this	this	DET
ajst-20193	64	8	model	model	NOUN
ajst-20193	64	9	allows	allow	VERB
ajst-20193	64	10	all	all	DET
ajst-20193	64	11	other	other	ADJ
ajst-20193	64	12	inputs	input	NOUN
ajst-20193	64	13	in	in	ADP
ajst-20193	64	14	the	the	DET
ajst-20193	64	15	sequence	sequence	NOUN
ajst-20193	64	16	to	to	PART
ajst-20193	64	17	be	be	AUX
ajst-20193	64	18	considered	consider	VERB
ajst-20193	64	19	while	while	SCONJ
ajst-20193	64	20	processing	process	VERB
ajst-20193	64	21	each	each	DET
ajst-20193	64	22	input	input	NOUN
ajst-20193	64	23	,	,	PUNCT
ajst-20193	64	24	and	and	CCONJ
ajst-20193	64	25	gives	give	VERB
ajst-20193	64	26	different	different	ADJ
ajst-20193	64	27	weights	weight	NOUN
ajst-20193	64	28	according	accord	VERB
ajst-20193	64	29	to	to	ADP
ajst-20193	64	30	their	their	PRON
ajst-20193	64	31	relationships	relationship	NOUN
ajst-20193	64	32	.	.	PUNCT
ajst-20193	65	1	in	in	ADP
ajst-20193	65	2	order	order	NOUN
ajst-20193	65	3	to	to	PART
ajst-20193	65	4	further	far	ADV
ajst-20193	65	5	enhance	enhance	VERB
ajst-20193	65	6	the	the	DET
ajst-20193	65	7	expression	expression	NOUN
ajst-20193	65	8	ability	ability	NOUN
ajst-20193	65	9	of	of	ADP
ajst-20193	65	10	the	the	DET
ajst-20193	65	11	model	model	NOUN
ajst-20193	65	12	,	,	PUNCT
ajst-20193	65	13	transformer	transformer	NOUN
ajst-20193	65	14	adopts	adopt	VERB
ajst-20193	65	15	a	a	DET
ajst-20193	65	16	multi	multi	ADJ
ajst-20193	65	17	head	head	NOUN
ajst-20193	65	18	attention	attention	NOUN
ajst-20193	65	19	mechanism	mechanism	NOUN
ajst-20193	65	20	,	,	PUNCT
ajst-20193	65	21	which	which	PRON
ajst-20193	65	22	applies	apply	VERB
ajst-20193	65	23	the	the	DET
ajst-20193	65	24	self	self	NOUN
ajst-20193	65	25	attention	attention	NOUN
ajst-20193	65	26	mechanism	mechanism	NOUN
ajst-20193	65	27	in	in	ADP
ajst-20193	65	28	parallel	parallel	NOUN
ajst-20193	65	29	for	for	ADP
ajst-20193	65	30	many	many	ADJ
ajst-20193	65	31	times	time	NOUN
ajst-20193	65	32	,	,	PUNCT
ajst-20193	65	33	paying	pay	VERB
ajst-20193	65	34	attention	attention	NOUN
ajst-20193	65	35	to	to	ADP
ajst-20193	65	36	different	different	ADJ
ajst-20193	65	37	information	information	NOUN
ajst-20193	65	38	each	each	DET
ajst-20193	65	39	time	time	NOUN
ajst-20193	65	40	,	,	PUNCT
ajst-20193	65	41	and	and	CCONJ
ajst-20193	65	42	then	then	ADV
ajst-20193	65	43	stitching	stitch	VERB
ajst-20193	65	44	these	these	DET
ajst-20193	65	45	results	result	NOUN
ajst-20193	65	46	together	together	ADV
ajst-20193	65	47	and	and	CCONJ
ajst-20193	65	48	processing	process	VERB
ajst-20193	65	49	them	they	PRON
ajst-20193	65	50	through	through	ADP
ajst-20193	65	51	a	a	DET
ajst-20193	65	52	linear	linear	ADJ
ajst-20193	65	53	layer	layer	NOUN
ajst-20193	65	54	.	.	PUNCT
ajst-20193	66	1	in	in	ADP
ajst-20193	66	2	addition	addition	NOUN
ajst-20193	66	3	,	,	PUNCT
ajst-20193	66	4	position	position	NOUN
ajst-20193	66	5	coding	coding	NOUN
ajst-20193	66	6	is	be	AUX
ajst-20193	66	7	introduced	introduce	VERB
ajst-20193	66	8	to	to	PART
ajst-20193	66	9	represent	represent	VERB
ajst-20193	66	10	the	the	DET
ajst-20193	66	11	position	position	NOUN
ajst-20193	66	12	information	information	NOUN
ajst-20193	66	13	of	of	ADP
ajst-20193	66	14	words	word	NOUN
ajst-20193	66	15	in	in	ADP
ajst-20193	66	16	the	the	DET
ajst-20193	66	17	input	input	NOUN
ajst-20193	66	18	sequence	sequence	NOUN
ajst-20193	66	19	,	,	PUNCT
ajst-20193	66	20	so	so	SCONJ
ajst-20193	66	21	that	that	SCONJ
ajst-20193	66	22	the	the	DET
ajst-20193	66	23	model	model	NOUN
ajst-20193	66	24	can	can	AUX
ajst-20193	66	25	capture	capture	VERB
ajst-20193	66	26	the	the	DET
ajst-20193	66	27	sequence	sequence	NOUN
ajst-20193	66	28	information	information	NOUN
ajst-20193	66	29	of	of	ADP
ajst-20193	66	30	the	the	DET
ajst-20193	66	31	sequence	sequence	NOUN
ajst-20193	66	32	.	.	PUNCT
ajst-20193	67	1	transformer	transformer	NOUN
ajst-20193	67	2	model	model	NOUN
ajst-20193	67	3	also	also	ADV
ajst-20193	67	4	includes	include	VERB
ajst-20193	67	5	feedforward	feedforward	ADJ
ajst-20193	67	6	neural	neural	ADJ
ajst-20193	67	7	network	network	NOUN
ajst-20193	67	8	,	,	PUNCT
ajst-20193	67	9	which	which	PRON
ajst-20193	67	10	provides	provide	VERB
ajst-20193	67	11	nonlinear	nonlinear	ADJ
ajst-20193	67	12	capability	capability	NOUN
ajst-20193	67	13	for	for	ADP
ajst-20193	67	14	the	the	DET
ajst-20193	67	15	model	model	NOUN
ajst-20193	67	16	.	.	PUNCT
ajst-20193	68	1	each	each	DET
ajst-20193	68	2	self	self	NOUN
ajst-20193	68	3	attention	attention	NOUN
ajst-20193	68	4	and	and	CCONJ
ajst-20193	68	5	feedforward	feedforward	NOUN
ajst-20193	68	6	network	network	NOUN
ajst-20193	68	7	layer	layer	NOUN
ajst-20193	68	8	is	be	AUX
ajst-20193	68	9	followed	follow	VERB
ajst-20193	68	10	by	by	ADP
ajst-20193	68	11	residual	residual	ADJ
ajst-20193	68	12	connection	connection	NOUN
ajst-20193	68	13	and	and	CCONJ
ajst-20193	68	14	layer	layer	NOUN
ajst-20193	68	15	normalization	normalization	NOUN
ajst-20193	68	16	,	,	PUNCT
ajst-20193	68	17	which	which	PRON
ajst-20193	68	18	is	be	AUX
ajst-20193	68	19	helpful	helpful	ADJ
ajst-20193	68	20	to	to	ADP
ajst-20193	68	21	the	the	DET
ajst-20193	68	22	training	training	NOUN
ajst-20193	68	23	stability	stability	NOUN
ajst-20193	68	24	and	and	CCONJ
ajst-20193	68	25	performance	performance	NOUN
ajst-20193	68	26	of	of	ADP
ajst-20193	68	27	the	the	DET
ajst-20193	68	28	model	model	NOUN
ajst-20193	68	29	.	.	PUNCT
ajst-20193	69	1	in	in	ADP
ajst-20193	69	2	general	general	ADJ
ajst-20193	69	3	,	,	PUNCT
ajst-20193	69	4	transformer	transformer	NOUN
ajst-20193	69	5	model	model	NOUN
ajst-20193	69	6	has	have	AUX
ajst-20193	69	7	become	become	VERB
ajst-20193	69	8	one	one	NUM
ajst-20193	69	9	of	of	ADP
ajst-20193	69	10	the	the	DET
ajst-20193	69	11	mainstream	mainstream	NOUN
ajst-20193	69	12	models	model	NOUN
ajst-20193	69	13	in	in	ADP
ajst-20193	69	14	the	the	DET
ajst-20193	69	15	field	field	NOUN
ajst-20193	69	16	of	of	ADP
ajst-20193	69	17	natural	natural	ADJ
ajst-20193	69	18	language	language	NOUN
ajst-20193	69	19	processing	processing	NOUN
ajst-20193	69	20	due	due	ADP
ajst-20193	69	21	to	to	ADP
ajst-20193	69	22	its	its	PRON
ajst-20193	69	23	efficient	efficient	ADJ
ajst-20193	69	24	self	self	NOUN
ajst-20193	69	25	attention	attention	NOUN
ajst-20193	69	26	mechanism	mechanism	NOUN
ajst-20193	69	27	and	and	CCONJ
ajst-20193	69	28	parallel	parallel	ADJ
ajst-20193	69	29	computing	computing	NOUN
ajst-20193	69	30	ability	ability	NOUN
ajst-20193	69	31	,	,	PUNCT
ajst-20193	69	32	and	and	CCONJ
ajst-20193	69	33	has	have	AUX
ajst-20193	69	34	also	also	ADV
ajst-20193	69	35	been	be	AUX
ajst-20193	69	36	applied	apply	VERB
ajst-20193	69	37	in	in	ADP
ajst-20193	69	38	other	other	ADJ
ajst-20193	69	39	fields	field	NOUN
ajst-20193	69	40	such	such	ADJ
ajst-20193	69	41	as	as	ADP
ajst-20193	69	42	computer	computer	NOUN
ajst-20193	69	43	vision	vision	NOUN
ajst-20193	69	44	and	and	CCONJ
ajst-20193	69	45	audio	audio	NOUN
ajst-20193	69	46	processing	processing	NOUN
ajst-20193	69	47	.	.	PUNCT
ajst-20193	70	1	figure	figure	NOUN
ajst-20193	70	2	3	3	NUM
ajst-20193	70	3	.	.	PUNCT
ajst-20193	70	4	transformer	transformer	ADJ
ajst-20193	70	5	structure	structure	NOUN
ajst-20193	70	6	3.3	3.3	NUM
ajst-20193	70	7	.	.	PUNCT
ajst-20193	71	1	construction	construction	NOUN
ajst-20193	71	2	of	of	ADP
ajst-20193	71	3	anomaly	anomaly	NOUN
ajst-20193	71	4	detection	detection	NOUN
ajst-20193	71	5	algorithm	algorithm	NOUN
ajst-20193	71	6	the	the	DET
ajst-20193	71	7	anomaly	anomaly	NOUN
ajst-20193	71	8	detection	detection	NOUN
ajst-20193	71	9	algorithm	algorithm	NOUN
ajst-20193	71	10	based	base	VERB
ajst-20193	71	11	on	on	ADP
ajst-20193	71	12	tcn	tcn	PROPN
ajst-20193	71	13	transformer	transformer	NOUN
ajst-20193	71	14	is	be	AUX
ajst-20193	71	15	a	a	DET
ajst-20193	71	16	technology	technology	NOUN
ajst-20193	71	17	combining	combine	VERB
ajst-20193	71	18	time	time	NOUN
ajst-20193	71	19	convolution	convolution	NOUN
ajst-20193	71	20	network	network	NOUN
ajst-20193	71	21	(	(	PUNCT
ajst-20193	71	22	tcn	tcn	PROPN
ajst-20193	71	23	)	)	PUNCT
ajst-20193	71	24	and	and	CCONJ
ajst-20193	71	25	transformer	transformer	NOUN
ajst-20193	71	26	model	model	NOUN
ajst-20193	71	27	,	,	PUNCT
ajst-20193	71	28	which	which	PRON
ajst-20193	71	29	is	be	AUX
ajst-20193	71	30	specially	specially	ADV
ajst-20193	71	31	used	use	VERB
ajst-20193	71	32	to	to	PART
ajst-20193	71	33	identify	identify	VERB
ajst-20193	71	34	abnormal	abnormal	ADJ
ajst-20193	71	35	points	point	NOUN
ajst-20193	71	36	in	in	ADP
ajst-20193	71	37	landslide	landslide	NOUN
ajst-20193	71	38	displacement	displacement	NOUN
ajst-20193	71	39	monitoring	monitoring	NOUN
ajst-20193	71	40	data	datum	NOUN
ajst-20193	71	41	.	.	PUNCT
ajst-20193	72	1	the	the	DET
ajst-20193	72	2	algorithm	algorithm	NOUN
ajst-20193	72	3	first	first	ADV
ajst-20193	72	4	uses	use	VERB
ajst-20193	72	5	tcn	tcn	NOUN
ajst-20193	72	6	to	to	ADP
ajst-20193	72	7	model	model	NOUN
ajst-20193	72	8	time	time	PROPN
ajst-20193	72	9	series	series	PROPN
ajst-20193	72	10	data	data	PROPN
ajst-20193	72	11	and	and	CCONJ
ajst-20193	72	12	capture	capture	VERB
ajst-20193	72	13	long	long	ADJ
ajst-20193	72	14	-	-	PUNCT
ajst-20193	72	15	term	term	NOUN
ajst-20193	72	16	dependencies	dependency	NOUN
ajst-20193	72	17	,	,	PUNCT
ajst-20193	72	18	and	and	CCONJ
ajst-20193	72	19	then	then	ADV
ajst-20193	72	20	further	further	ADJ
ajst-20193	72	21	extracts	extract	NOUN
ajst-20193	72	22	data	datum	NOUN
ajst-20193	72	23	features	feature	VERB
ajst-20193	72	24	through	through	ADP
ajst-20193	72	25	transformer	transformer	NOUN
ajst-20193	72	26	model	model	NOUN
ajst-20193	72	27	.	.	PUNCT
ajst-20193	73	1	finally	finally	ADV
ajst-20193	73	2	,	,	PUNCT
ajst-20193	73	3	anomaly	anomaly	NOUN
ajst-20193	73	4	recognition	recognition	NOUN
ajst-20193	73	5	is	be	AUX
ajst-20193	73	6	carried	carry	VERB
ajst-20193	73	7	out	out	ADP
ajst-20193	73	8	by	by	ADP
ajst-20193	73	9	combining	combine	VERB
ajst-20193	73	10	the	the	DET
ajst-20193	73	11	self	self	NOUN
ajst-20193	73	12	attention	attention	NOUN
ajst-20193	73	13	mechanism	mechanism	NOUN
ajst-20193	73	14	and	and	CCONJ
ajst-20193	73	15	the	the	DET
ajst-20193	73	16	feature	feature	NOUN
ajst-20193	73	17	information	information	NOUN
ajst-20193	73	18	extracted	extract	VERB
ajst-20193	73	19	by	by	ADP
ajst-20193	73	20	tcn	tcn	PROPN
ajst-20193	73	21	.	.	PUNCT
ajst-20193	74	1	the	the	DET
ajst-20193	74	2	algorithm	algorithm	NOUN
ajst-20193	74	3	can	can	AUX
ajst-20193	74	4	effectively	effectively	ADV
ajst-20193	74	5	identify	identify	VERB
ajst-20193	74	6	abnormal	abnormal	ADJ
ajst-20193	74	7	points	point	NOUN
ajst-20193	74	8	and	and	CCONJ
ajst-20193	74	9	provide	provide	VERB
ajst-20193	74	10	accurate	accurate	ADJ
ajst-20193	74	11	data	datum	NOUN
ajst-20193	74	12	support	support	NOUN
ajst-20193	74	13	for	for	ADP
ajst-20193	74	14	landslide	landslide	NOUN
ajst-20193	74	15	early	early	ADJ
ajst-20193	74	16	warning	warning	NOUN
ajst-20193	74	17	.	.	PUNCT
ajst-20193	75	1	this	this	DET
ajst-20193	75	2	method	method	NOUN
ajst-20193	75	3	has	have	VERB
ajst-20193	75	4	significant	significant	ADJ
ajst-20193	75	5	advantages	advantage	NOUN
ajst-20193	75	6	in	in	ADP
ajst-20193	75	7	processing	process	VERB
ajst-20193	75	8	large	large	ADJ
ajst-20193	75	9	-	-	PUNCT
ajst-20193	75	10	scale	scale	NOUN
ajst-20193	75	11	and	and	CCONJ
ajst-20193	75	12	high	high	ADV
ajst-20193	75	13	-	-	PUNCT
ajst-20193	75	14	dimensional	dimensional	ADJ
ajst-20193	75	15	landslide	landslide	NOUN
ajst-20193	75	16	displacement	displacement	NOUN
ajst-20193	75	17	monitoring	monitoring	NOUN
ajst-20193	75	18	data	datum	NOUN
ajst-20193	75	19	,	,	PUNCT
ajst-20193	75	20	and	and	CCONJ
ajst-20193	75	21	can	can	AUX
ajst-20193	75	22	improve	improve	VERB
ajst-20193	75	23	the	the	DET
ajst-20193	75	24	accuracy	accuracy	NOUN
ajst-20193	75	25	and	and	CCONJ
ajst-20193	75	26	efficiency	efficiency	NOUN
ajst-20193	75	27	of	of	ADP
ajst-20193	75	28	anomaly	anomaly	NOUN
ajst-20193	75	29	detection	detection	NOUN
ajst-20193	75	30	.	.	PUNCT
ajst-20193	76	1	the	the	DET
ajst-20193	76	2	model	model	NOUN
ajst-20193	76	3	diagram	diagram	NOUN
ajst-20193	76	4	of	of	ADP
ajst-20193	76	5	this	this	DET
ajst-20193	76	6	algorithm	algorithm	NOUN
ajst-20193	76	7	is	be	AUX
ajst-20193	76	8	shown	show	VERB
ajst-20193	76	9	in	in	ADP
ajst-20193	76	10	figure	figure	NOUN
ajst-20193	76	11	4	4	NUM
ajst-20193	76	12	.	.	PUNCT
ajst-20193	76	13	figure	figure	VERB
ajst-20193	76	14	4	4	NUM
ajst-20193	76	15	.	.	PUNCT
ajst-20193	76	16	tcn	tcn	PROPN
ajst-20193	76	17	transformer	transformer	NOUN
ajst-20193	76	18	model	model	NOUN
ajst-20193	76	19	diagram	diagram	PROPN
ajst-20193	76	20	4	4	NUM
ajst-20193	76	21	.	.	PUNCT
ajst-20193	77	1	experimental	experimental	ADJ
ajst-20193	77	2	analysis	analysis	NOUN
ajst-20193	77	3	4.1	4.1	NUM
ajst-20193	77	4	.	.	PUNCT
ajst-20193	78	1	experimental	experimental	ADJ
ajst-20193	78	2	environment	environment	NOUN
ajst-20193	78	3	the	the	DET
ajst-20193	78	4	running	run	VERB
ajst-20193	78	5	environment	environment	NOUN
ajst-20193	78	6	of	of	ADP
ajst-20193	78	7	machine	machine	NOUN
ajst-20193	78	8	learning	learning	NOUN
ajst-20193	78	9	model	model	NOUN
ajst-20193	78	10	in	in	ADP
ajst-20193	78	11	this	this	DET
ajst-20193	78	12	paper	paper	NOUN
ajst-20193	78	13	is	be	AUX
ajst-20193	78	14	shown	show	VERB
ajst-20193	78	15	in	in	ADP
ajst-20193	78	16	the	the	DET
ajst-20193	78	17	table	table	NOUN
ajst-20193	78	18	below	below	ADV
ajst-20193	78	19	:	:	PUNCT
ajst-20193	78	20	table	table	NOUN
ajst-20193	78	21	1	1	NUM
ajst-20193	78	22	.	.	PUNCT
ajst-20193	78	23	experimental	experimental	ADJ
ajst-20193	78	24	environment	environment	NOUN
ajst-20193	78	25	name	name	NOUN
ajst-20193	78	26	specifications	specification	NOUN
ajst-20193	78	27	operating	operating	NOUN
ajst-20193	78	28	system	system	NOUN
ajst-20193	78	29	windows11	windows11	NOUN
ajst-20193	78	30	memory	memory	NOUN
ajst-20193	78	31	capacity	capacity	NOUN
ajst-20193	78	32	16	16	NUM
ajst-20193	78	33	gb	gb	NOUN
ajst-20193	78	34	cpu	cpu	VERB
ajst-20193	78	35	amd	amd	INTJ
ajst-20193	78	36	ryzen	ryzen	ADJ
ajst-20193	78	37	7	7	NUM
ajst-20193	78	38	5800h	5800h	PROPN
ajst-20193	78	39	gru	gru	PROPN
ajst-20193	78	40	rtx3060	rtx3060	NOUN
ajst-20193	78	41	python	python	NOUN
ajst-20193	78	42	3.8.15	3.8.15	NUM
ajst-20193	78	43	4.2	4.2	NUM
ajst-20193	78	44	.	.	PUNCT
ajst-20193	79	1	evaluating	evaluate	VERB
ajst-20193	79	2	indicator	indicator	NOUN
ajst-20193	79	3	model	model	NOUN
ajst-20193	79	4	evaluation	evaluation	NOUN
ajst-20193	79	5	indicators	indicator	NOUN
ajst-20193	79	6	play	play	VERB
ajst-20193	79	7	a	a	DET
ajst-20193	79	8	crucial	crucial	ADJ
ajst-20193	79	9	role	role	NOUN
ajst-20193	79	10	in	in	ADP
ajst-20193	79	11	deep	deep	ADJ
ajst-20193	79	12	learning	learning	NOUN
ajst-20193	79	13	tasks	task	NOUN
ajst-20193	79	14	.	.	PUNCT
ajst-20193	80	1	they	they	PRON
ajst-20193	80	2	can	can	AUX
ajst-20193	80	3	help	help	VERB
ajst-20193	80	4	researchers	researcher	NOUN
ajst-20193	80	5	better	well	ADV
ajst-20193	80	6	identify	identify	VERB
ajst-20193	80	7	and	and	CCONJ
ajst-20193	80	8	evaluate	evaluate	VERB
ajst-20193	80	9	the	the	DET
ajst-20193	80	10	effects	effect	NOUN
ajst-20193	80	11	of	of	ADP
ajst-20193	80	12	different	different	ADJ
ajst-20193	80	13	tasks	task	NOUN
ajst-20193	80	14	,	,	PUNCT
ajst-20193	80	15	such	such	ADJ
ajst-20193	80	16	as	as	ADP
ajst-20193	80	17	measuring	measure	VERB
ajst-20193	80	18	the	the	DET
ajst-20193	80	19	experimental	experimental	ADJ
ajst-20193	80	20	effects	effect	NOUN
ajst-20193	80	21	of	of	ADP
ajst-20193	80	22	classification	classification	NOUN
ajst-20193	80	23	,	,	PUNCT
ajst-20193	80	24	regression	regression	NOUN
ajst-20193	80	25	,	,	PUNCT
ajst-20193	80	26	sorting	sorting	NOUN
ajst-20193	80	27	,	,	PUNCT
ajst-20193	80	28	clustering	clustering	NOUN
ajst-20193	80	29	and	and	CCONJ
ajst-20193	80	30	other	other	ADJ
ajst-20193	80	31	tasks	task	NOUN
ajst-20193	80	32	with	with	ADP
ajst-20193	80	33	model	model	NOUN
ajst-20193	80	34	evaluation	evaluation	NOUN
ajst-20193	80	35	indicators	indicator	NOUN
ajst-20193	80	36	,	,	PUNCT
ajst-20193	80	37	so	so	SCONJ
ajst-20193	80	38	as	as	SCONJ
ajst-20193	80	39	to	to	PART
ajst-20193	80	40	help	help	VERB
ajst-20193	80	41	the	the	DET
ajst-20193	80	42	experimenter	experimenter	NOUN
ajst-20193	80	43	better	well	ADV
ajst-20193	80	44	achieve	achieve	VERB
ajst-20193	80	45	the	the	DET
ajst-20193	80	46	task	task	NOUN
ajst-20193	80	47	objectives	objective	NOUN
ajst-20193	80	48	.	.	PUNCT
ajst-20193	81	1	therefore	therefore	ADV
ajst-20193	81	2	,	,	PUNCT
ajst-20193	81	3	it	it	PRON
ajst-20193	81	4	is	be	AUX
ajst-20193	81	5	very	very	ADV
ajst-20193	81	6	important	important	ADJ
ajst-20193	81	7	to	to	PART
ajst-20193	81	8	select	select	VERB
ajst-20193	81	9	appropriate	appropriate	ADJ
ajst-20193	81	10	model	model	NOUN
ajst-20193	81	11	evaluation	evaluation	NOUN
ajst-20193	81	12	indicators	indicator	NOUN
ajst-20193	81	13	.	.	PUNCT
ajst-20193	82	1	for	for	ADP
ajst-20193	82	2	the	the	DET
ajst-20193	82	3	effect	effect	NOUN
ajst-20193	82	4	of	of	ADP
ajst-20193	82	5	anomaly	anomaly	NOUN
ajst-20193	82	6	detection	detection	NOUN
ajst-20193	82	7	tasks	task	NOUN
ajst-20193	82	8	,	,	PUNCT
ajst-20193	82	9	the	the	DET
ajst-20193	82	10	accuracy	accuracy	NOUN
ajst-20193	82	11	rate	rate	NOUN
ajst-20193	82	12	,	,	PUNCT
ajst-20193	82	13	recall	recall	NOUN
ajst-20193	82	14	rate	rate	NOUN
ajst-20193	82	15	,	,	PUNCT
ajst-20193	82	16	accuracy	accuracy	NOUN
ajst-20193	82	17	rate	rate	NOUN
ajst-20193	82	18	,	,	PUNCT
ajst-20193	82	19	f1	f1	NOUN
ajst-20193	82	20	value	value	NOUN
ajst-20193	82	21	and	and	CCONJ
ajst-20193	82	22	other	other	ADJ
ajst-20193	82	23	evaluation	evaluation	NOUN
ajst-20193	82	24	indicators	indicator	NOUN
ajst-20193	82	25	are	be	AUX
ajst-20193	82	26	used	use	VERB
ajst-20193	82	27	to	to	PART
ajst-20193	82	28	measure	measure	VERB
ajst-20193	82	29	the	the	DET
ajst-20193	82	30	performance	performance	NOUN
ajst-20193	82	31	of	of	ADP
ajst-20193	82	32	each	each	DET
ajst-20193	82	33	in	in	ADP
ajst-20193	82	34	-	-	PUNCT
ajst-20193	82	35	depth	depth	NOUN
ajst-20193	82	36	learning	learning	NOUN
ajst-20193	82	37	model	model	NOUN
ajst-20193	82	38	.	.	PUNCT
ajst-20193	83	1	considering	consider	VERB
ajst-20193	83	2	the	the	DET
ajst-20193	83	3	importance	importance	NOUN
ajst-20193	83	4	of	of	ADP
ajst-20193	83	5	the	the	DET
ajst-20193	83	6	landslide	landslide	NOUN
ajst-20193	83	7	field	field	NOUN
ajst-20193	83	8	,	,	PUNCT
ajst-20193	83	9	the	the	DET
ajst-20193	83	10	recall	recall	NOUN
ajst-20193	83	11	rate	rate	NOUN
ajst-20193	83	12	is	be	AUX
ajst-20193	83	13	taken	take	VERB
ajst-20193	83	14	as	as	ADP
ajst-20193	83	15	the	the	DET
ajst-20193	83	16	most	most	ADV
ajst-20193	83	17	important	important	ADJ
ajst-20193	83	18	indicator	indicator	NOUN
ajst-20193	83	19	.	.	PUNCT
ajst-20193	84	1	(	(	PUNCT
ajst-20193	84	2	1	1	X
ajst-20193	84	3	)	)	PUNCT
ajst-20193	84	4	accuracy	accuracy	NOUN
ajst-20193	84	5	the	the	DET
ajst-20193	84	6	accuracy	accuracy	NOUN
ajst-20193	84	7	of	of	ADP
ajst-20193	84	8	the	the	DET
ajst-20193	84	9	model	model	NOUN
ajst-20193	84	10	is	be	AUX
ajst-20193	84	11	one	one	NUM
ajst-20193	84	12	of	of	ADP
ajst-20193	84	13	the	the	DET
ajst-20193	84	14	most	most	ADV
ajst-20193	84	15	basic	basic	ADJ
ajst-20193	84	16	evaluation	evaluation	NOUN
ajst-20193	84	17	indicators	indicator	NOUN
ajst-20193	84	18	in	in	ADP
ajst-20193	84	19	the	the	DET
ajst-20193	84	20	classification	classification	NOUN
ajst-20193	84	21	problem	problem	NOUN
ajst-20193	84	22	.	.	PUNCT
ajst-20193	85	1	it	it	PRON
ajst-20193	85	2	represents	represent	VERB
ajst-20193	85	3	the	the	DET
ajst-20193	85	4	ratio	ratio	NOUN
ajst-20193	85	5	between	between	ADP
ajst-20193	85	6	the	the	DET
ajst-20193	85	7	number	number	NOUN
ajst-20193	85	8	of	of	ADP
ajst-20193	85	9	samples	sample	NOUN
ajst-20193	85	10	correctly	correctly	ADV
ajst-20193	85	11	predicted	predict	VERB
ajst-20193	85	12	by	by	ADP
ajst-20193	85	13	the	the	DET
ajst-20193	85	14	model	model	NOUN
ajst-20193	85	15	and	and	CCONJ
ajst-20193	85	16	the	the	DET
ajst-20193	85	17	total	total	ADJ
ajst-20193	85	18	number	number	NOUN
ajst-20193	85	19	of	of	ADP
ajst-20193	85	20	samples	sample	NOUN
ajst-20193	85	21	.	.	PUNCT
ajst-20193	86	1	the	the	DET
ajst-20193	86	2	calculation	calculation	NOUN
ajst-20193	86	3	formula	formula	NOUN
ajst-20193	86	4	of	of	ADP
ajst-20193	86	5	accuracy	accuracy	NOUN
ajst-20193	86	6	rate	rate	NOUN
ajst-20193	86	7	is	be	AUX
ajst-20193	86	8	as	as	SCONJ
ajst-20193	86	9	follows	follow	VERB
ajst-20193	86	10	:	:	PUNCT
ajst-20193	86	11			NOUN
ajst-20193	86	12			SYM
ajst-20193	86	13			NOUN
ajst-20193	86	14			NUM
ajst-20193	86	15	count	count	NOUN
ajst-20193	86	16	correct	correct	ADJ
ajst-20193	86	17	acc	acc	PROPN
ajst-20193	86	18	count	count	VERB
ajst-20193	86	19	total	total	ADJ
ajst-20193	86	20			ADV
ajst-20193	86	21	in	in	ADP
ajst-20193	86	22	the	the	DET
ajst-20193	86	23	formula	formula	NOUN
ajst-20193	86	24	,	,	PUNCT
ajst-20193	86	25	count	count	NOUN
ajst-20193	86	26	(	(	PUNCT
ajst-20193	86	27	correct	correct	ADJ
ajst-20193	86	28	)	)	PUNCT
ajst-20193	86	29	is	be	AUX
ajst-20193	86	30	the	the	DET
ajst-20193	86	31	number	number	NOUN
ajst-20193	86	32	of	of	ADP
ajst-20193	86	33	correctly	correctly	ADV
ajst-20193	86	34	predicted	predict	VERB
ajst-20193	86	35	samples	sample	NOUN
ajst-20193	86	36	,	,	PUNCT
ajst-20193	86	37	and	and	CCONJ
ajst-20193	86	38	count	count	NOUN
ajst-20193	86	39	(	(	PUNCT
ajst-20193	86	40	total	total	NOUN
ajst-20193	86	41	)	)	PUNCT
ajst-20193	86	42	is	be	AUX
ajst-20193	86	43	the	the	DET
ajst-20193	86	44	number	number	NOUN
ajst-20193	86	45	of	of	ADP
ajst-20193	86	46	all	all	DET
ajst-20193	86	47	samples.in	samples.in	NUM
ajst-20193	86	48	case	case	NOUN
ajst-20193	86	49	of	of	ADP
ajst-20193	86	50	abnormal	abnormal	ADJ
ajst-20193	86	51	detection	detection	NOUN
ajst-20193	86	52	,	,	PUNCT
ajst-20193	86	53	the	the	DET
ajst-20193	86	54	formula	formula	NOUN
ajst-20193	86	55	for	for	ADP
ajst-20193	86	56	calculating	calculate	VERB
ajst-20193	86	57	the	the	DET
ajst-20193	86	58	accuracy	accuracy	NOUN
ajst-20193	86	59	rate	rate	NOUN
ajst-20193	86	60	is	be	AUX
ajst-20193	86	61	as	as	SCONJ
ajst-20193	86	62	follows	follow	VERB
ajst-20193	86	63	:	:	PUNCT
ajst-20193	86	64	tp	tp	PART
ajst-20193	86	65	tn	tn	PROPN
ajst-20193	86	66	acc	acc	PROPN
ajst-20193	86	67	tp	tp	PROPN
ajst-20193	86	68	fp	fp	PROPN
ajst-20193	86	69	fn	fn	PROPN
ajst-20193	86	70	tn	tn	PROPN
ajst-20193	87	1			PROPN
ajst-20193	87	2			PROPN
ajst-20193	87	3			PROPN
ajst-20193	87	4			VERB
ajst-20193	87	5			PUNCT
ajst-20193	87	6	accuracy	accuracy	NOUN
ajst-20193	87	7	is	be	AUX
ajst-20193	87	8	an	an	DET
ajst-20193	87	9	intuitive	intuitive	ADJ
ajst-20193	87	10	and	and	CCONJ
ajst-20193	87	11	easy	easy	ADJ
ajst-20193	87	12	to	to	PART
ajst-20193	87	13	understand	understand	VERB
ajst-20193	87	14	important	important	ADJ
ajst-20193	87	15	indicator	indicator	NOUN
ajst-20193	87	16	,	,	PUNCT
ajst-20193	87	17	which	which	PRON
ajst-20193	87	18	reflects	reflect	VERB
ajst-20193	87	19	the	the	DET
ajst-20193	87	20	number	number	NOUN
ajst-20193	87	21	of	of	ADP
ajst-20193	87	22	samples	sample	NOUN
ajst-20193	87	23	that	that	PRON
ajst-20193	87	24	the	the	DET
ajst-20193	87	25	model	model	NOUN
ajst-20193	87	26	can	can	AUX
ajst-20193	87	27	accurately	accurately	ADV
ajst-20193	87	28	predict	predict	VERB
ajst-20193	87	29	in	in	ADP
ajst-20193	87	30	the	the	DET
ajst-20193	87	31	classification	classification	NOUN
ajst-20193	87	32	process	process	NOUN
ajst-20193	87	33	.	.	PUNCT
ajst-20193	88	1	however	however	ADV
ajst-20193	88	2	,	,	PUNCT
ajst-20193	88	3	when	when	SCONJ
ajst-20193	88	4	the	the	DET
ajst-20193	88	5	class	class	NOUN
ajst-20193	88	6	distribution	distribution	NOUN
ajst-20193	88	7	of	of	ADP
ajst-20193	88	8	the	the	DET
ajst-20193	88	9	dataset	dataset	NOUN
ajst-20193	88	10	is	be	AUX
ajst-20193	88	11	uneven	uneven	ADJ
ajst-20193	88	12	,	,	PUNCT
ajst-20193	88	13	the	the	DET
ajst-20193	88	14	model	model	NOUN
ajst-20193	88	15	only	only	ADV
ajst-20193	88	16	needs	need	VERB
ajst-20193	88	17	to	to	PART
ajst-20193	88	18	predict	predict	VERB
ajst-20193	88	19	most	most	ADJ
ajst-20193	88	20	class	class	NOUN
ajst-20193	88	21	samples	sample	NOUN
ajst-20193	88	22	to	to	PART
ajst-20193	88	23	achieve	achieve	VERB
ajst-20193	88	24	high	high	ADJ
ajst-20193	88	25	accuracy	accuracy	NOUN
ajst-20193	88	26	.	.	PUNCT
ajst-20193	89	1	therefore	therefore	ADV
ajst-20193	89	2	,	,	PUNCT
ajst-20193	89	3	in	in	ADP
ajst-20193	89	4	the	the	DET
ajst-20193	89	5	case	case	NOUN
ajst-20193	89	6	of	of	ADP
ajst-20193	89	7	unbalanced	unbalanced	ADJ
ajst-20193	89	8	sample	sample	NOUN
ajst-20193	89	9	categories	category	NOUN
ajst-20193	89	10	,	,	PUNCT
ajst-20193	89	11	it	it	PRON
ajst-20193	89	12	is	be	AUX
ajst-20193	89	13	misleading	misleading	ADJ
ajst-20193	89	14	to	to	PART
ajst-20193	89	15	rely	rely	VERB
ajst-20193	89	16	solely	solely	ADV
ajst-20193	89	17	on	on	ADP
ajst-20193	89	18	the	the	DET
ajst-20193	89	19	accuracy	accuracy	NOUN
ajst-20193	89	20	rate	rate	NOUN
ajst-20193	89	21	to	to	PART
ajst-20193	89	22	evaluate	evaluate	VERB
ajst-20193	89	23	the	the	DET
ajst-20193	89	24	model	model	NOUN
ajst-20193	89	25	.	.	PUNCT
ajst-20193	90	1	and	and	CCONJ
ajst-20193	90	2	the	the	DET
ajst-20193	90	3	accuracy	accuracy	NOUN
ajst-20193	90	4	rate	rate	NOUN
ajst-20193	90	5	only	only	ADV
ajst-20193	90	6	provides	provide	VERB
ajst-20193	90	7	an	an	DET
ajst-20193	90	8	overall	overall	ADJ
ajst-20193	90	9	indicator	indicator	NOUN
ajst-20193	90	10	,	,	PUNCT
ajst-20193	90	11	and	and	CCONJ
ajst-20193	90	12	it	it	PRON
ajst-20193	90	13	is	be	AUX
ajst-20193	90	14	impossible	impossible	ADJ
ajst-20193	90	15	to	to	PART
ajst-20193	90	16	know	know	VERB
ajst-20193	90	17	the	the	DET
ajst-20193	90	18	94	94	NUM
ajst-20193	90	19	classification	classification	NOUN
ajst-20193	90	20	performance	performance	NOUN
ajst-20193	90	21	of	of	ADP
ajst-20193	90	22	each	each	DET
ajst-20193	90	23	model	model	NOUN
ajst-20193	90	24	in	in	ADP
ajst-20193	90	25	each	each	DET
ajst-20193	90	26	category	category	NOUN
ajst-20193	90	27	.	.	PUNCT
ajst-20193	91	1	(	(	PUNCT
ajst-20193	91	2	2	2	X
ajst-20193	91	3	)	)	PUNCT
ajst-20193	91	4	precision	precision	NOUN
ajst-20193	91	5	the	the	DET
ajst-20193	91	6	accuracy	accuracy	NOUN
ajst-20193	91	7	rate	rate	NOUN
ajst-20193	91	8	measures	measure	VERB
ajst-20193	91	9	the	the	DET
ajst-20193	91	10	proportion	proportion	NOUN
ajst-20193	91	11	of	of	ADP
ajst-20193	91	12	all	all	DET
ajst-20193	91	13	samples	sample	NOUN
ajst-20193	91	14	predicted	predict	VERB
ajst-20193	91	15	by	by	ADP
ajst-20193	91	16	the	the	DET
ajst-20193	91	17	model	model	NOUN
ajst-20193	91	18	to	to	PART
ajst-20193	91	19	be	be	AUX
ajst-20193	91	20	positive	positive	ADJ
ajst-20193	91	21	,	,	PUNCT
ajst-20193	91	22	and	and	CCONJ
ajst-20193	91	23	the	the	DET
ajst-20193	91	24	calculation	calculation	NOUN
ajst-20193	91	25	formula	formula	NOUN
ajst-20193	91	26	is	be	AUX
ajst-20193	91	27	as	as	SCONJ
ajst-20193	91	28	follows	follow	VERB
ajst-20193	91	29	.	.	PUNCT
ajst-20193	92	1	pr	pr	PROPN
ajst-20193	92	2	tp	tp	ADP
ajst-20193	92	3	tn	tn	PROPN
ajst-20193	92	4	ecision	ecision	PROPN
ajst-20193	92	5	tp	tp	ADP
ajst-20193	92	6	fp	fp	PROPN
ajst-20193	92	7	fn	fn	PROPN
ajst-20193	92	8	tn	tn	PROPN
ajst-20193	92	9			PROPN
ajst-20193	92	10			PROPN
ajst-20193	92	11			PROPN
ajst-20193	92	12			PUNCT
ajst-20193	92	13			X
ajst-20193	92	14	(	(	PUNCT
ajst-20193	92	15	3	3	X
ajst-20193	92	16	)	)	PUNCT
ajst-20193	92	17	recall	recall	NOUN
ajst-20193	92	18	in	in	ADP
ajst-20193	92	19	this	this	DET
ajst-20193	92	20	study	study	NOUN
ajst-20193	92	21	,	,	PUNCT
ajst-20193	92	22	recall	recall	PROPN
ajst-20193	92	23	refers	refer	VERB
ajst-20193	92	24	to	to	ADP
ajst-20193	92	25	the	the	DET
ajst-20193	92	26	probability	probability	NOUN
ajst-20193	92	27	that	that	SCONJ
ajst-20193	92	28	samples	sample	NOUN
ajst-20193	92	29	labeled	label	VERB
ajst-20193	92	30	as	as	ADP
ajst-20193	92	31	normal	normal	ADJ
ajst-20193	92	32	data	datum	NOUN
ajst-20193	92	33	are	be	AUX
ajst-20193	92	34	actually	actually	ADV
ajst-20193	92	35	recognized	recognize	VERB
ajst-20193	92	36	as	as	ADP
ajst-20193	92	37	normal	normal	ADJ
ajst-20193	92	38	data	datum	NOUN
ajst-20193	92	39	.	.	PUNCT
ajst-20193	93	1	the	the	DET
ajst-20193	93	2	calculation	calculation	NOUN
ajst-20193	93	3	formula	formula	NOUN
ajst-20193	93	4	is	be	AUX
ajst-20193	93	5	shown	show	VERB
ajst-20193	93	6	in	in	ADP
ajst-20193	93	7	the	the	DET
ajst-20193	93	8	following	follow	VERB
ajst-20193	93	9	formula	formula	NOUN
ajst-20193	93	10	.	.	PUNCT
ajst-20193	94	1	re	re	ADP
ajst-20193	94	2	tp	tp	PART
ajst-20193	94	3	call	call	VERB
ajst-20193	94	4	tp	tp	ADP
ajst-20193	94	5	fn	fn	PROPN
ajst-20193	94	6			PROPN
ajst-20193	94	7			PUNCT
ajst-20193	94	8	by	by	ADP
ajst-20193	94	9	evaluating	evaluate	VERB
ajst-20193	94	10	the	the	DET
ajst-20193	94	11	recall	recall	NOUN
ajst-20193	94	12	rate	rate	NOUN
ajst-20193	94	13	,	,	PUNCT
ajst-20193	94	14	this	this	DET
ajst-20193	94	15	paper	paper	NOUN
ajst-20193	94	16	can	can	AUX
ajst-20193	94	17	accurately	accurately	ADV
ajst-20193	94	18	understand	understand	VERB
ajst-20193	94	19	how	how	SCONJ
ajst-20193	94	20	many	many	ADJ
ajst-20193	94	21	normal	normal	ADJ
ajst-20193	94	22	data	datum	NOUN
ajst-20193	94	23	are	be	AUX
ajst-20193	94	24	successfully	successfully	ADV
ajst-20193	94	25	predicted	predict	VERB
ajst-20193	94	26	.	.	PUNCT
ajst-20193	95	1	in	in	ADP
ajst-20193	95	2	the	the	DET
ajst-20193	95	3	field	field	NOUN
ajst-20193	95	4	of	of	ADP
ajst-20193	95	5	landslide	landslide	NOUN
ajst-20193	95	6	disasters	disaster	NOUN
ajst-20193	95	7	,	,	PUNCT
ajst-20193	95	8	normal	normal	ADJ
ajst-20193	95	9	data	datum	NOUN
ajst-20193	95	10	can	can	AUX
ajst-20193	95	11	not	not	PART
ajst-20193	95	12	be	be	AUX
ajst-20193	95	13	generally	generally	ADV
ajst-20193	95	14	identified	identify	VERB
ajst-20193	95	15	as	as	ADP
ajst-20193	95	16	abnormal	abnormal	ADJ
ajst-20193	95	17	data	datum	NOUN
ajst-20193	95	18	,	,	PUNCT
ajst-20193	95	19	so	so	SCONJ
ajst-20193	95	20	this	this	DET
ajst-20193	95	21	paper	paper	NOUN
ajst-20193	95	22	will	will	AUX
ajst-20193	95	23	try	try	VERB
ajst-20193	95	24	to	to	PART
ajst-20193	95	25	improve	improve	VERB
ajst-20193	95	26	the	the	DET
ajst-20193	95	27	recall	recall	NOUN
ajst-20193	95	28	rate	rate	NOUN
ajst-20193	95	29	index	index	NOUN
ajst-20193	95	30	in	in	ADP
ajst-20193	95	31	the	the	DET
ajst-20193	95	32	experiment	experiment	NOUN
ajst-20193	95	33	.	.	PUNCT
ajst-20193	96	1	(	(	PUNCT
ajst-20193	96	2	4	4	X
ajst-20193	96	3	)	)	PUNCT
ajst-20193	96	4	f1	f1	NOUN
ajst-20193	96	5	f1	f1	NOUN
ajst-20193	96	6	is	be	AUX
ajst-20193	96	7	the	the	DET
ajst-20193	96	8	comprehensive	comprehensive	ADJ
ajst-20193	96	9	value	value	NOUN
ajst-20193	96	10	of	of	ADP
ajst-20193	96	11	precision	precision	NOUN
ajst-20193	96	12	and	and	CCONJ
ajst-20193	96	13	recall	recall	NOUN
ajst-20193	96	14	,	,	PUNCT
ajst-20193	96	15	which	which	PRON
ajst-20193	96	16	avoids	avoid	VERB
ajst-20193	96	17	the	the	DET
ajst-20193	96	18	problems	problem	NOUN
ajst-20193	96	19	that	that	PRON
ajst-20193	96	20	may	may	AUX
ajst-20193	96	21	be	be	AUX
ajst-20193	96	22	caused	cause	VERB
ajst-20193	96	23	by	by	ADP
ajst-20193	96	24	focusing	focus	VERB
ajst-20193	96	25	only	only	ADV
ajst-20193	96	26	on	on	ADP
ajst-20193	96	27	one	one	NUM
ajst-20193	96	28	of	of	ADP
ajst-20193	96	29	the	the	DET
ajst-20193	96	30	indicators	indicator	NOUN
ajst-20193	96	31	.	.	PUNCT
ajst-20193	97	1	the	the	DET
ajst-20193	97	2	maximum	maximum	ADJ
ajst-20193	97	3	value	value	NOUN
ajst-20193	97	4	of	of	ADP
ajst-20193	97	5	f1	f1	NOUN
ajst-20193	97	6	is	be	AUX
ajst-20193	97	7	1	1	NUM
ajst-20193	97	8	,	,	PUNCT
ajst-20193	97	9	and	and	CCONJ
ajst-20193	97	10	the	the	DET
ajst-20193	97	11	minimum	minimum	ADJ
ajst-20193	97	12	value	value	NOUN
ajst-20193	97	13	is	be	AUX
ajst-20193	97	14	0	0	NUM
ajst-20193	97	15	.	.	PUNCT
ajst-20193	98	1	the	the	PRON
ajst-20193	98	2	higher	high	ADJ
ajst-20193	98	3	the	the	DET
ajst-20193	98	4	f1	f1	PROPN
ajst-20193	98	5	value	value	NOUN
ajst-20193	98	6	,	,	PUNCT
ajst-20193	98	7	the	the	PRON
ajst-20193	98	8	better	well	ADJ
ajst-20193	98	9	the	the	DET
ajst-20193	98	10	performance	performance	NOUN
ajst-20193	98	11	of	of	ADP
ajst-20193	98	12	the	the	DET
ajst-20193	98	13	model	model	NOUN
ajst-20193	98	14	in	in	ADP
ajst-20193	98	15	terms	term	NOUN
ajst-20193	98	16	of	of	ADP
ajst-20193	98	17	accuracy	accuracy	NOUN
ajst-20193	98	18	and	and	CCONJ
ajst-20193	98	19	recall	recall	NOUN
ajst-20193	98	20	.	.	PUNCT
ajst-20193	99	1	the	the	DET
ajst-20193	99	2	specific	specific	ADJ
ajst-20193	99	3	formula	formula	NOUN
ajst-20193	99	4	for	for	ADP
ajst-20193	99	5	calculating	calculate	VERB
ajst-20193	99	6	the	the	DET
ajst-20193	99	7	f1	f1	NOUN
ajst-20193	99	8	value	value	NOUN
ajst-20193	99	9	is	be	AUX
ajst-20193	99	10	shown	show	VERB
ajst-20193	99	11	in	in	ADP
ajst-20193	99	12	the	the	DET
ajst-20193	99	13	following	follow	VERB
ajst-20193	99	14	formula	formula	NOUN
ajst-20193	99	15	.	.	PUNCT
ajst-20193	100	1	2	2	NUM
ajst-20193	100	2	1	1	NUM
ajst-20193	100	3	2	2	NUM
ajst-20193	100	4	tp	tp	ADP
ajst-20193	100	5	f	f	PROPN
ajst-20193	100	6	tp	tp	PROPN
ajst-20193	100	7	fn	fn	PROPN
ajst-20193	100	8	fp	fp	PROPN
ajst-20193	100	9			PROPN
ajst-20193	100	10			PROPN
ajst-20193	100	11			PROPN
ajst-20193	100	12			ADV
ajst-20193	100	13			VERB
ajst-20193	100	14	4.3	4.3	NUM
ajst-20193	100	15	.	.	PUNCT
ajst-20193	101	1	experimental	experimental	ADJ
ajst-20193	101	2	result	result	NOUN
ajst-20193	101	3	tcn	tcn	PROPN
ajst-20193	101	4	transformer	transformer	NOUN
ajst-20193	101	5	model	model	NOUN
ajst-20193	101	6	is	be	AUX
ajst-20193	101	7	used	use	VERB
ajst-20193	101	8	to	to	PART
ajst-20193	101	9	test	test	VERB
ajst-20193	101	10	on	on	ADP
ajst-20193	101	11	gnss	gnss	NOUN
ajst-20193	101	12	and	and	CCONJ
ajst-20193	101	13	fracture	fracture	NOUN
ajst-20193	101	14	data	datum	NOUN
ajst-20193	101	15	sets	set	NOUN
ajst-20193	101	16	respectively	respectively	ADV
ajst-20193	101	17	.	.	PUNCT
ajst-20193	102	1	the	the	DET
ajst-20193	102	2	actual	actual	ADJ
ajst-20193	102	3	anomaly	anomaly	NOUN
ajst-20193	102	4	detection	detection	NOUN
ajst-20193	102	5	effect	effect	NOUN
ajst-20193	102	6	is	be	AUX
ajst-20193	102	7	shown	show	VERB
ajst-20193	102	8	in	in	ADP
ajst-20193	102	9	table	table	NOUN
ajst-20193	102	10	2	2	NUM
ajst-20193	102	11	.	.	PUNCT
ajst-20193	103	1	it	it	PRON
ajst-20193	103	2	can	can	AUX
ajst-20193	103	3	be	be	AUX
ajst-20193	103	4	clearly	clearly	ADV
ajst-20193	103	5	seen	see	VERB
ajst-20193	103	6	that	that	SCONJ
ajst-20193	103	7	the	the	DET
ajst-20193	103	8	model	model	NOUN
ajst-20193	103	9	has	have	VERB
ajst-20193	103	10	a	a	DET
ajst-20193	103	11	strong	strong	ADJ
ajst-20193	103	12	ability	ability	NOUN
ajst-20193	103	13	to	to	PART
ajst-20193	103	14	identify	identify	VERB
ajst-20193	103	15	the	the	DET
ajst-20193	103	16	abnormal	abnormal	ADJ
ajst-20193	103	17	data	datum	NOUN
ajst-20193	103	18	in	in	ADP
ajst-20193	103	19	the	the	DET
ajst-20193	103	20	landslide	landslide	NOUN
ajst-20193	103	21	displacement	displacement	NOUN
ajst-20193	103	22	monitoring	monitoring	NOUN
ajst-20193	103	23	data	datum	NOUN
ajst-20193	103	24	,	,	PUNCT
ajst-20193	103	25	and	and	CCONJ
ajst-20193	103	26	can	can	AUX
ajst-20193	103	27	maintain	maintain	VERB
ajst-20193	103	28	90	90	NUM
ajst-20193	103	29	%	%	NOUN
ajst-20193	103	30	accuracy	accuracy	NOUN
ajst-20193	103	31	while	while	SCONJ
ajst-20193	103	32	ensuring	ensure	VERB
ajst-20193	103	33	a	a	DET
ajst-20193	103	34	high	high	ADJ
ajst-20193	103	35	recall	recall	NOUN
ajst-20193	103	36	rate	rate	NOUN
ajst-20193	103	37	.	.	PUNCT
ajst-20193	104	1	table	table	NOUN
ajst-20193	104	2	2	2	NUM
ajst-20193	104	3	.	.	PUNCT
ajst-20193	104	4	identification	identification	NOUN
ajst-20193	104	5	effect	effect	NOUN
ajst-20193	104	6	of	of	ADP
ajst-20193	104	7	abnormal	abnormal	ADJ
ajst-20193	104	8	data	datum	NOUN
ajst-20193	104	9	data	data	NOUN
ajst-20193	104	10	type	type	NOUN
ajst-20193	104	11	accuracy	accuracy	NOUN
ajst-20193	104	12	precision	precision	NOUN
ajst-20193	104	13	recall	recall	NOUN
ajst-20193	104	14	f1	f1	PROPN
ajst-20193	104	15	gnss	gnss	NOUN
ajst-20193	104	16	data	datum	NOUN
ajst-20193	104	17	89.08	89.08	NUM
ajst-20193	104	18	%	%	NOUN
ajst-20193	104	19	81.18	81.18	NUM
ajst-20193	104	20	%	%	NOUN
ajst-20193	104	21	97.08	97.08	NUM
ajst-20193	104	22	%	%	NOUN
ajst-20193	104	23	0.8824	0.8824	NUM
ajst-20193	104	24	fracture	fracture	NOUN
ajst-20193	104	25	data	datum	NOUN
ajst-20193	104	26	88.72	88.72	NUM
ajst-20193	104	27	%	%	NOUN
ajst-20193	104	28	77.84	77.84	NUM
ajst-20193	104	29	%	%	NOUN
ajst-20193	104	30	96.21	96.21	NUM
ajst-20193	104	31	%	%	NOUN
ajst-20193	104	32	0.8585	0.8585	NUM
ajst-20193	104	33	5	5	NUM
ajst-20193	104	34	.	.	PUNCT
ajst-20193	105	1	conclusion	conclusion	NOUN
ajst-20193	105	2	this	this	DET
ajst-20193	105	3	paper	paper	NOUN
ajst-20193	105	4	discusses	discuss	VERB
ajst-20193	105	5	the	the	DET
ajst-20193	105	6	application	application	NOUN
ajst-20193	105	7	of	of	ADP
ajst-20193	105	8	tcn	tcn	PROPN
ajst-20193	105	9	attention	attention	NOUN
ajst-20193	105	10	model	model	NOUN
ajst-20193	105	11	based	base	VERB
ajst-20193	105	12	on	on	ADP
ajst-20193	105	13	tcn	tcn	NOUN
ajst-20193	105	14	and	and	CCONJ
ajst-20193	105	15	attention	attention	NOUN
ajst-20193	105	16	in	in	ADP
ajst-20193	105	17	the	the	DET
ajst-20193	105	18	classification	classification	NOUN
ajst-20193	105	19	and	and	CCONJ
ajst-20193	105	20	early	early	ADJ
ajst-20193	105	21	warning	warning	NOUN
ajst-20193	105	22	of	of	ADP
ajst-20193	105	23	landslide	landslide	NOUN
ajst-20193	105	24	displacement	displacement	NOUN
ajst-20193	105	25	monitoring	monitoring	NOUN
ajst-20193	105	26	data	datum	NOUN
ajst-20193	105	27	.	.	PUNCT
ajst-20193	106	1	through	through	ADP
ajst-20193	106	2	the	the	DET
ajst-20193	106	3	construction	construction	NOUN
ajst-20193	106	4	and	and	CCONJ
ajst-20193	106	5	experimental	experimental	ADJ
ajst-20193	106	6	verification	verification	NOUN
ajst-20193	106	7	of	of	ADP
ajst-20193	106	8	the	the	DET
ajst-20193	106	9	model	model	NOUN
ajst-20193	106	10	,	,	PUNCT
ajst-20193	106	11	the	the	DET
ajst-20193	106	12	following	follow	VERB
ajst-20193	106	13	conclusions	conclusion	NOUN
ajst-20193	106	14	are	be	AUX
ajst-20193	106	15	drawn	draw	VERB
ajst-20193	106	16	:	:	PUNCT
ajst-20193	106	17	tcn	tcn	NOUN
ajst-20193	106	18	attention	attention	NOUN
ajst-20193	106	19	model	model	NOUN
ajst-20193	106	20	shows	show	VERB
ajst-20193	106	21	significant	significant	ADJ
ajst-20193	106	22	application	application	NOUN
ajst-20193	106	23	value	value	NOUN
ajst-20193	106	24	in	in	ADP
ajst-20193	106	25	the	the	DET
ajst-20193	106	26	classification	classification	NOUN
ajst-20193	106	27	and	and	CCONJ
ajst-20193	106	28	early	early	ADJ
ajst-20193	106	29	warning	warning	NOUN
ajst-20193	106	30	of	of	ADP
ajst-20193	106	31	landslide	landslide	NOUN
ajst-20193	106	32	displacement	displacement	NOUN
ajst-20193	106	33	monitoring	monitoring	NOUN
ajst-20193	106	34	data	datum	NOUN
ajst-20193	106	35	.	.	PUNCT
ajst-20193	107	1	by	by	ADP
ajst-20193	107	2	combining	combine	VERB
ajst-20193	107	3	tcn	tcn	NOUN
ajst-20193	107	4	and	and	CCONJ
ajst-20193	107	5	attention	attention	NOUN
ajst-20193	107	6	mechanism	mechanism	NOUN
ajst-20193	107	7	,	,	PUNCT
ajst-20193	107	8	the	the	DET
ajst-20193	107	9	model	model	NOUN
ajst-20193	107	10	can	can	AUX
ajst-20193	107	11	effectively	effectively	ADV
ajst-20193	107	12	extract	extract	VERB
ajst-20193	107	13	key	key	ADJ
ajst-20193	107	14	features	feature	NOUN
ajst-20193	107	15	in	in	ADP
ajst-20193	107	16	time	time	NOUN
ajst-20193	107	17	series	series	PROPN
ajst-20193	107	18	data	data	PROPN
ajst-20193	107	19	,	,	PUNCT
ajst-20193	107	20	and	and	CCONJ
ajst-20193	107	21	strengthen	strengthen	VERB
ajst-20193	107	22	the	the	DET
ajst-20193	107	23	attention	attention	NOUN
ajst-20193	107	24	to	to	ADP
ajst-20193	107	25	important	important	ADJ
ajst-20193	107	26	time	time	NOUN
ajst-20193	107	27	points	point	NOUN
ajst-20193	107	28	.	.	PUNCT
ajst-20193	108	1	the	the	DET
ajst-20193	108	2	experimental	experimental	ADJ
ajst-20193	108	3	results	result	NOUN
ajst-20193	108	4	show	show	VERB
ajst-20193	108	5	that	that	SCONJ
ajst-20193	108	6	the	the	DET
ajst-20193	108	7	tcn	tcn	NOUN
ajst-20193	108	8	attention	attention	NOUN
ajst-20193	108	9	model	model	NOUN
ajst-20193	108	10	has	have	VERB
ajst-20193	108	11	high	high	ADJ
ajst-20193	108	12	accuracy	accuracy	NOUN
ajst-20193	108	13	and	and	CCONJ
ajst-20193	108	14	real	real	ADJ
ajst-20193	108	15	-	-	PUNCT
ajst-20193	108	16	time	time	NOUN
ajst-20193	108	17	performance	performance	NOUN
ajst-20193	108	18	in	in	ADP
ajst-20193	108	19	the	the	DET
ajst-20193	108	20	abnormal	abnormal	ADJ
ajst-20193	108	21	identification	identification	NOUN
ajst-20193	108	22	task	task	NOUN
ajst-20193	108	23	of	of	ADP
ajst-20193	108	24	landslide	landslide	NOUN
ajst-20193	108	25	displacement	displacement	NOUN
ajst-20193	108	26	monitoring	monitoring	NOUN
ajst-20193	108	27	data	datum	NOUN
ajst-20193	108	28	,	,	PUNCT
ajst-20193	108	29	which	which	PRON
ajst-20193	108	30	provides	provide	VERB
ajst-20193	108	31	strong	strong	ADJ
ajst-20193	108	32	support	support	NOUN
ajst-20193	108	33	for	for	ADP
ajst-20193	108	34	the	the	DET
ajst-20193	108	35	early	early	ADJ
ajst-20193	108	36	warning	warning	NOUN
ajst-20193	108	37	and	and	CCONJ
ajst-20193	108	38	prevention	prevention	NOUN
ajst-20193	108	39	of	of	ADP
ajst-20193	108	40	landslide	landslide	NOUN
ajst-20193	108	41	disasters	disaster	NOUN
ajst-20193	108	42	.	.	PUNCT
ajst-20193	109	1	although	although	SCONJ
ajst-20193	109	2	the	the	DET
ajst-20193	109	3	tcn	tcn	NOUN
ajst-20193	109	4	attention	attention	NOUN
ajst-20193	109	5	model	model	NOUN
ajst-20193	109	6	has	have	AUX
ajst-20193	109	7	achieved	achieve	VERB
ajst-20193	109	8	good	good	ADJ
ajst-20193	109	9	results	result	NOUN
ajst-20193	109	10	in	in	ADP
ajst-20193	109	11	the	the	DET
ajst-20193	109	12	current	current	ADJ
ajst-20193	109	13	research	research	NOUN
ajst-20193	109	14	,	,	PUNCT
ajst-20193	109	15	there	there	PRON
ajst-20193	109	16	is	be	VERB
ajst-20193	109	17	still	still	ADV
ajst-20193	109	18	room	room	NOUN
ajst-20193	109	19	for	for	ADP
ajst-20193	109	20	further	further	ADJ
ajst-20193	109	21	research	research	NOUN
ajst-20193	109	22	.	.	PUNCT
ajst-20193	110	1	future	future	ADJ
ajst-20193	110	2	research	research	NOUN
ajst-20193	110	3	directions	direction	NOUN
ajst-20193	110	4	can	can	AUX
ajst-20193	110	5	be	be	AUX
ajst-20193	110	6	carried	carry	VERB
ajst-20193	110	7	out	out	ADP
ajst-20193	110	8	from	from	ADP
ajst-20193	110	9	the	the	DET
ajst-20193	110	10	following	follow	VERB
ajst-20193	110	11	two	two	NUM
ajst-20193	110	12	aspects	aspect	NOUN
ajst-20193	110	13	:	:	PUNCT
ajst-20193	110	14	first	first	ADJ
ajst-20193	110	15	,	,	PUNCT
ajst-20193	110	16	further	further	ADJ
ajst-20193	110	17	optimization	optimization	NOUN
ajst-20193	110	18	of	of	ADP
ajst-20193	110	19	the	the	DET
ajst-20193	110	20	model	model	NOUN
ajst-20193	110	21	.	.	PUNCT
ajst-20193	111	1	the	the	DET
ajst-20193	111	2	performance	performance	NOUN
ajst-20193	111	3	of	of	ADP
ajst-20193	111	4	the	the	DET
ajst-20193	111	5	model	model	NOUN
ajst-20193	111	6	in	in	ADP
ajst-20193	111	7	landslide	landslide	NOUN
ajst-20193	111	8	displacement	displacement	NOUN
ajst-20193	111	9	monitoring	monitoring	NOUN
ajst-20193	111	10	data	datum	NOUN
ajst-20193	111	11	classification	classification	NOUN
ajst-20193	111	12	and	and	CCONJ
ajst-20193	111	13	early	early	ADJ
ajst-20193	111	14	warning	warning	NOUN
ajst-20193	111	15	can	can	AUX
ajst-20193	111	16	be	be	AUX
ajst-20193	111	17	further	far	ADV
ajst-20193	111	18	improved	improve	VERB
ajst-20193	111	19	by	by	ADP
ajst-20193	111	20	adjusting	adjust	VERB
ajst-20193	111	21	the	the	DET
ajst-20193	111	22	structure	structure	NOUN
ajst-20193	111	23	and	and	CCONJ
ajst-20193	111	24	parameters	parameter	NOUN
ajst-20193	111	25	of	of	ADP
ajst-20193	111	26	the	the	DET
ajst-20193	111	27	model	model	NOUN
ajst-20193	111	28	,	,	PUNCT
ajst-20193	111	29	such	such	ADJ
ajst-20193	111	30	as	as	ADP
ajst-20193	111	31	increasing	increase	VERB
ajst-20193	111	32	the	the	DET
ajst-20193	111	33	depth	depth	NOUN
ajst-20193	111	34	of	of	ADP
ajst-20193	111	35	the	the	DET
ajst-20193	111	36	alluvium	alluvium	NOUN
ajst-20193	111	37	,	,	PUNCT
ajst-20193	111	38	expanding	expand	VERB
ajst-20193	111	39	the	the	DET
ajst-20193	111	40	receptive	receptive	ADJ
ajst-20193	111	41	field	field	NOUN
ajst-20193	111	42	,	,	PUNCT
ajst-20193	111	43	etc	etc	X
ajst-20193	111	44	.	.	X
ajst-20193	112	1	in	in	ADP
ajst-20193	112	2	addition	addition	NOUN
ajst-20193	112	3	,	,	PUNCT
ajst-20193	112	4	other	other	ADJ
ajst-20193	112	5	advanced	advanced	ADJ
ajst-20193	112	6	technologies	technology	NOUN
ajst-20193	112	7	,	,	PUNCT
ajst-20193	112	8	such	such	ADJ
ajst-20193	112	9	as	as	ADP
ajst-20193	112	10	transfer	transfer	NOUN
ajst-20193	112	11	learning	learning	NOUN
ajst-20193	112	12	and	and	CCONJ
ajst-20193	112	13	reinforcement	reinforcement	NOUN
ajst-20193	112	14	learning	learning	NOUN
ajst-20193	112	15	,	,	PUNCT
ajst-20193	112	16	can	can	AUX
ajst-20193	112	17	be	be	AUX
ajst-20193	112	18	explored	explore	VERB
ajst-20193	112	19	to	to	PART
ajst-20193	112	20	enhance	enhance	VERB
ajst-20193	112	21	the	the	DET
ajst-20193	112	22	generalization	generalization	NOUN
ajst-20193	112	23	ability	ability	NOUN
ajst-20193	112	24	and	and	CCONJ
ajst-20193	112	25	adaptability	adaptability	NOUN
ajst-20193	112	26	of	of	ADP
ajst-20193	112	27	the	the	DET
ajst-20193	112	28	model	model	NOUN
ajst-20193	112	29	.	.	PUNCT
ajst-20193	113	1	the	the	DET
ajst-20193	113	2	second	second	ADJ
ajst-20193	113	3	is	be	AUX
ajst-20193	113	4	the	the	DET
ajst-20193	113	5	application	application	NOUN
ajst-20193	113	6	of	of	ADP
ajst-20193	113	7	the	the	DET
ajst-20193	113	8	model	model	NOUN
ajst-20193	113	9	in	in	ADP
ajst-20193	113	10	a	a	DET
ajst-20193	113	11	wider	wide	ADJ
ajst-20193	113	12	range	range	NOUN
ajst-20193	113	13	of	of	ADP
ajst-20193	113	14	scenarios	scenario	NOUN
ajst-20193	113	15	.	.	PUNCT
ajst-20193	114	1	the	the	DET
ajst-20193	114	2	application	application	NOUN
ajst-20193	114	3	of	of	ADP
ajst-20193	114	4	tcn	tcn	NOUN
ajst-20193	114	5	attention	attention	NOUN
ajst-20193	114	6	model	model	NOUN
ajst-20193	114	7	in	in	ADP
ajst-20193	114	8	the	the	DET
ajst-20193	114	9	classification	classification	NOUN
ajst-20193	114	10	and	and	CCONJ
ajst-20193	114	11	early	early	ADJ
ajst-20193	114	12	warning	warning	NOUN
ajst-20193	114	13	of	of	ADP
ajst-20193	114	14	landslide	landslide	NOUN
ajst-20193	114	15	displacement	displacement	NOUN
ajst-20193	114	16	monitoring	monitoring	NOUN
ajst-20193	114	17	data	datum	NOUN
ajst-20193	114	18	is	be	AUX
ajst-20193	114	19	only	only	ADV
ajst-20193	114	20	the	the	DET
ajst-20193	114	21	starting	starting	NOUN
ajst-20193	114	22	point	point	NOUN
ajst-20193	114	23	,	,	PUNCT
ajst-20193	114	24	and	and	CCONJ
ajst-20193	114	25	it	it	PRON
ajst-20193	114	26	can	can	AUX
ajst-20193	114	27	be	be	AUX
ajst-20193	114	28	applied	apply	VERB
ajst-20193	114	29	to	to	ADP
ajst-20193	114	30	a	a	DET
ajst-20193	114	31	wider	wide	ADJ
ajst-20193	114	32	range	range	NOUN
ajst-20193	114	33	of	of	ADP
ajst-20193	114	34	scenarios	scenario	NOUN
ajst-20193	114	35	in	in	ADP
ajst-20193	114	36	the	the	DET
ajst-20193	114	37	future	future	NOUN
ajst-20193	114	38	,	,	PUNCT
ajst-20193	114	39	such	such	ADJ
ajst-20193	114	40	as	as	ADP
ajst-20193	114	41	other	other	ADJ
ajst-20193	114	42	geological	geological	ADJ
ajst-20193	114	43	disaster	disaster	NOUN
ajst-20193	114	44	early	early	ADJ
ajst-20193	114	45	warning	warning	NOUN
ajst-20193	114	46	,	,	PUNCT
ajst-20193	114	47	environmental	environmental	ADJ
ajst-20193	114	48	monitoring	monitoring	NOUN
ajst-20193	114	49	,	,	PUNCT
ajst-20193	114	50	financial	financial	ADJ
ajst-20193	114	51	risk	risk	NOUN
ajst-20193	114	52	control	control	NOUN
ajst-20193	114	53	and	and	CCONJ
ajst-20193	114	54	other	other	ADJ
ajst-20193	114	55	fields	field	NOUN
ajst-20193	114	56	.	.	PUNCT
ajst-20193	115	1	by	by	ADP
ajst-20193	115	2	expanding	expand	VERB
ajst-20193	115	3	the	the	DET
ajst-20193	115	4	application	application	NOUN
ajst-20193	115	5	scope	scope	NOUN
ajst-20193	115	6	of	of	ADP
ajst-20193	115	7	the	the	DET
ajst-20193	115	8	model	model	NOUN
ajst-20193	115	9	,	,	PUNCT
ajst-20193	115	10	the	the	DET
ajst-20193	115	11	practicability	practicability	NOUN
ajst-20193	115	12	and	and	CCONJ
ajst-20193	115	13	universality	universality	NOUN
ajst-20193	115	14	of	of	ADP
ajst-20193	115	15	tcn	tcn	NOUN
ajst-20193	115	16	attention	attention	NOUN
ajst-20193	115	17	model	model	NOUN
ajst-20193	115	18	can	can	AUX
ajst-20193	115	19	be	be	AUX
ajst-20193	115	20	further	far	ADV
ajst-20193	115	21	verified	verify	VERB
ajst-20193	115	22	and	and	CCONJ
ajst-20193	115	23	improved	improve	VERB
ajst-20193	115	24	.	.	PUNCT
ajst-20193	116	1	in	in	ADP
ajst-20193	116	2	a	a	DET
ajst-20193	116	3	word	word	NOUN
ajst-20193	116	4	,	,	PUNCT
ajst-20193	116	5	this	this	DET
ajst-20193	116	6	study	study	NOUN
ajst-20193	116	7	is	be	AUX
ajst-20193	116	8	based	base	VERB
ajst-20193	116	9	on	on	ADP
ajst-20193	116	10	the	the	DET
ajst-20193	116	11	application	application	NOUN
ajst-20193	116	12	of	of	ADP
ajst-20193	116	13	tcn	tcn	NOUN
ajst-20193	116	14	attention	attention	NOUN
ajst-20193	116	15	model	model	NOUN
ajst-20193	116	16	in	in	ADP
ajst-20193	116	17	the	the	DET
ajst-20193	116	18	classification	classification	NOUN
ajst-20193	116	19	and	and	CCONJ
ajst-20193	116	20	early	early	ADJ
ajst-20193	116	21	warning	warning	NOUN
ajst-20193	116	22	of	of	ADP
ajst-20193	116	23	landslide	landslide	NOUN
ajst-20193	116	24	displacement	displacement	NOUN
ajst-20193	116	25	monitoring	monitoring	NOUN
ajst-20193	116	26	data	datum	NOUN
ajst-20193	116	27	,	,	PUNCT
ajst-20193	116	28	which	which	PRON
ajst-20193	116	29	verifies	verify	VERB
ajst-20193	116	30	the	the	DET
ajst-20193	116	31	effectiveness	effectiveness	NOUN
ajst-20193	116	32	and	and	CCONJ
ajst-20193	116	33	feasibility	feasibility	NOUN
ajst-20193	116	34	of	of	ADP
ajst-20193	116	35	the	the	DET
ajst-20193	116	36	model	model	NOUN
ajst-20193	116	37	.	.	PUNCT
ajst-20193	117	1	future	future	ADJ
ajst-20193	117	2	research	research	NOUN
ajst-20193	117	3	will	will	AUX
ajst-20193	117	4	continue	continue	VERB
ajst-20193	117	5	to	to	PART
ajst-20193	117	6	focus	focus	VERB
ajst-20193	117	7	on	on	ADP
ajst-20193	117	8	the	the	DET
ajst-20193	117	9	optimization	optimization	NOUN
ajst-20193	117	10	and	and	CCONJ
ajst-20193	117	11	expansion	expansion	NOUN
ajst-20193	117	12	of	of	ADP
ajst-20193	117	13	the	the	DET
ajst-20193	117	14	model	model	NOUN
ajst-20193	117	15	,	,	PUNCT
ajst-20193	117	16	with	with	ADP
ajst-20193	117	17	a	a	DET
ajst-20193	117	18	view	view	NOUN
ajst-20193	117	19	to	to	ADP
ajst-20193	117	20	providing	provide	VERB
ajst-20193	117	21	more	more	ADV
ajst-20193	117	22	scientific	scientific	ADJ
ajst-20193	117	23	and	and	CCONJ
ajst-20193	117	24	effective	effective	ADJ
ajst-20193	117	25	solutions	solution	NOUN
ajst-20193	117	26	for	for	ADP
ajst-20193	117	27	landslide	landslide	NOUN
ajst-20193	117	28	disaster	disaster	NOUN
ajst-20193	117	29	early	early	ADJ
ajst-20193	117	30	warning	warning	NOUN
ajst-20193	117	31	and	and	CCONJ
ajst-20193	117	32	wider	wide	ADJ
ajst-20193	117	33	applications	application	NOUN
ajst-20193	117	34	.	.	PUNCT
ajst-20193	118	1	references	reference	NOUN
ajst-20193	118	2	[	[	X
ajst-20193	118	3	1	1	NUM
ajst-20193	118	4	]	]	X
ajst-20193	118	5	wen	wen	NOUN
ajst-20193	118	6	-	-	NOUN
ajst-20193	118	7	gengcao	gengcao	NOUN
ajst-20193	118	8	,	,	PUNCT
ajst-20193	118	9	yufu	yufu	PROPN
ajst-20193	118	10	,	,	PUNCT
ajst-20193	118	11	qiu	qiu	PROPN
ajst-20193	118	12	-	-	PUNCT
ajst-20193	118	13	yaodong	yaodong	PROPN
ajst-20193	118	14	,	,	PUNCT
ajst-20193	118	15	et	et	PROPN
ajst-20193	118	16	al	al	PROPN
ajst-20193	118	17	.	.	PROPN
ajst-20193	118	18	landslide	landslide	PROPN
ajst-20193	118	19	susceptibility	susceptibility	NOUN
ajst-20193	118	20	assessment	assessment	NOUN
ajst-20193	118	21	in	in	ADP
ajst-20193	118	22	western	western	ADJ
ajst-20193	118	23	henan	henan	PROPN
ajst-20193	118	24	province	province	NOUN
ajst-20193	118	25	based	base	VERB
ajst-20193	118	26	on	on	ADP
ajst-20193	118	27	a	a	DET
ajst-20193	118	28	comparison	comparison	NOUN
ajst-20193	118	29	of	of	ADP
ajst-20193	118	30	conventional	conventional	ADJ
ajst-20193	118	31	and	and	CCONJ
ajst-20193	118	32	ensemble	ensemble	ADJ
ajst-20193	118	33	machine	machine	NOUN
ajst-20193	118	34	learning	learn	VERB
ajst-20193	118	35	[	[	X
ajst-20193	118	36	j	j	X
ajst-20193	118	37	]	]	X
ajst-20193	118	38	.	.	PUNCT
ajst-20193	119	1	china	china	PROPN
ajst-20193	119	2	geology	geology	PROPN
ajst-20193	119	3	,	,	PUNCT
ajst-20193	119	4	2023,6(3	2023,6(3	NUM
ajst-20193	119	5	):	):	PUNCT
ajst-20193	119	6	409	409	NUM
ajst-20193	119	7	-	-	SYM
ajst-20193	119	8	419	419	NUM
ajst-20193	119	9	.	.	PUNCT
ajst-20193	120	1	[	[	X
ajst-20193	120	2	2	2	NUM
ajst-20193	120	3	]	]	PUNCT
ajst-20193	120	4	a.l.achu	a.l.achu	PROPN
ajst-20193	120	5	,	,	PUNCT
ajst-20193	120	6	c.d.aju	c.d.aju	PROPN
ajst-20193	120	7	,	,	PUNCT
ajst-20193	120	8	marianodinapoli	marianodinapoli	NOUN
ajst-20193	120	9	,	,	PUNCT
ajst-20193	120	10	et	et	PROPN
ajst-20193	120	11	al	al	PROPN
ajst-20193	120	12	.	.	PROPN
ajst-20193	120	13	machine	machine	NOUN
ajst-20193	120	14	-	-	PUNCT
ajst-20193	120	15	learning	learn	VERB
ajst-20193	120	16	based	base	VERB
ajst-20193	120	17	landslide	landslide	NOUN
ajst-20193	120	18	susceptibility	susceptibility	NOUN
ajst-20193	120	19	modelling	modelling	NOUN
ajst-20193	120	20	with	with	ADP
ajst-20193	120	21	emphasis	emphasis	NOUN
ajst-20193	120	22	on	on	ADP
ajst-20193	120	23	uncertainty	uncertainty	NOUN
ajst-20193	120	24	analysis	analysis	NOUN
ajst-20193	120	25	[	[	X
ajst-20193	120	26	j	j	X
ajst-20193	120	27	]	]	X
ajst-20193	120	28	.	.	PUNCT
ajst-20193	121	1	geoscience	geoscience	NOUN
ajst-20193	121	2	frontiers	frontier	NOUN
ajst-20193	121	3	,	,	PUNCT
ajst-20193	121	4	2023,14(6	2023,14(6	NOUN
ajst-20193	121	5	):	):	PUNCT
ajst-20193	121	6	339	339	NUM
ajst-20193	121	7	-	-	SYM
ajst-20193	121	8	352	352	NUM
ajst-20193	121	9	.	.	PUNCT
ajst-20193	122	1	[	[	X
ajst-20193	122	2	3	3	NUM
ajst-20193	122	3	]	]	X
ajst-20193	122	4	shanrongrong	shanrongrong	NOUN
ajst-20193	122	5	,	,	PUNCT
ajst-20193	122	6	mazhenyu	mazhenyu	NOUN
ajst-20193	122	7	,	,	PUNCT
ajst-20193	122	8	luhongyu	luhongyu	PROPN
ajst-20193	122	9	.	.	PUNCT
ajst-20193	123	1	a	a	DET
ajst-20193	123	2	voltage	voltage	NOUN
ajst-20193	123	3	control	control	NOUN
ajst-20193	123	4	method	method	NOUN
ajst-20193	123	5	for	for	ADP
ajst-20193	123	6	distribution	distribution	NOUN
ajst-20193	123	7	networks	network	NOUN
ajst-20193	123	8	based	base	VERB
ajst-20193	123	9	on	on	ADP
ajst-20193	123	10	tcn	tcn	NOUN
ajst-20193	123	11	and	and	CCONJ
ajst-20193	123	12	mpga	mpga	NOUN
ajst-20193	123	13	under	under	ADP
ajst-20193	123	14	cloud	cloud	NOUN
ajst-20193	123	15	edge	edge	NOUN
ajst-20193	123	16	collaborative	collaborative	ADJ
ajst-20193	123	17	architecture[j	architecture[j	PROPN
ajst-20193	123	18	]	]	PUNCT
ajst-20193	123	19	.	.	PUNCT
ajst-20193	124	1	measurement	measurement	NOUN
ajst-20193	124	2	:	:	PUNCT
ajst-20193	124	3	sensors	sensor	NOUN
ajst-20193	124	4	,	,	PUNCT
ajst-20193	124	5	2024,31	2024,31	NUM
ajst-20193	124	6	:	:	PUNCT
ajst-20193	124	7	100969-	100969-	NUM
ajst-20193	124	8	.	.	PUNCT
ajst-20193	125	1	[	[	X
ajst-20193	125	2	4	4	NUM
ajst-20193	125	3	]	]	X
ajst-20193	125	4	huxiaoke	huxiaoke	NOUN
ajst-20193	125	5	,	,	PUNCT
ajst-20193	125	6	zhouxiaomin	zhouxiaomin	NOUN
ajst-20193	125	7	,	,	PUNCT
ajst-20193	125	8	liuhongfei	liuhongfei	PROPN
ajst-20193	125	9	,	,	PUNCT
ajst-20193	125	10	et	et	PROPN
ajst-20193	125	11	al	al	PROPN
ajst-20193	125	12	.	.	PROPN
ajst-20193	125	13	enhanced	enhance	VERB
ajst-20193	125	14	predictive	predictive	ADJ
ajst-20193	125	15	modeling	modeling	NOUN
ajst-20193	125	16	of	of	ADP
ajst-20193	125	17	hot	hot	ADJ
ajst-20193	125	18	rolling	rolling	NOUN
ajst-20193	125	19	work	work	NOUN
ajst-20193	125	20	roll	roll	NOUN
ajst-20193	125	21	wear	wear	NOUN
ajst-20193	125	22	using	use	VERB
ajst-20193	125	23	tcnlstm	tcnlstm	NOUN
ajst-20193	125	24	-	-	PUNCT
ajst-20193	125	25	attention[j	attention[j	PROPN
ajst-20193	125	26	]	]	PUNCT
ajst-20193	125	27	.	.	PUNCT
ajst-20193	126	1	the	the	DET
ajst-20193	126	2	international	international	ADJ
ajst-20193	126	3	journal	journal	NOUN
ajst-20193	126	4	of	of	ADP
ajst-20193	126	5	advanced	advanced	ADJ
ajst-20193	126	6	manufacturing	manufacturing	NOUN
ajst-20193	126	7	technology	technology	NOUN
ajst-20193	126	8	,	,	PUNCT
ajst-20193	126	9	2024,131(3	2024,131(3	PROPN
ajst-20193	126	10	-	-	SYM
ajst-20193	126	11	4	4	NUM
ajst-20193	126	12	):	):	PUNCT
ajst-20193	126	13	1335	1335	NUM
ajst-20193	126	14	-	-	SYM
ajst-20193	126	15	1346	1346	NUM
ajst-20193	126	16	.	.	PUNCT
ajst-20193	127	1	[	[	X
ajst-20193	127	2	5	5	NUM
ajst-20193	127	3	]	]	SYM
ajst-20193	127	4	xiaodanwang	xiaodanwang	PROPN
ajst-20193	127	5	,	,	PUNCT
ajst-20193	127	6	pengwang	pengwang	PROPN
ajst-20193	127	7	,	,	PUNCT
ajst-20193	127	8	yafeisong	yafeisong	PROPN
ajst-20193	127	9	,	,	PUNCT
ajst-20193	127	10	et	et	PROPN
ajst-20193	127	11	al	al	PROPN
ajst-20193	127	12	.	.	PUNCT
ajst-20193	127	13	recognition	recognition	NOUN
ajst-20193	127	14	of	of	ADP
ajst-20193	127	15	high	high	ADJ
ajst-20193	127	16	-	-	PUNCT
ajst-20193	127	17	resolution	resolution	NOUN
ajst-20193	127	18	range	range	NOUN
ajst-20193	127	19	profile	profile	NOUN
ajst-20193	127	20	sequence	sequence	NOUN
ajst-20193	127	21	based	base	VERB
ajst-20193	127	22	on	on	ADP
ajst-20193	127	23	tcn	tcn	NOUN
ajst-20193	127	24	with	with	ADP
ajst-20193	127	25	sequence	sequence	NOUN
ajst-20193	127	26	length	length	NOUN
ajst-20193	127	27	-	-	PUNCT
ajst-20193	127	28	adaptive	adaptive	ADJ
ajst-20193	127	29	algorithm	algorithm	NOUN
ajst-20193	127	30	and	and	CCONJ
ajst-20193	127	31	elastic	elastic	ADJ
ajst-20193	127	32	net	net	NOUN
ajst-20193	127	33	regularization[j	regularization[j	X
ajst-20193	127	34	]	]	PUNCT
ajst-20193	127	35	.	.	PUNCT
ajst-20193	128	1	expert	expert	NOUN
ajst-20193	128	2	systems	system	NOUN
ajst-20193	128	3	with	with	ADP
ajst-20193	128	4	applications	application	NOUN
ajst-20193	128	5	,	,	PUNCT
ajst-20193	128	6	2024,248	2024,248	NUM
ajst-20193	128	7	:	:	PUNCT
ajst-20193	128	8	123417-	123417-	NUM
ajst-20193	128	9	.	.	PUNCT
ajst-20193	129	1	[	[	X
ajst-20193	129	2	6	6	NUM
ajst-20193	129	3	]	]	SYM
ajst-20193	129	4	jialongyu	jialongyu	NOUN
ajst-20193	129	5	,	,	PUNCT
ajst-20193	129	6	chuntaoyang	chuntaoyang	NOUN
ajst-20193	129	7	,	,	PUNCT
ajst-20193	129	8	jiangtaotan	jiangtaotan	PROPN
ajst-20193	129	9	.	.	PUNCT
ajst-20193	130	1	research	research	NOUN
ajst-20193	130	2	and	and	CCONJ
ajst-20193	130	3	development	development	NOUN
ajst-20193	130	4	trend	trend	NOUN
ajst-20193	130	5	of	of	ADP
ajst-20193	130	6	landslide	landslide	NOUN
ajst-20193	130	7	deformation	deformation	NOUN
ajst-20193	130	8	monitoring	monitor	VERB
ajst-20193	130	9	methods[j	methods[j	NOUN
ajst-20193	130	10	]	]	PUNCT
ajst-20193	130	11	.	.	PUNCT
ajst-20193	131	1	advances	advance	NOUN
ajst-20193	131	2	in	in	ADP
ajst-20193	131	3	computer	computer	NOUN
ajst-20193	131	4	,	,	PUNCT
ajst-20193	131	5	signals	signal	NOUN
ajst-20193	131	6	and	and	CCONJ
ajst-20193	131	7	systems	system	NOUN
ajst-20193	131	8	,	,	PUNCT
ajst-20193	131	9	2023,7(3	2023,7(3	NUM
ajst-20193	131	10	)	)	PUNCT
ajst-20193	131	11	.	.	PUNCT
ajst-20193	132	1	[	[	X
ajst-20193	132	2	7	7	NUM
ajst-20193	132	3	]	]	PUNCT
ajst-20193	132	4	wangchun	wangchun	NOUN
ajst-20193	132	5	,	,	PUNCT
ajst-20193	132	6	panshirui	panshirui	PROPN
ajst-20193	132	7	,	,	PUNCT
ajst-20193	132	8	yucelinap	yucelinap	NOUN
ajst-20193	132	9	.	.	PROPN
ajst-20193	132	10	,	,	PUNCT
ajst-20193	132	11	et	et	PROPN
ajst-20193	132	12	al	al	PROPN
ajst-20193	132	13	.	.	PUNCT
ajst-20193	133	1	deep	deep	ADJ
ajst-20193	133	2	neighboraware	neighboraware	NOUN
ajst-20193	133	3	embedding	embed	VERB
ajst-20193	133	4	for	for	ADP
ajst-20193	133	5	node	node	ADJ
ajst-20193	133	6	clustering	cluster	VERB
ajst-20193	133	7	in	in	ADP
ajst-20193	133	8	attributed	attribute	VERB
ajst-20193	133	9	graphs[j	graphs[j	PROPN
ajst-20193	133	10	]	]	PUNCT
ajst-20193	133	11	.	.	PUNCT
ajst-20193	134	1	pattern	pattern	NOUN
ajst-20193	134	2	recognition	recognition	NOUN
ajst-20193	134	3	,	,	PUNCT
ajst-20193	134	4	2022,122	2022,122	NUM
ajst-20193	134	5	.	.	PUNCT
ajst-20193	135	1	[	[	X
ajst-20193	135	2	8	8	NUM
ajst-20193	135	3	]	]	X
ajst-20193	135	4	heiglmichael	heiglmichael	PROPN
ajst-20193	135	5	,	,	PUNCT
ajst-20193	135	6	anandkumarashutosh	anandkumarashutosh	PROPN
ajst-20193	135	7	,	,	PUNCT
ajst-20193	135	8	urmannandreas	urmannandrea	NOUN
ajst-20193	135	9	,	,	PUNCT
ajst-20193	135	10	et	et	PROPN
ajst-20193	135	11	al	al	PROPN
ajst-20193	135	12	.	.	PROPN
ajst-20193	136	1	on	on	ADP
ajst-20193	136	2	the	the	DET
ajst-20193	136	3	improvement	improvement	NOUN
ajst-20193	136	4	of	of	ADP
ajst-20193	136	5	the	the	DET
ajst-20193	136	6	isolation	isolation	NOUN
ajst-20193	136	7	forest	forest	NOUN
ajst-20193	136	8	algorithm	algorithm	NOUN
ajst-20193	136	9	for	for	ADP
ajst-20193	136	10	outlier	outlier	ADJ
ajst-20193	136	11	detection	detection	NOUN
ajst-20193	136	12	with	with	ADP
ajst-20193	136	13	streaming	streaming	NOUN
ajst-20193	136	14	data[j	data[j	NOUN
ajst-20193	136	15	]	]	PUNCT
ajst-20193	136	16	.	.	PUNCT
ajst-20193	137	1	electronics	electronic	NOUN
ajst-20193	137	2	,	,	PUNCT
ajst-20193	137	3	2021,10(13	2021,10(13	NUM
ajst-20193	137	4	):	):	PUNCT
ajst-20193	137	5	1534	1534	NUM
ajst-20193	137	6	-	-	SYM
ajst-20193	137	7	1534	1534	NUM
ajst-20193	137	8	.	.	PUNCT
ajst-20193	138	1	95	95	NUM
ajst-20193	139	1	[	[	X
ajst-20193	139	2	9	9	NUM
ajst-20193	139	3	]	]	SYM
ajst-20193	139	4	xiaowudeng	xiaowudeng	PROPN
ajst-20193	139	5	,	,	PUNCT
ajst-20193	139	6	pengjiang	pengjiang	PROPN
ajst-20193	139	7	,	,	PUNCT
ajst-20193	139	8	xiaoningpeng	xiaoningpeng	PROPN
ajst-20193	139	9	,	,	PUNCT
ajst-20193	139	10	et	et	PROPN
ajst-20193	139	11	al	al	PROPN
ajst-20193	139	12	.	.	PUNCT
ajst-20193	140	1	support	support	PROPN
ajst-20193	140	2	highorder	highorder	PROPN
ajst-20193	140	3	tensor	tensor	NOUN
ajst-20193	140	4	data	data	NOUN
ajst-20193	140	5	description	description	NOUN
ajst-20193	140	6	for	for	ADP
ajst-20193	140	7	outlier	outlier	ADJ
ajst-20193	140	8	detection	detection	NOUN
ajst-20193	140	9	in	in	ADP
ajst-20193	140	10	highdimensional	highdimensional	ADJ
ajst-20193	140	11	big	big	ADJ
ajst-20193	140	12	sensor	sensor	NOUN
ajst-20193	140	13	data[j	data[j	NOUN
ajst-20193	140	14	]	]	PUNCT
ajst-20193	140	15	.	.	PUNCT
ajst-20193	141	1	future	future	ADJ
ajst-20193	141	2	generation	generation	NOUN
ajst-20193	141	3	computer	computer	NOUN
ajst-20193	141	4	systems	system	NOUN
ajst-20193	141	5	,	,	PUNCT
ajst-20193	141	6	2018,81	2018,81	NUM
ajst-20193	141	7	:	:	PUNCT
ajst-20193	141	8	177	177	NUM
ajst-20193	141	9	-	-	SYM
ajst-20193	141	10	187	187	NUM
ajst-20193	141	11	.	.	PUNCT
ajst-20193	142	1	[	[	X
ajst-20193	142	2	10	10	NUM
ajst-20193	142	3	]	]	X
ajst-20193	142	4	guptashalmoli	guptashalmoli	NOUN
ajst-20193	142	5	,	,	PUNCT
ajst-20193	142	6	kumarravi	kumarravi	PROPN
ajst-20193	142	7	,	,	PUNCT
ajst-20193	142	8	lukefu	lukefu	PROPN
ajst-20193	142	9	,	,	PUNCT
ajst-20193	142	10	et	et	PROPN
ajst-20193	142	11	al	al	PROPN
ajst-20193	142	12	.	.	PUNCT
ajst-20193	143	1	local	local	ADJ
ajst-20193	143	2	search	search	NOUN
ajst-20193	143	3	methods	method	NOUN
ajst-20193	143	4	for	for	ADP
ajst-20193	143	5	k	k	NOUN
ajst-20193	143	6	-	-	PUNCT
ajst-20193	143	7	means	means	NOUN
ajst-20193	143	8	with	with	ADP
ajst-20193	143	9	outliers[j	outliers[j	PROPN
ajst-20193	143	10	]	]	PUNCT
ajst-20193	143	11	.	.	PUNCT
ajst-20193	144	1	proceedings	proceeding	NOUN
ajst-20193	144	2	of	of	ADP
ajst-20193	144	3	the	the	DET
ajst-20193	144	4	vldb	vldb	NOUN
ajst-20193	144	5	endowment	endowment	NOUN
ajst-20193	144	6	,	,	PUNCT
ajst-20193	144	7	2017,10(7	2017,10(7	NOUN
ajst-20193	144	8	):	):	PUNCT
ajst-20193	144	9	757	757	PROPN
ajst-20193	144	10	-	-	SYM
ajst-20193	144	11	768	768	NUM
ajst-20193	144	12	.	.	PUNCT