id	sid	tid	token	lemma	pos
fcis-31901	1	1	frontiers	frontier	NOUN
fcis-31901	1	2	in	in	ADP
fcis-31901	1	3	computing	computing	NOUN
fcis-31901	1	4	and	and	CCONJ
fcis-31901	1	5	intelligent	intelligent	ADJ
fcis-31901	1	6	systems	system	NOUN
fcis-31901	1	7	issn	issn	VERB
fcis-31901	1	8	:	:	PUNCT
fcis-31901	1	9	2832	2832	NUM
fcis-31901	1	10	-	-	SYM
fcis-31901	1	11	6024	6024	NUM
fcis-31901	1	12	|	|	NOUN
fcis-31901	1	13	vol	vol	NOUN
fcis-31901	1	14	.	.	PROPN
fcis-31901	2	1	13	13	NUM
fcis-31901	2	2	,	,	PUNCT
fcis-31901	2	3	no	no	INTJ
fcis-31901	2	4	.	.	NOUN
fcis-31901	2	5	3	3	NUM
fcis-31901	2	6	,	,	PUNCT
fcis-31901	2	7	2025	2025	NUM
fcis-31901	2	8	46	46	NUM
fcis-31901	2	9	a	a	DET
fcis-31901	2	10	hybrid	hybrid	ADJ
fcis-31901	2	11	computational	computational	ADJ
fcis-31901	2	12	framework	framework	NOUN
fcis-31901	2	13	for	for	ADP
fcis-31901	2	14	regional	regional	ADJ
fcis-31901	2	15	classification	classification	NOUN
fcis-31901	2	16	in	in	ADP
fcis-31901	2	17	gravity‐aided	gravity‐aided	ADJ
fcis-31901	2	18	navigation	navigation	NOUN
fcis-31901	2	19	:	:	PUNCT
fcis-31901	2	20	integrating	integrate	VERB
fcis-31901	2	21	hierarchical	hierarchical	ADJ
fcis-31901	2	22	clustering	clustering	NOUN
fcis-31901	2	23	and	and	CCONJ
fcis-31901	2	24	random	random	ADJ
fcis-31901	2	25	forests	forest	NOUN
fcis-31901	2	26	yichen	yichen	ADV
fcis-31901	2	27	yang	yang	PROPN
fcis-31901	2	28	1	1	NUM
fcis-31901	2	29	,	,	PUNCT
fcis-31901	2	30	*	*	PUNCT
fcis-31901	2	31	,	,	PUNCT
fcis-31901	2	32	jiayuan	jiayuan	PROPN
fcis-31901	2	33	du	du	PROPN
fcis-31901	2	34	2	2	NUM
fcis-31901	2	35	1	1	NUM
fcis-31901	2	36	shenyang	shenyang	PROPN
fcis-31901	2	37	aerospace	aerospace	PROPN
fcis-31901	2	38	university	university	PROPN
fcis-31901	2	39	,	,	PUNCT
fcis-31901	2	40	shenyang	shenyang	PROPN
fcis-31901	2	41	,	,	PUNCT
fcis-31901	2	42	liaoning	liaoning	PROPN
fcis-31901	2	43	,	,	PUNCT
fcis-31901	2	44	china	china	PROPN
fcis-31901	2	45	2	2	NUM
fcis-31901	2	46	dalian	dalian	PROPN
fcis-31901	2	47	polytechnic	polytechnic	ADJ
fcis-31901	2	48	university	university	PROPN
fcis-31901	2	49	,	,	PUNCT
fcis-31901	2	50	dalian	dalian	PROPN
fcis-31901	2	51	,	,	PUNCT
fcis-31901	2	52	liaoning	liaoning	PROPN
fcis-31901	2	53	,	,	PUNCT
fcis-31901	2	54	china	china	PROPN
fcis-31901	2	55	*	*	PUNCT
fcis-31901	2	56	corresponding	correspond	VERB
fcis-31901	2	57	author	author	NOUN
fcis-31901	2	58	:	:	PUNCT
fcis-31901	2	59	yichen	yichen	PROPN
fcis-31901	2	60	yang	yang	PROPN
fcis-31901	2	61	(	(	PUNCT
fcis-31901	2	62	email	email	NOUN
fcis-31901	2	63	:	:	PUNCT
fcis-31901	2	64	1918988932@qq.com	1918988932@qq.com	NUM
fcis-31901	2	65	)	)	PUNCT
fcis-31901	2	66	abstract	abstract	NOUN
fcis-31901	2	67	:	:	PUNCT
fcis-31901	2	68	data	data	NOUN
fcis-31901	2	69	-	-	PUNCT
fcis-31901	2	70	driven	drive	VERB
fcis-31901	2	71	gravity	gravity	NOUN
fcis-31901	2	72	-	-	PUNCT
fcis-31901	2	73	aided	aid	VERB
fcis-31901	2	74	underwater	underwater	ADJ
fcis-31901	2	75	navigation	navigation	NOUN
fcis-31901	2	76	requires	require	VERB
fcis-31901	2	77	reliable	reliable	ADJ
fcis-31901	2	78	region	region	NOUN
fcis-31901	2	79	-	-	PUNCT
fcis-31901	2	80	of	of	ADP
fcis-31901	2	81	-	-	PUNCT
fcis-31901	2	82	acceptance	acceptance	NOUN
fcis-31901	2	83	(	(	PUNCT
fcis-31901	2	84	roa	roa	NOUN
fcis-31901	2	85	)	)	PUNCT
fcis-31901	2	86	classification	classification	NOUN
fcis-31901	2	87	to	to	PART
fcis-31901	2	88	ensure	ensure	VERB
fcis-31901	2	89	robust	robust	ADJ
fcis-31901	2	90	localization	localization	NOUN
fcis-31901	2	91	.	.	PUNCT
fcis-31901	3	1	we	we	PRON
fcis-31901	3	2	formulate	formulate	VERB
fcis-31901	3	3	roa	roa	NOUN
fcis-31901	3	4	prediction	prediction	NOUN
fcis-31901	3	5	on	on	ADP
fcis-31901	3	6	geospatial	geospatial	ADJ
fcis-31901	3	7	gravity	gravity	NOUN
fcis-31901	3	8	-	-	PUNCT
fcis-31901	3	9	anomaly	anomaly	NOUN
fcis-31901	3	10	fields	field	NOUN
fcis-31901	3	11	as	as	ADP
fcis-31901	3	12	a	a	DET
fcis-31901	3	13	pattern	pattern	NOUN
fcis-31901	3	14	-	-	PUNCT
fcis-31901	3	15	recognition	recognition	NOUN
fcis-31901	3	16	task	task	NOUN
fcis-31901	3	17	and	and	CCONJ
fcis-31901	3	18	design	design	VERB
fcis-31901	3	19	a	a	DET
fcis-31901	3	20	hybrid	hybrid	ADJ
fcis-31901	3	21	computational	computational	ADJ
fcis-31901	3	22	pipeline	pipeline	NOUN
fcis-31901	3	23	that	that	PRON
fcis-31901	3	24	couples	couple	VERB
fcis-31901	3	25	unsupervised	unsupervise	VERB
fcis-31901	3	26	calibration	calibration	NOUN
fcis-31901	3	27	with	with	ADP
fcis-31901	3	28	supervised	supervised	ADJ
fcis-31901	3	29	inference	inference	NOUN
fcis-31901	3	30	.	.	PUNCT
fcis-31901	4	1	this	this	DET
fcis-31901	4	2	paper	paper	NOUN
fcis-31901	4	3	proposes	propose	VERB
fcis-31901	4	4	:	:	PUNCT
fcis-31901	4	5	(	(	PUNCT
fcis-31901	4	6	i	i	NOUN
fcis-31901	4	7	)	)	PUNCT
fcis-31901	4	8	feature	feature	NOUN
fcis-31901	4	9	normalization	normalization	NOUN
fcis-31901	4	10	over	over	ADP
fcis-31901	4	11	longitude	longitude	ADJ
fcis-31901	4	12	–	–	PUNCT
fcis-31901	4	13	latitude	latitude	NOUN
fcis-31901	4	14	–	–	PUNCT
fcis-31901	4	15	anomaly	anomaly	NOUN
fcis-31901	4	16	triplets	triplet	NOUN
fcis-31901	4	17	;	;	PUNCT
fcis-31901	4	18	(	(	PUNCT
fcis-31901	4	19	ii	ii	NOUN
fcis-31901	4	20	)	)	PUNCT
fcis-31901	4	21	ward	ward	NOUN
fcis-31901	4	22	hierarchical	hierarchical	ADJ
fcis-31901	4	23	clustering	clustering	NOUN
fcis-31901	4	24	to	to	PART
fcis-31901	4	25	calibrate	calibrate	VERB
fcis-31901	4	26	roa	roa	NOUN
fcis-31901	4	27	labels	label	NOUN
fcis-31901	4	28	;	;	PUNCT
fcis-31901	4	29	and	and	CCONJ
fcis-31901	4	30	(	(	PUNCT
fcis-31901	4	31	iii	iii	X
fcis-31901	4	32	)	)	PUNCT
fcis-31901	4	33	a	a	DET
fcis-31901	4	34	random	random	ADJ
fcis-31901	4	35	-	-	PUNCT
fcis-31901	4	36	forest	forest	NOUN
fcis-31901	4	37	classifier	classifier	NOUN
fcis-31901	4	38	trained	train	VERB
fcis-31901	4	39	on	on	ADP
fcis-31901	4	40	the	the	DET
fcis-31901	4	41	calibrated	calibrate	VERB
fcis-31901	4	42	labels	label	NOUN
fcis-31901	4	43	,	,	PUNCT
fcis-31901	4	44	benchmarked	benchmarke	VERB
fcis-31901	4	45	against	against	ADP
fcis-31901	4	46	svm	svm	PROPN
fcis-31901	4	47	,	,	PUNCT
fcis-31901	4	48	knn	knn	PROPN
fcis-31901	4	49	,	,	PUNCT
fcis-31901	4	50	and	and	CCONJ
fcis-31901	4	51	decision	decision	NOUN
fcis-31901	4	52	-	-	PUNCT
fcis-31901	4	53	tree	tree	NOUN
fcis-31901	4	54	baselines	baseline	NOUN
fcis-31901	4	55	.	.	PUNCT
fcis-31901	5	1	the	the	DET
fcis-31901	5	2	trained	train	VERB
fcis-31901	5	3	model	model	NOUN
fcis-31901	5	4	is	be	AUX
fcis-31901	5	5	directly	directly	ADV
fcis-31901	5	6	transferred	transfer	VERB
fcis-31901	5	7	to	to	ADP
fcis-31901	5	8	a	a	DET
fcis-31901	5	9	second	second	ADJ
fcis-31901	5	10	dataset	dataset	NOUN
fcis-31901	5	11	to	to	PART
fcis-31901	5	12	assess	assess	VERB
fcis-31901	5	13	cross	cross	ADJ
fcis-31901	5	14	-	-	ADJ
fcis-31901	5	15	dataset	dataset	ADJ
fcis-31901	5	16	generalization	generalization	NOUN
fcis-31901	5	17	.	.	PUNCT
fcis-31901	6	1	experiments	experiment	NOUN
fcis-31901	6	2	show	show	VERB
fcis-31901	6	3	high	high	ADJ
fcis-31901	6	4	accuracy	accuracy	NOUN
fcis-31901	6	5	and	and	CCONJ
fcis-31901	6	6	balanced	balanced	ADJ
fcis-31901	6	7	precision	precision	NOUN
fcis-31901	6	8	/	/	SYM
fcis-31901	6	9	recall	recall	NOUN
fcis-31901	6	10	on	on	ADP
fcis-31901	6	11	held	hold	VERB
fcis-31901	6	12	-	-	PUNCT
fcis-31901	6	13	out	out	ADP
fcis-31901	6	14	data	datum	NOUN
fcis-31901	6	15	,	,	PUNCT
fcis-31901	6	16	and	and	CCONJ
fcis-31901	6	17	0.99	0.99	NUM
fcis-31901	6	18	accuracy	accuracy	NOUN
fcis-31901	6	19	in	in	ADP
fcis-31901	6	20	the	the	DET
fcis-31901	6	21	transfer	transfer	NOUN
fcis-31901	6	22	evaluation	evaluation	NOUN
fcis-31901	6	23	,	,	PUNCT
fcis-31901	6	24	while	while	SCONJ
fcis-31901	6	25	maintaining	maintain	VERB
fcis-31901	6	26	low	low	ADJ
fcis-31901	6	27	inference	inference	NOUN
fcis-31901	6	28	latency	latency	NOUN
fcis-31901	6	29	suitable	suitable	ADJ
fcis-31901	6	30	for	for	ADP
fcis-31901	6	31	deployment	deployment	NOUN
fcis-31901	6	32	.	.	PUNCT
fcis-31901	7	1	the	the	DET
fcis-31901	7	2	framework	framework	NOUN
fcis-31901	7	3	offers	offer	VERB
fcis-31901	7	4	a	a	DET
fcis-31901	7	5	practical	practical	ADJ
fcis-31901	7	6	,	,	PUNCT
fcis-31901	7	7	generalizable	generalizable	ADJ
fcis-31901	7	8	solution	solution	NOUN
fcis-31901	7	9	for	for	ADP
fcis-31901	7	10	roa	roa	PROPN
fcis-31901	7	11	prediction	prediction	NOUN
fcis-31901	7	12	that	that	PRON
fcis-31901	7	13	can	can	AUX
fcis-31901	7	14	be	be	AUX
fcis-31901	7	15	integrated	integrate	VERB
fcis-31901	7	16	with	with	ADP
fcis-31901	7	17	downstream	downstream	ADJ
fcis-31901	7	18	path	path	NOUN
fcis-31901	7	19	-	-	PUNCT
fcis-31901	7	20	planning	planning	NOUN
fcis-31901	7	21	and	and	CCONJ
fcis-31901	7	22	decision	decision	NOUN
fcis-31901	7	23	modules	module	NOUN
fcis-31901	7	24	.	.	PUNCT
fcis-31901	8	1	keywords	keyword	NOUN
fcis-31901	8	2	:	:	PUNCT
fcis-31901	8	3	hierarchical	hierarchical	ADJ
fcis-31901	8	4	clustering	clustering	NOUN
fcis-31901	8	5	;	;	PUNCT
fcis-31901	8	6	random	random	ADJ
fcis-31901	8	7	forest	forest	NOUN
fcis-31901	8	8	classification	classification	NOUN
fcis-31901	8	9	;	;	PUNCT
fcis-31901	8	10	domain	domain	NOUN
fcis-31901	8	11	adaptation	adaptation	NOUN
fcis-31901	8	12	.	.	PUNCT
fcis-31901	9	1	1	1	X
fcis-31901	9	2	.	.	X
fcis-31901	9	3	introduction	introduction	NOUN
fcis-31901	9	4	accurate	accurate	ADJ
fcis-31901	9	5	regional	regional	ADJ
fcis-31901	9	6	classification	classification	NOUN
fcis-31901	9	7	in	in	ADP
fcis-31901	9	8	gravity	gravity	NOUN
fcis-31901	9	9	-	-	PUNCT
fcis-31901	9	10	aided	aid	VERB
fcis-31901	9	11	navigation	navigation	NOUN
fcis-31901	9	12	is	be	AUX
fcis-31901	9	13	a	a	DET
fcis-31901	9	14	critical	critical	ADJ
fcis-31901	9	15	challenge	challenge	NOUN
fcis-31901	9	16	for	for	ADP
fcis-31901	9	17	underwater	underwater	ADJ
fcis-31901	9	18	localization	localization	NOUN
fcis-31901	9	19	.	.	PUNCT
fcis-31901	10	1	the	the	DET
fcis-31901	10	2	task	task	NOUN
fcis-31901	10	3	can	can	AUX
fcis-31901	10	4	be	be	AUX
fcis-31901	10	5	formalized	formalize	VERB
fcis-31901	10	6	as	as	ADP
fcis-31901	10	7	pattern	pattern	NOUN
fcis-31901	10	8	recognition	recognition	NOUN
fcis-31901	10	9	on	on	ADP
fcis-31901	10	10	geospatial	geospatial	ADJ
fcis-31901	10	11	gravity	gravity	NOUN
fcis-31901	10	12	anomaly	anomaly	NOUN
fcis-31901	10	13	fields	field	NOUN
fcis-31901	10	14	,	,	PUNCT
fcis-31901	10	15	where	where	SCONJ
fcis-31901	10	16	heterogeneous	heterogeneous	ADJ
fcis-31901	10	17	anomaly	anomaly	NOUN
fcis-31901	10	18	distributions	distribution	NOUN
fcis-31901	10	19	complicate	complicate	VERB
fcis-31901	10	20	calibration	calibration	NOUN
fcis-31901	10	21	of	of	ADP
fcis-31901	10	22	reliable	reliable	ADJ
fcis-31901	10	23	regions	region	NOUN
fcis-31901	10	24	of	of	ADP
fcis-31901	10	25	acceptance	acceptance	NOUN
fcis-31901	10	26	(	(	PUNCT
fcis-31901	10	27	roa	roa	PROPN
fcis-31901	10	28	)	)	PUNCT
fcis-31901	10	29	.	.	PUNCT
fcis-31901	11	1	this	this	DET
fcis-31901	11	2	issue	issue	NOUN
fcis-31901	11	3	is	be	AUX
fcis-31901	11	4	demanding	demand	VERB
fcis-31901	11	5	due	due	ADJ
fcis-31901	11	6	to	to	ADP
fcis-31901	11	7	the	the	DET
fcis-31901	11	8	sensitivity	sensitivity	NOUN
fcis-31901	11	9	of	of	ADP
fcis-31901	11	10	navigation	navigation	NOUN
fcis-31901	11	11	systems	system	NOUN
fcis-31901	11	12	to	to	PART
fcis-31901	11	13	anomaly	anomaly	VERB
fcis-31901	11	14	variations	variation	NOUN
fcis-31901	11	15	and	and	CCONJ
fcis-31901	11	16	the	the	DET
fcis-31901	11	17	difficulty	difficulty	NOUN
fcis-31901	11	18	of	of	ADP
fcis-31901	11	19	ensuring	ensure	VERB
fcis-31901	11	20	consistent	consistent	ADJ
fcis-31901	11	21	performance	performance	NOUN
fcis-31901	11	22	across	across	ADP
fcis-31901	11	23	zones	zone	NOUN
fcis-31901	11	24	[	[	X
fcis-31901	11	25	1,2	1,2	NUM
fcis-31901	11	26	]	]	PUNCT
fcis-31901	11	27	.	.	PUNCT
fcis-31901	12	1	recent	recent	ADJ
fcis-31901	12	2	advances	advance	NOUN
fcis-31901	12	3	in	in	ADP
fcis-31901	12	4	machine	machine	NOUN
fcis-31901	12	5	learning	learning	NOUN
fcis-31901	12	6	and	and	CCONJ
fcis-31901	12	7	geospatial	geospatial	ADJ
fcis-31901	12	8	data	datum	NOUN
fcis-31901	12	9	mining	mining	NOUN
fcis-31901	12	10	have	have	AUX
fcis-31901	12	11	provided	provide	VERB
fcis-31901	12	12	new	new	ADJ
fcis-31901	12	13	opportunities	opportunity	NOUN
fcis-31901	12	14	for	for	ADP
fcis-31901	12	15	anomaly	anomaly	NOUN
fcis-31901	12	16	-	-	PUNCT
fcis-31901	12	17	based	base	VERB
fcis-31901	12	18	regional	regional	ADJ
fcis-31901	12	19	prediction	prediction	NOUN
fcis-31901	12	20	.	.	PUNCT
fcis-31901	13	1	hierarchical	hierarchical	ADJ
fcis-31901	13	2	clustering	clustering	NOUN
fcis-31901	13	3	has	have	AUX
fcis-31901	13	4	shown	show	VERB
fcis-31901	13	5	advantages	advantage	NOUN
fcis-31901	13	6	in	in	ADP
fcis-31901	13	7	capturing	capture	VERB
fcis-31901	13	8	structural	structural	ADJ
fcis-31901	13	9	similarity	similarity	NOUN
fcis-31901	13	10	in	in	ADP
fcis-31901	13	11	spatial	spatial	ADJ
fcis-31901	13	12	datasets	dataset	NOUN
fcis-31901	13	13	[	[	X
fcis-31901	13	14	3	3	NUM
fcis-31901	13	15	]	]	PUNCT
fcis-31901	13	16	,	,	PUNCT
fcis-31901	13	17	while	while	SCONJ
fcis-31901	13	18	ensemble	ensemble	ADJ
fcis-31901	13	19	classifiers	classifier	NOUN
fcis-31901	13	20	such	such	ADJ
fcis-31901	13	21	as	as	ADP
fcis-31901	13	22	random	random	ADJ
fcis-31901	13	23	forests	forest	NOUN
fcis-31901	13	24	have	have	AUX
fcis-31901	13	25	demonstrated	demonstrate	VERB
fcis-31901	13	26	robustness	robustness	NOUN
fcis-31901	13	27	against	against	ADP
fcis-31901	13	28	noise	noise	NOUN
fcis-31901	13	29	and	and	CCONJ
fcis-31901	13	30	nonlinear	nonlinear	ADJ
fcis-31901	13	31	feature	feature	NOUN
fcis-31901	13	32	interactions	interaction	NOUN
fcis-31901	13	33	[	[	X
fcis-31901	13	34	4	4	NUM
fcis-31901	13	35	]	]	PUNCT
fcis-31901	13	36	.	.	PUNCT
fcis-31901	14	1	however	however	ADV
fcis-31901	14	2	,	,	PUNCT
fcis-31901	14	3	the	the	DET
fcis-31901	14	4	integration	integration	NOUN
fcis-31901	14	5	of	of	ADP
fcis-31901	14	6	unsupervised	unsupervised	ADJ
fcis-31901	14	7	calibration	calibration	NOUN
fcis-31901	14	8	and	and	CCONJ
fcis-31901	14	9	supervised	supervise	VERB
fcis-31901	14	10	inference	inference	NOUN
fcis-31901	14	11	for	for	ADP
fcis-31901	14	12	anomaly	anomaly	NOUN
fcis-31901	14	13	-	-	PUNCT
fcis-31901	14	14	driven	drive	VERB
fcis-31901	14	15	navigation	navigation	NOUN
fcis-31901	14	16	remains	remain	VERB
fcis-31901	14	17	underexplored	underexplored	ADJ
fcis-31901	14	18	,	,	PUNCT
fcis-31901	14	19	especially	especially	ADV
fcis-31901	14	20	with	with	ADP
fcis-31901	14	21	respect	respect	NOUN
fcis-31901	14	22	to	to	ADP
fcis-31901	14	23	transferability	transferability	NOUN
fcis-31901	14	24	across	across	ADP
fcis-31901	14	25	datasets	dataset	NOUN
fcis-31901	14	26	.	.	PUNCT
fcis-31901	15	1	this	this	DET
fcis-31901	15	2	paper	paper	NOUN
fcis-31901	15	3	develops	develop	VERB
fcis-31901	15	4	a	a	DET
fcis-31901	15	5	hybrid	hybrid	ADJ
fcis-31901	15	6	computational	computational	ADJ
fcis-31901	15	7	framework	framework	NOUN
fcis-31901	15	8	that	that	PRON
fcis-31901	15	9	couples	couple	NOUN
fcis-31901	15	10	ward	ward	NOUN
fcis-31901	15	11	-	-	PUNCT
fcis-31901	15	12	based	base	VERB
fcis-31901	15	13	hierarchical	hierarchical	ADJ
fcis-31901	15	14	clustering	clustering	NOUN
fcis-31901	15	15	with	with	ADP
fcis-31901	15	16	random	random	ADJ
fcis-31901	15	17	forest	forest	NOUN
fcis-31901	15	18	classification	classification	NOUN
fcis-31901	15	19	to	to	PART
fcis-31901	15	20	construct	construct	VERB
fcis-31901	15	21	reliable	reliable	ADJ
fcis-31901	15	22	roa	roa	NOUN
fcis-31901	15	23	predictors	predictor	NOUN
fcis-31901	15	24	.	.	PUNCT
fcis-31901	16	1	the	the	DET
fcis-31901	16	2	proposed	propose	VERB
fcis-31901	16	3	approach	approach	NOUN
fcis-31901	16	4	not	not	PART
fcis-31901	16	5	only	only	ADV
fcis-31901	16	6	improves	improve	VERB
fcis-31901	16	7	intra	intra	ADJ
fcis-31901	16	8	-	-	ADJ
fcis-31901	16	9	dataset	dataset	ADJ
fcis-31901	16	10	accuracy	accuracy	NOUN
fcis-31901	16	11	but	but	CCONJ
fcis-31901	16	12	also	also	ADV
fcis-31901	16	13	exhibits	exhibit	VERB
fcis-31901	16	14	strong	strong	ADJ
fcis-31901	16	15	generalization	generalization	NOUN
fcis-31901	16	16	performance	performance	NOUN
fcis-31901	16	17	when	when	SCONJ
fcis-31901	16	18	transferred	transfer	VERB
fcis-31901	16	19	to	to	ADP
fcis-31901	16	20	new	new	ADJ
fcis-31901	16	21	anomaly	anomaly	NOUN
fcis-31901	16	22	datasets	dataset	NOUN
fcis-31901	16	23	,	,	PUNCT
fcis-31901	16	24	achieving	achieve	VERB
fcis-31901	16	25	accuracy	accuracy	NOUN
fcis-31901	16	26	rates	rate	NOUN
fcis-31901	16	27	up	up	ADV
fcis-31901	16	28	to	to	ADP
fcis-31901	16	29	0.99	0.99	NUM
fcis-31901	16	30	.	.	PUNCT
fcis-31901	17	1	such	such	ADJ
fcis-31901	17	2	performance	performance	NOUN
fcis-31901	17	3	highlights	highlight	VERB
fcis-31901	17	4	the	the	DET
fcis-31901	17	5	practicality	practicality	NOUN
fcis-31901	17	6	of	of	ADP
fcis-31901	17	7	the	the	DET
fcis-31901	17	8	method	method	NOUN
fcis-31901	17	9	for	for	ADP
fcis-31901	17	10	deployment	deployment	NOUN
fcis-31901	17	11	in	in	ADP
fcis-31901	17	12	real	real	ADJ
fcis-31901	17	13	-	-	PUNCT
fcis-31901	17	14	world	world	NOUN
fcis-31901	17	15	navigation	navigation	NOUN
fcis-31901	17	16	systems	system	NOUN
fcis-31901	17	17	,	,	PUNCT
fcis-31901	17	18	contributing	contribute	VERB
fcis-31901	17	19	a	a	DET
fcis-31901	17	20	generalizable	generalizable	ADJ
fcis-31901	17	21	solution	solution	NOUN
fcis-31901	17	22	for	for	ADP
fcis-31901	17	23	data	data	NOUN
fcis-31901	17	24	-	-	PUNCT
fcis-31901	17	25	driven	drive	VERB
fcis-31901	17	26	anomaly	anomaly	NOUN
fcis-31901	17	27	classification	classification	NOUN
fcis-31901	17	28	[	[	X
fcis-31901	17	29	5,6	5,6	NUM
fcis-31901	17	30	]	]	SYM
fcis-31901	17	31	.	.	PUNCT
fcis-31901	18	1	2	2	X
fcis-31901	18	2	.	.	X
fcis-31901	18	3	hierarchical	hierarchical	ADJ
fcis-31901	18	4	clustering	clustering	ADJ
fcis-31901	18	5	framework	framework	NOUN
fcis-31901	18	6	for	for	ADP
fcis-31901	18	7	regional	regional	ADJ
fcis-31901	18	8	calibration	calibration	NOUN
fcis-31901	18	9	in	in	ADP
fcis-31901	18	10	gravityanomaly	gravityanomaly	PROPN
fcis-31901	18	11	fields	field	VERB
fcis-31901	18	12	2.1	2.1	NUM
fcis-31901	18	13	.	.	PUNCT
fcis-31901	19	1	data	datum	NOUN
fcis-31901	19	2	standardization	standardization	NOUN
fcis-31901	19	3	and	and	CCONJ
fcis-31901	19	4	distribution	distribution	NOUN
fcis-31901	19	5	visualization	visualization	NOUN
fcis-31901	19	6	the	the	DET
fcis-31901	19	7	raw	raw	ADJ
fcis-31901	19	8	dataset	dataset	NOUN
fcis-31901	19	9	consists	consist	NOUN
fcis-31901	19	10	of	of	ADP
fcis-31901	19	11	longitude	longitude	NOUN
fcis-31901	19	12	,	,	PUNCT
fcis-31901	19	13	latitude	latitude	NOUN
fcis-31901	19	14	,	,	PUNCT
fcis-31901	19	15	and	and	CCONJ
fcis-31901	19	16	gravity	gravity	NOUN
fcis-31901	19	17	anomaly	anomaly	NOUN
fcis-31901	19	18	measurements	measurement	NOUN
fcis-31901	19	19	,	,	PUNCT
fcis-31901	19	20	which	which	PRON
fcis-31901	19	21	were	be	AUX
fcis-31901	19	22	first	first	ADV
fcis-31901	19	23	visualized	visualize	VERB
fcis-31901	19	24	to	to	PART
fcis-31901	19	25	reveal	reveal	VERB
fcis-31901	19	26	the	the	DET
fcis-31901	19	27	spatial	spatial	ADJ
fcis-31901	19	28	heterogeneity	heterogeneity	NOUN
fcis-31901	19	29	of	of	ADP
fcis-31901	19	30	the	the	DET
fcis-31901	19	31	anomaly	anomaly	NOUN
fcis-31901	19	32	field	field	NOUN
fcis-31901	19	33	.	.	PUNCT
fcis-31901	20	1	the	the	DET
fcis-31901	20	2	distribution	distribution	NOUN
fcis-31901	20	3	map	map	NOUN
fcis-31901	20	4	highlights	highlight	NOUN
fcis-31901	20	5	regions	region	NOUN
fcis-31901	20	6	of	of	ADP
fcis-31901	20	7	significant	significant	ADJ
fcis-31901	20	8	fluctuations	fluctuation	NOUN
fcis-31901	20	9	,	,	PUNCT
fcis-31901	20	10	providing	provide	VERB
fcis-31901	20	11	an	an	DET
fcis-31901	20	12	intuitive	intuitive	ADJ
fcis-31901	20	13	reference	reference	NOUN
fcis-31901	20	14	for	for	ADP
fcis-31901	20	15	subsequent	subsequent	ADJ
fcis-31901	20	16	clustering	clustering	NOUN
fcis-31901	20	17	and	and	CCONJ
fcis-31901	20	18	classification	classification	NOUN
fcis-31901	20	19	tasks	task	NOUN
fcis-31901	20	20	.	.	PUNCT
fcis-31901	21	1	to	to	PART
fcis-31901	21	2	ensure	ensure	VERB
fcis-31901	21	3	comparability	comparability	NOUN
fcis-31901	21	4	among	among	ADP
fcis-31901	21	5	features	feature	NOUN
fcis-31901	21	6	with	with	ADP
fcis-31901	21	7	different	different	ADJ
fcis-31901	21	8	scales	scale	NOUN
fcis-31901	21	9	,	,	PUNCT
fcis-31901	21	10	the	the	DET
fcis-31901	21	11	three	three	NUM
fcis-31901	21	12	attributes	attribute	NOUN
fcis-31901	21	13	were	be	AUX
fcis-31901	21	14	normalized	normalize	VERB
fcis-31901	21	15	prior	prior	ADV
fcis-31901	21	16	to	to	ADP
fcis-31901	21	17	model	model	NOUN
fcis-31901	21	18	construction	construction	NOUN
fcis-31901	21	19	.	.	PUNCT
fcis-31901	22	1	the	the	DET
fcis-31901	22	2	standardization	standardization	NOUN
fcis-31901	22	3	procedure	procedure	NOUN
fcis-31901	22	4	is	be	AUX
fcis-31901	22	5	expressed	express	VERB
fcis-31901	22	6	as	as	ADP
fcis-31901	22	7	:	:	PUNCT
fcis-31901	22	8			PROPN
fcis-31901	22	9			PROPN
fcis-31901	22	10			PROPN
fcis-31901	22	11			PROPN
fcis-31901	22	12	,	,	PUNCT
fcis-31901	22	13	where	where	SCONJ
fcis-31901	22	14	max	max	PROPN
fcis-31901	22	15	,	,	PUNCT
fcis-31901	22	16	minij	minij	NOUN
fcis-31901	22	17	j	j	PROPN
fcis-31901	23	1	ij	ij	INTJ
fcis-31901	23	2	j	j	PROPN
fcis-31901	23	3	ij	ij	INTJ
fcis-31901	24	1	j	j	PROPN
fcis-31901	24	2	ij	ij	INTJ
fcis-31901	24	3	j	j	PROPN
fcis-31901	24	4	j	j	PROPN
fcis-31901	25	1	x	x	VERB
fcis-31901	26	1	m	m	VERB
fcis-31901	26	2	x	x	X
fcis-31901	26	3	m	m	VERB
fcis-31901	26	4	x	x	X
fcis-31901	26	5	m	m	VERB
fcis-31901	26	6	x	x	X
fcis-31901	26	7	m	m	VERB
fcis-31901	26	8	m	m	VERB
fcis-31901	26	9			NOUN
fcis-31901	26	10			NUM
fcis-31901	26	11			NUM
fcis-31901	26	12			PROPN
fcis-31901	26	13			NOUN
fcis-31901	26	14	(	(	PUNCT
fcis-31901	26	15	1	1	NUM
fcis-31901	26	16	)	)	PUNCT
fcis-31901	26	17	this	this	DET
fcis-31901	26	18	transformation	transformation	NOUN
fcis-31901	26	19	aligns	align	VERB
fcis-31901	26	20	the	the	DET
fcis-31901	26	21	ranges	range	NOUN
fcis-31901	26	22	of	of	ADP
fcis-31901	26	23	longitude	longitude	NOUN
fcis-31901	26	24	,	,	PUNCT
fcis-31901	26	25	latitude	latitude	NOUN
fcis-31901	26	26	,	,	PUNCT
fcis-31901	26	27	and	and	CCONJ
fcis-31901	26	28	anomaly	anomaly	NOUN
fcis-31901	26	29	values	value	NOUN
fcis-31901	26	30	,	,	PUNCT
fcis-31901	26	31	thereby	thereby	ADV
fcis-31901	26	32	eliminating	eliminate	VERB
fcis-31901	26	33	the	the	DET
fcis-31901	26	34	bias	bias	NOUN
fcis-31901	26	35	introduced	introduce	VERB
fcis-31901	26	36	by	by	ADP
fcis-31901	26	37	heterogeneous	heterogeneous	ADJ
fcis-31901	26	38	magnitudes	magnitude	NOUN
fcis-31901	26	39	and	and	CCONJ
fcis-31901	26	40	establishing	establish	VERB
fcis-31901	26	41	a	a	DET
fcis-31901	26	42	consistent	consistent	ADJ
fcis-31901	26	43	basis	basis	NOUN
fcis-31901	26	44	for	for	ADP
fcis-31901	26	45	hierarchical	hierarchical	ADJ
fcis-31901	26	46	clustering	clustering	NOUN
fcis-31901	26	47	.	.	PUNCT
fcis-31901	27	1	as	as	SCONJ
fcis-31901	27	2	shown	show	VERB
fcis-31901	27	3	in	in	ADP
fcis-31901	27	4	figure	figure	NOUN
fcis-31901	27	5	1	1	NUM
fcis-31901	27	6	,	,	PUNCT
fcis-31901	27	7	the	the	DET
fcis-31901	27	8	processed	process	VERB
fcis-31901	27	9	distribution	distribution	NOUN
fcis-31901	27	10	map	map	NOUN
fcis-31901	27	11	clearly	clearly	ADV
fcis-31901	27	12	demonstrates	demonstrate	VERB
fcis-31901	27	13	localized	localized	ADJ
fcis-31901	27	14	gravity	gravity	NOUN
fcis-31901	27	15	variations	variation	NOUN
fcis-31901	27	16	,	,	PUNCT
fcis-31901	27	17	which	which	PRON
fcis-31901	27	18	are	be	AUX
fcis-31901	27	19	critical	critical	ADJ
fcis-31901	27	20	for	for	ADP
fcis-31901	27	21	the	the	DET
fcis-31901	27	22	identification	identification	NOUN
fcis-31901	27	23	of	of	ADP
fcis-31901	27	24	coherent	coherent	ADJ
fcis-31901	27	25	regions	region	NOUN
fcis-31901	27	26	of	of	ADP
fcis-31901	27	27	acceptance	acceptance	NOUN
fcis-31901	27	28	.	.	PUNCT
fcis-31901	28	1	to	to	PART
fcis-31901	28	2	identify	identify	VERB
fcis-31901	28	3	coherent	coherent	ADJ
fcis-31901	28	4	regions	region	NOUN
fcis-31901	28	5	within	within	ADP
fcis-31901	28	6	the	the	DET
fcis-31901	28	7	gravity	gravity	NOUN
fcis-31901	28	8	anomaly	anomaly	NOUN
fcis-31901	28	9	field	field	NOUN
fcis-31901	28	10	,	,	PUNCT
fcis-31901	28	11	an	an	DET
fcis-31901	28	12	agglomerative	agglomerative	ADJ
fcis-31901	28	13	hierarchical	hierarchical	ADJ
fcis-31901	28	14	clustering	clustering	NOUN
fcis-31901	28	15	(	(	PUNCT
fcis-31901	28	16	agnes	agne	NOUN
fcis-31901	28	17	)	)	PUNCT
fcis-31901	28	18	framework	framework	NOUN
fcis-31901	28	19	was	be	AUX
fcis-31901	28	20	applied	apply	VERB
fcis-31901	28	21	using	use	VERB
fcis-31901	28	22	the	the	DET
fcis-31901	28	23	ward	ward	NOUN
fcis-31901	28	24	linkage	linkage	NOUN
fcis-31901	28	25	criterion	criterion	NOUN
fcis-31901	28	26	.	.	PUNCT
fcis-31901	29	1	this	this	DET
fcis-31901	29	2	method	method	NOUN
fcis-31901	29	3	iteratively	iteratively	ADV
fcis-31901	29	4	merges	merge	VERB
fcis-31901	29	5	clusters	cluster	NOUN
fcis-31901	29	6	in	in	ADP
fcis-31901	29	7	a	a	DET
fcis-31901	29	8	bottom	bottom	ADJ
fcis-31901	29	9	-	-	PUNCT
fcis-31901	29	10	up	up	ADP
fcis-31901	29	11	manner	manner	NOUN
fcis-31901	29	12	by	by	ADP
fcis-31901	29	13	minimizing	minimize	VERB
fcis-31901	29	14	the	the	DET
fcis-31901	29	15	total	total	ADJ
fcis-31901	29	16	within	within	ADP
fcis-31901	29	17	-	-	PUNCT
fcis-31901	29	18	cluster	cluster	NOUN
fcis-31901	29	19	variance	variance	NOUN
fcis-31901	29	20	,	,	PUNCT
fcis-31901	29	21	thereby	thereby	ADV
fcis-31901	29	22	ensuring	ensure	VERB
fcis-31901	29	23	compact	compact	ADJ
fcis-31901	29	24	and	and	CCONJ
fcis-31901	29	25	well	well	ADV
fcis-31901	29	26	-	-	PUNCT
fcis-31901	29	27	separated	separate	VERB
fcis-31901	29	28	groupings	grouping	NOUN
fcis-31901	29	29	.	.	PUNCT
fcis-31901	30	1	by	by	ADP
fcis-31901	30	2	selecting	select	VERB
fcis-31901	30	3	an	an	DET
fcis-31901	30	4	appropriate	appropriate	ADJ
fcis-31901	30	5	threshold	threshold	NOUN
fcis-31901	30	6	on	on	ADP
fcis-31901	30	7	the	the	DET
fcis-31901	30	8	resulting	result	VERB
fcis-31901	30	9	dendrogram	dendrogram	NOUN
fcis-31901	30	10	,	,	PUNCT
fcis-31901	30	11	distinct	distinct	ADJ
fcis-31901	30	12	region	region	NOUN
fcis-31901	30	13	labels	label	NOUN
fcis-31901	30	14	were	be	AUX
fcis-31901	30	15	derived	derive	VERB
fcis-31901	30	16	and	and	CCONJ
fcis-31901	30	17	appended	append	VERB
fcis-31901	30	18	to	to	ADP
fcis-31901	30	19	the	the	DET
fcis-31901	30	20	dataset	dataset	NOUN
fcis-31901	30	21	,	,	PUNCT
fcis-31901	30	22	providing	provide	VERB
fcis-31901	30	23	a	a	DET
fcis-31901	30	24	structured	structured	ADJ
fcis-31901	30	25	partition	partition	NOUN
fcis-31901	30	26	of	of	ADP
fcis-31901	30	27	the	the	DET
fcis-31901	30	28	anomaly	anomaly	NOUN
fcis-31901	30	29	field	field	NOUN
fcis-31901	30	30	.	.	PUNCT
fcis-31901	31	1	47	47	NUM
fcis-31901	31	2	figure	figure	NOUN
fcis-31901	31	3	1	1	NUM
fcis-31901	31	4	.	.	PUNCT
fcis-31901	31	5	distribution	distribution	NOUN
fcis-31901	31	6	of	of	ADP
fcis-31901	31	7	gravity	gravity	NOUN
fcis-31901	31	8	anomaly	anomaly	NOUN
fcis-31901	31	9	data	datum	NOUN
fcis-31901	31	10	across	across	ADP
fcis-31901	31	11	the	the	DET
fcis-31901	31	12	study	study	NOUN
fcis-31901	31	13	region	region	NOUN
fcis-31901	31	14	2.2	2.2	NUM
fcis-31901	31	15	.	.	PUNCT
fcis-31901	32	1	hierarchical	hierarchical	ADJ
fcis-31901	32	2	clustering	clustering	NOUN
fcis-31901	32	3	for	for	ADP
fcis-31901	32	4	regional	regional	ADJ
fcis-31901	32	5	partitioning	partitioning	ADJ
fcis-31901	32	6	figure	figure	NOUN
fcis-31901	32	7	2	2	NUM
fcis-31901	32	8	.	.	PUNCT
fcis-31901	32	9	visualization	visualization	NOUN
fcis-31901	32	10	of	of	ADP
fcis-31901	32	11	regional	regional	ADJ
fcis-31901	32	12	partitioning	partitioning	NOUN
fcis-31901	32	13	based	base	VERB
fcis-31901	32	14	on	on	ADP
fcis-31901	32	15	ward	ward	NOUN
fcis-31901	32	16	hierarchical	hierarchical	ADJ
fcis-31901	32	17	clustering	cluster	VERB
fcis-31901	32	18	the	the	DET
fcis-31901	32	19	clustering	clustering	ADJ
fcis-31901	32	20	results	result	NOUN
fcis-31901	32	21	reveal	reveal	VERB
fcis-31901	32	22	four	four	NUM
fcis-31901	32	23	major	major	ADJ
fcis-31901	32	24	regions	region	NOUN
fcis-31901	32	25	with	with	ADP
fcis-31901	32	26	distinct	distinct	ADJ
fcis-31901	32	27	geospatial	geospatial	ADJ
fcis-31901	32	28	patterns	pattern	NOUN
fcis-31901	32	29	,	,	PUNCT
fcis-31901	32	30	each	each	PRON
fcis-31901	32	31	exhibiting	exhibit	VERB
fcis-31901	32	32	consistent	consistent	ADJ
fcis-31901	32	33	gravity	gravity	NOUN
fcis-31901	32	34	anomaly	anomaly	NOUN
fcis-31901	32	35	characteristics	characteristic	NOUN
fcis-31901	32	36	.	.	PUNCT
fcis-31901	33	1	such	such	ADJ
fcis-31901	33	2	regionalization	regionalization	NOUN
fcis-31901	33	3	enhances	enhance	VERB
fcis-31901	33	4	interpretability	interpretability	NOUN
fcis-31901	33	5	and	and	CCONJ
fcis-31901	33	6	supports	support	VERB
fcis-31901	33	7	subsequent	subsequent	ADJ
fcis-31901	33	8	classification	classification	NOUN
fcis-31901	33	9	tasks	task	NOUN
fcis-31901	33	10	by	by	ADP
fcis-31901	33	11	providing	provide	VERB
fcis-31901	33	12	reliable	reliable	ADJ
fcis-31901	33	13	calibration	calibration	NOUN
fcis-31901	33	14	labels	label	NOUN
fcis-31901	33	15	.	.	PUNCT
fcis-31901	34	1	as	as	SCONJ
fcis-31901	34	2	shown	show	VERB
fcis-31901	34	3	in	in	ADP
fcis-31901	34	4	figure	figure	NOUN
fcis-31901	34	5	2	2	NUM
fcis-31901	34	6	,	,	PUNCT
fcis-31901	34	7	the	the	DET
fcis-31901	34	8	four	four	NUM
fcis-31901	34	9	regions	region	NOUN
fcis-31901	34	10	are	be	AUX
fcis-31901	34	11	visualized	visualize	VERB
fcis-31901	34	12	with	with	ADP
fcis-31901	34	13	distinct	distinct	ADJ
fcis-31901	34	14	colors	color	NOUN
fcis-31901	34	15	,	,	PUNCT
fcis-31901	34	16	highlighting	highlight	VERB
fcis-31901	34	17	clear	clear	ADJ
fcis-31901	34	18	spatial	spatial	ADJ
fcis-31901	34	19	boundaries	boundary	NOUN
fcis-31901	34	20	and	and	CCONJ
fcis-31901	34	21	heterogeneity	heterogeneity	NOUN
fcis-31901	34	22	across	across	ADP
fcis-31901	34	23	the	the	DET
fcis-31901	34	24	study	study	NOUN
fcis-31901	34	25	area	area	NOUN
fcis-31901	34	26	.	.	PUNCT
fcis-31901	35	1	3	3	X
fcis-31901	35	2	.	.	X
fcis-31901	35	3	comparative	comparative	ADJ
fcis-31901	35	4	classification	classification	NOUN
fcis-31901	35	5	modeling	modeling	NOUN
fcis-31901	35	6	with	with	ADP
fcis-31901	35	7	multiple	multiple	ADJ
fcis-31901	35	8	algorithms	algorithm	NOUN
fcis-31901	35	9	for	for	ADP
fcis-31901	35	10	the	the	DET
fcis-31901	35	11	classification	classification	NOUN
fcis-31901	35	12	task	task	NOUN
fcis-31901	35	13	,	,	PUNCT
fcis-31901	35	14	longitude	longitude	NOUN
fcis-31901	35	15	,	,	PUNCT
fcis-31901	35	16	latitude	latitude	NOUN
fcis-31901	35	17	,	,	PUNCT
fcis-31901	35	18	and	and	CCONJ
fcis-31901	35	19	gravity	gravity	NOUN
fcis-31901	35	20	anomaly	anomaly	NOUN
fcis-31901	35	21	values	value	NOUN
fcis-31901	35	22	were	be	AUX
fcis-31901	35	23	selected	select	VERB
fcis-31901	35	24	as	as	SCONJ
fcis-31901	35	25	the	the	DET
fcis-31901	35	26	input	input	NOUN
fcis-31901	35	27	feature	feature	NOUN
fcis-31901	35	28	set	set	VERB
fcis-31901	35	29	x	x	SYM
fcis-31901	35	30	,	,	PUNCT
fcis-31901	35	31	while	while	SCONJ
fcis-31901	35	32	the	the	DET
fcis-31901	35	33	region	region	NOUN
fcis-31901	35	34	labels	label	VERB
fcis-31901	35	35	derived	derive	VERB
fcis-31901	35	36	from	from	ADP
fcis-31901	35	37	clustering	cluster	VERB
fcis-31901	35	38	served	serve	VERB
fcis-31901	35	39	as	as	ADP
fcis-31901	35	40	the	the	DET
fcis-31901	35	41	target	target	NOUN
fcis-31901	35	42	variable	variable	ADJ
fcis-31901	35	43	y	y	PROPN
fcis-31901	35	44	.	.	PUNCT
fcis-31901	36	1	this	this	DET
fcis-31901	36	2	design	design	NOUN
fcis-31901	36	3	ensures	ensure	VERB
fcis-31901	36	4	that	that	SCONJ
fcis-31901	36	5	both	both	CCONJ
fcis-31901	36	6	spatial	spatial	ADJ
fcis-31901	36	7	coordinates	coordinate	NOUN
fcis-31901	36	8	and	and	CCONJ
fcis-31901	36	9	anomaly	anomaly	NOUN
fcis-31901	36	10	intensity	intensity	NOUN
fcis-31901	36	11	contribute	contribute	VERB
fcis-31901	36	12	jointly	jointly	ADV
fcis-31901	36	13	to	to	ADP
fcis-31901	36	14	the	the	DET
fcis-31901	36	15	prediction	prediction	NOUN
fcis-31901	36	16	of	of	ADP
fcis-31901	36	17	regional	regional	ADJ
fcis-31901	36	18	categories	category	NOUN
fcis-31901	36	19	.	.	PUNCT
fcis-31901	37	1	to	to	PART
fcis-31901	37	2	obtain	obtain	VERB
fcis-31901	37	3	robust	robust	ADJ
fcis-31901	37	4	estimates	estimate	NOUN
fcis-31901	37	5	,	,	PUNCT
fcis-31901	37	6	the	the	DET
fcis-31901	37	7	dataset	dataset	NOUN
fcis-31901	37	8	was	be	AUX
fcis-31901	37	9	randomly	randomly	ADV
fcis-31901	37	10	partitioned	partition	VERB
fcis-31901	37	11	into	into	ADP
fcis-31901	37	12	training	training	NOUN
fcis-31901	37	13	and	and	CCONJ
fcis-31901	37	14	testing	testing	NOUN
fcis-31901	37	15	subsets	subset	NOUN
fcis-31901	37	16	,	,	PUNCT
fcis-31901	37	17	with	with	ADP
fcis-31901	37	18	80	80	NUM
fcis-31901	37	19	%	%	NOUN
fcis-31901	37	20	used	use	VERB
fcis-31901	37	21	for	for	ADP
fcis-31901	37	22	model	model	NOUN
fcis-31901	37	23	fitting	fitting	ADJ
fcis-31901	37	24	and	and	CCONJ
fcis-31901	37	25	20	20	NUM
fcis-31901	37	26	%	%	NOUN
fcis-31901	37	27	reserved	reserve	VERB
fcis-31901	37	28	for	for	ADP
fcis-31901	37	29	evaluation	evaluation	NOUN
fcis-31901	37	30	,	,	PUNCT
fcis-31901	37	31	ensuring	ensure	VERB
fcis-31901	37	32	reproducibility	reproducibility	NOUN
fcis-31901	37	33	through	through	ADP
fcis-31901	37	34	fixed	fix	VERB
fcis-31901	37	35	partitioning	partitioning	NOUN
fcis-31901	37	36	.	.	PUNCT
fcis-31901	38	1	a	a	DET
fcis-31901	38	2	random	random	ADJ
fcis-31901	38	3	forest	forest	NOUN
fcis-31901	38	4	(	(	PUNCT
fcis-31901	38	5	rf	rf	NOUN
fcis-31901	38	6	)	)	PUNCT
fcis-31901	38	7	classifier	classifier	NOUN
fcis-31901	38	8	was	be	AUX
fcis-31901	38	9	constructed	construct	VERB
fcis-31901	38	10	as	as	ADP
fcis-31901	38	11	the	the	DET
fcis-31901	38	12	primary	primary	ADJ
fcis-31901	38	13	model	model	NOUN
fcis-31901	38	14	.	.	PUNCT
fcis-31901	39	1	rf	rf	PRON
fcis-31901	39	2	is	be	AUX
fcis-31901	39	3	an	an	DET
fcis-31901	39	4	ensemble	ensemble	ADJ
fcis-31901	39	5	method	method	NOUN
fcis-31901	39	6	that	that	PRON
fcis-31901	39	7	aggregates	aggregate	VERB
fcis-31901	39	8	the	the	DET
fcis-31901	39	9	predictions	prediction	NOUN
fcis-31901	39	10	of	of	ADP
fcis-31901	39	11	multiple	multiple	ADJ
fcis-31901	39	12	decision	decision	NOUN
fcis-31901	39	13	trees	tree	NOUN
fcis-31901	39	14	,	,	PUNCT
fcis-31901	39	15	each	each	PRON
fcis-31901	39	16	trained	train	VERB
fcis-31901	39	17	on	on	ADP
fcis-31901	39	18	bootstrap	bootstrap	NOUN
fcis-31901	39	19	samples	sample	NOUN
fcis-31901	39	20	with	with	ADP
fcis-31901	39	21	feature	feature	NOUN
fcis-31901	39	22	randomness	randomness	NOUN
fcis-31901	39	23	.	.	PUNCT
fcis-31901	40	1	for	for	ADP
fcis-31901	40	2	an	an	DET
fcis-31901	40	3	ensemble	ensemble	NOUN
fcis-31901	40	4	of	of	ADP
fcis-31901	40	5	t	t	PROPN
fcis-31901	40	6	trees	tree	NOUN
fcis-31901	40	7	,	,	PUNCT
fcis-31901	40	8	the	the	DET
fcis-31901	40	9	predicted	predict	VERB
fcis-31901	40	10	class	class	NOUN
fcis-31901	40	11	ŷ	ŷ	NUM
fcis-31901	40	12	for	for	ADP
fcis-31901	40	13	an	an	DET
fcis-31901	40	14	input	input	NOUN
fcis-31901	40	15	x	x	PUNCT
fcis-31901	40	16	is	be	AUX
fcis-31901	40	17	given	give	VERB
fcis-31901	40	18	by	by	ADP
fcis-31901	40	19	majority	majority	NOUN
fcis-31901	40	20	voting	voting	NOUN
fcis-31901	40	21	:	:	PUNCT
fcis-31901	41	1			X
fcis-31901	41	2			PROPN
fcis-31901	41	3	1	1	NUM
fcis-31901	41	4	ˆ	ˆ	NOUN
fcis-31901	41	5	arg	arg	NOUN
fcis-31901	41	6	max	max	PROPN
fcis-31901	41	7	(	(	PUNCT
fcis-31901	41	8	)	)	PUNCT
fcis-31901	41	9	,	,	PUNCT
fcis-31901	41	10	t	t	PROPN
fcis-31901	41	11	t	t	PROPN
fcis-31901	41	12	c	c	PROPN
fcis-31901	41	13	t	t	PROPN
fcis-31901	41	14	y	y	NOUN
fcis-31901	41	15	h	h	NOUN
fcis-31901	41	16	x	x	X
fcis-31901	41	17	c	c	PROPN
fcis-31901	41	18			NOUN
fcis-31901	41	19			PROPN
fcis-31901	41	20			NUM
fcis-31901	41	21	1	1	PROPN
fcis-31901	41	22	c	c	NOUN
fcis-31901	41	23	(	(	PUNCT
fcis-31901	41	24	2	2	NUM
fcis-31901	41	25	)	)	PUNCT
fcis-31901	41	26	where	where	SCONJ
fcis-31901	41	27	(	(	PUNCT
fcis-31901	41	28	)	)	PUNCT
fcis-31901	41	29	th	th	X
fcis-31901	41	30	x	x	PUNCT
fcis-31901	41	31	denotes	denote	VERB
fcis-31901	41	32	the	the	DET
fcis-31901	41	33	prediction	prediction	NOUN
fcis-31901	41	34	of	of	ADP
fcis-31901	41	35	the	the	DET
fcis-31901	41	36	t	t	PROPN
fcis-31901	41	37	-th	-th	PUNCT
fcis-31901	41	38	tree	tree	NOUN
fcis-31901	41	39	.	.	PUNCT
fcis-31901	42	1	this	this	DET
fcis-31901	42	2	ensemble	ensemble	ADJ
fcis-31901	42	3	structure	structure	NOUN
fcis-31901	42	4	reduces	reduce	VERB
fcis-31901	42	5	variance	variance	NOUN
fcis-31901	42	6	and	and	CCONJ
fcis-31901	42	7	improves	improve	VERB
fcis-31901	42	8	generalization	generalization	NOUN
fcis-31901	42	9	over	over	ADP
fcis-31901	42	10	single	single	ADJ
fcis-31901	42	11	-	-	PUNCT
fcis-31901	42	12	tree	tree	NOUN
fcis-31901	42	13	models	model	NOUN
fcis-31901	42	14	.	.	PUNCT
fcis-31901	43	1	to	to	ADP
fcis-31901	43	2	benchmark	benchmark	NOUN
fcis-31901	43	3	performance	performance	NOUN
fcis-31901	43	4	,	,	PUNCT
fcis-31901	43	5	three	three	NUM
fcis-31901	43	6	widely	widely	ADV
fcis-31901	43	7	used	use	VERB
fcis-31901	43	8	classifiers	classifier	NOUN
fcis-31901	43	9	48	48	NUM
fcis-31901	43	10	were	be	AUX
fcis-31901	43	11	introduced	introduce	VERB
fcis-31901	43	12	for	for	ADP
fcis-31901	43	13	comparison	comparison	NOUN
fcis-31901	43	14	.	.	PUNCT
fcis-31901	44	1	the	the	DET
fcis-31901	44	2	support	support	NOUN
fcis-31901	44	3	vector	vector	NOUN
fcis-31901	44	4	machine	machine	NOUN
fcis-31901	44	5	(	(	PUNCT
fcis-31901	44	6	svm	svm	PROPN
fcis-31901	44	7	)	)	PUNCT
fcis-31901	44	8	seeks	seek	VERB
fcis-31901	44	9	an	an	DET
fcis-31901	44	10	optimal	optimal	ADJ
fcis-31901	44	11	separating	separate	VERB
fcis-31901	44	12	hyperplane	hyperplane	NOUN
fcis-31901	44	13	that	that	PRON
fcis-31901	44	14	maximizes	maximize	VERB
fcis-31901	44	15	the	the	DET
fcis-31901	44	16	margin	margin	NOUN
fcis-31901	44	17	between	between	ADP
fcis-31901	44	18	two	two	NUM
fcis-31901	44	19	classes	class	NOUN
fcis-31901	44	20	,	,	PUNCT
fcis-31901	44	21	expressed	express	VERB
fcis-31901	44	22	as	as	ADP
fcis-31901	44	23	:	:	PUNCT
fcis-31901	44	24			PROPN
fcis-31901	44	25	2	2	NOUN
fcis-31901	44	26	,	,	PUNCT
fcis-31901	44	27	1	1	NUM
fcis-31901	44	28	min	min	PROPN
fcis-31901	44	29	s.t	s.t	PROPN
fcis-31901	44	30	.	.	PROPN
fcis-31901	44	31	1	1	NUM
fcis-31901	44	32	,	,	PUNCT
fcis-31901	44	33	,	,	PUNCT
fcis-31901	44	34	2w	2w	PROPN
fcis-31901	44	35	b	b	NOUN
fcis-31901	44	36	i	i	PRON
fcis-31901	44	37	iw	iw	VERB
fcis-31901	44	38	y	y	PROPN
fcis-31901	44	39	w	w	PROPN
fcis-31901	44	40	x	x	PROPN
fcis-31901	44	41	b	b	PROPN
fcis-31901	44	42	i	i	NUM
fcis-31901	44	43			NUM
fcis-31901	44	44	‖	‖	PROPN
fcis-31901	44	45	‖	‖	PROPN
fcis-31901	44	46	•	•	NUM
fcis-31901	44	47	(	(	PUNCT
fcis-31901	44	48	3	3	X
fcis-31901	44	49	)	)	PUNCT
fcis-31901	44	50	figure	figure	NOUN
fcis-31901	44	51	3	3	NUM
fcis-31901	44	52	.	.	PUNCT
fcis-31901	44	53	accuracy	accuracy	NOUN
fcis-31901	44	54	comparison	comparison	NOUN
fcis-31901	44	55	of	of	ADP
fcis-31901	44	56	random	random	ADJ
fcis-31901	44	57	forest	forest	NOUN
fcis-31901	44	58	,	,	PUNCT
fcis-31901	44	59	svm	svm	PROPN
fcis-31901	44	60	,	,	PUNCT
fcis-31901	44	61	knn	knn	PROPN
fcis-31901	44	62	,	,	PUNCT
fcis-31901	44	63	and	and	CCONJ
fcis-31901	44	64	decision	decision	NOUN
fcis-31901	44	65	tree	tree	NOUN
fcis-31901	44	66	classifiers	classifier	NOUN
fcis-31901	44	67	where	where	SCONJ
fcis-31901	44	68	w	w	NOUN
fcis-31901	44	69	and	and	CCONJ
fcis-31901	44	70	b	b	NOUN
fcis-31901	44	71	define	define	VERB
fcis-31901	44	72	the	the	DET
fcis-31901	44	73	hyperplane	hyperplane	NOUN
fcis-31901	44	74	.	.	PUNCT
fcis-31901	45	1	the	the	DET
fcis-31901	45	2	k	k	NOUN
fcis-31901	45	3	-	-	PUNCT
fcis-31901	45	4	nearest	near	ADJ
fcis-31901	45	5	neighbor	neighbor	NOUN
fcis-31901	45	6	(	(	PUNCT
fcis-31901	45	7	knn	knn	PROPN
fcis-31901	45	8	)	)	PUNCT
fcis-31901	45	9	algorithm	algorithm	NOUN
fcis-31901	45	10	performs	perform	VERB
fcis-31901	45	11	classification	classification	NOUN
fcis-31901	45	12	by	by	ADP
fcis-31901	45	13	majority	majority	NOUN
fcis-31901	45	14	voting	voting	NOUN
fcis-31901	45	15	among	among	ADP
fcis-31901	45	16	the	the	DET
fcis-31901	45	17	k	k	PROPN
fcis-31901	45	18	closest	close	ADJ
fcis-31901	45	19	training	training	NOUN
fcis-31901	45	20	samples	sample	NOUN
fcis-31901	45	21	to	to	ADP
fcis-31901	45	22	a	a	DET
fcis-31901	45	23	query	query	NOUN
fcis-31901	45	24	point	point	NOUN
fcis-31901	45	25	,	,	PUNCT
fcis-31901	45	26	with	with	ADP
fcis-31901	45	27	distance	distance	NOUN
fcis-31901	45	28	typically	typically	ADV
fcis-31901	45	29	measured	measure	VERB
fcis-31901	45	30	in	in	ADP
fcis-31901	45	31	euclidean	euclidean	ADJ
fcis-31901	45	32	space	space	NOUN
fcis-31901	45	33	.	.	PUNCT
fcis-31901	46	1	finally	finally	ADV
fcis-31901	46	2	,	,	PUNCT
fcis-31901	46	3	the	the	DET
fcis-31901	46	4	decision	decision	NOUN
fcis-31901	46	5	tree	tree	NOUN
fcis-31901	46	6	classifier	classifier	NOUN
fcis-31901	46	7	recursively	recursively	ADV
fcis-31901	46	8	partitions	partition	VERB
fcis-31901	46	9	the	the	DET
fcis-31901	46	10	feature	feature	NOUN
fcis-31901	46	11	space	space	NOUN
fcis-31901	46	12	by	by	ADP
fcis-31901	46	13	selecting	select	VERB
fcis-31901	46	14	attributes	attribute	NOUN
fcis-31901	46	15	that	that	PRON
fcis-31901	46	16	maximize	maximize	VERB
fcis-31901	46	17	information	information	NOUN
fcis-31901	46	18	gain	gain	NOUN
fcis-31901	46	19	or	or	CCONJ
fcis-31901	46	20	minimize	minimize	VERB
fcis-31901	46	21	gini	gini	NOUN
fcis-31901	46	22	impurity	impurity	NOUN
fcis-31901	46	23	at	at	ADP
fcis-31901	46	24	each	each	DET
fcis-31901	46	25	split	split	NOUN
fcis-31901	46	26	,	,	PUNCT
fcis-31901	46	27	thereby	thereby	ADV
fcis-31901	46	28	forming	form	VERB
fcis-31901	46	29	interpretable	interpretable	ADJ
fcis-31901	46	30	hierarchical	hierarchical	ADJ
fcis-31901	46	31	rules	rule	NOUN
fcis-31901	46	32	for	for	ADP
fcis-31901	46	33	classification	classification	NOUN
fcis-31901	46	34	.	.	PUNCT
fcis-31901	47	1	the	the	DET
fcis-31901	47	2	models	model	NOUN
fcis-31901	47	3	were	be	AUX
fcis-31901	47	4	assessed	assess	VERB
fcis-31901	47	5	using	use	VERB
fcis-31901	47	6	accuracy	accuracy	NOUN
fcis-31901	47	7	as	as	ADP
fcis-31901	47	8	the	the	DET
fcis-31901	47	9	primary	primary	ADJ
fcis-31901	47	10	metric	metric	NOUN
fcis-31901	47	11	,	,	PUNCT
fcis-31901	47	12	alongside	alongside	ADP
fcis-31901	47	13	auxiliary	auxiliary	ADJ
fcis-31901	47	14	indicators	indicator	NOUN
fcis-31901	47	15	such	such	ADJ
fcis-31901	47	16	as	as	ADP
fcis-31901	47	17	precision	precision	NOUN
fcis-31901	47	18	,	,	PUNCT
fcis-31901	47	19	recall	recall	NOUN
fcis-31901	47	20	,	,	PUNCT
fcis-31901	47	21	and	and	CCONJ
fcis-31901	47	22	1f	1f	NUM
fcis-31901	47	23	-score	-score	NOUN
fcis-31901	47	24	to	to	PART
fcis-31901	47	25	provide	provide	VERB
fcis-31901	47	26	a	a	DET
fcis-31901	47	27	balanced	balanced	ADJ
fcis-31901	47	28	evaluation	evaluation	NOUN
fcis-31901	47	29	of	of	ADP
fcis-31901	47	30	predictive	predictive	ADJ
fcis-31901	47	31	performance	performance	NOUN
fcis-31901	47	32	.	.	PUNCT
fcis-31901	48	1	as	as	SCONJ
fcis-31901	48	2	shown	show	VERB
fcis-31901	48	3	in	in	ADP
fcis-31901	48	4	figure	figure	NOUN
fcis-31901	48	5	3	3	NUM
fcis-31901	48	6	,	,	PUNCT
fcis-31901	48	7	the	the	DET
fcis-31901	48	8	random	random	ADJ
fcis-31901	48	9	forest	forest	NOUN
fcis-31901	48	10	model	model	NOUN
fcis-31901	48	11	achieved	achieve	VERB
fcis-31901	48	12	the	the	DET
fcis-31901	48	13	highest	high	ADJ
fcis-31901	48	14	accuracy	accuracy	NOUN
fcis-31901	48	15	,	,	PUNCT
fcis-31901	48	16	outperforming	outperforming	NOUN
fcis-31901	48	17	svm	svm	PROPN
fcis-31901	48	18	,	,	PUNCT
fcis-31901	48	19	knn	knn	PROPN
fcis-31901	48	20	,	,	PUNCT
fcis-31901	48	21	and	and	CCONJ
fcis-31901	48	22	the	the	DET
fcis-31901	48	23	single	single	ADJ
fcis-31901	48	24	decision	decision	NOUN
fcis-31901	48	25	tree	tree	NOUN
fcis-31901	48	26	classifier	classifier	NOUN
fcis-31901	48	27	,	,	PUNCT
fcis-31901	48	28	thereby	thereby	ADV
fcis-31901	48	29	confirming	confirm	VERB
fcis-31901	48	30	its	its	PRON
fcis-31901	48	31	robustness	robustness	NOUN
fcis-31901	48	32	and	and	CCONJ
fcis-31901	48	33	suitability	suitability	NOUN
fcis-31901	48	34	for	for	ADP
fcis-31901	48	35	anomaly	anomaly	NOUN
fcis-31901	48	36	-	-	PUNCT
fcis-31901	48	37	based	base	VERB
fcis-31901	48	38	regional	regional	ADJ
fcis-31901	48	39	prediction	prediction	NOUN
fcis-31901	48	40	.	.	PUNCT
fcis-31901	49	1	4	4	X
fcis-31901	49	2	.	.	X
fcis-31901	49	3	model	model	NOUN
fcis-31901	49	4	transferability	transferability	NOUN
fcis-31901	49	5	and	and	CCONJ
fcis-31901	49	6	applicability	applicability	NOUN
fcis-31901	49	7	assessment	assessment	NOUN
fcis-31901	49	8	to	to	PART
fcis-31901	49	9	validate	validate	VERB
fcis-31901	49	10	the	the	DET
fcis-31901	49	11	generalization	generalization	NOUN
fcis-31901	49	12	capability	capability	NOUN
fcis-31901	49	13	of	of	ADP
fcis-31901	49	14	the	the	DET
fcis-31901	49	15	proposed	propose	VERB
fcis-31901	49	16	classification	classification	NOUN
fcis-31901	49	17	framework	framework	NOUN
fcis-31901	49	18	,	,	PUNCT
fcis-31901	49	19	a	a	DET
fcis-31901	49	20	second	second	ADJ
fcis-31901	49	21	dataset	dataset	NOUN
fcis-31901	49	22	of	of	ADP
fcis-31901	49	23	gravity	gravity	NOUN
fcis-31901	49	24	anomaly	anomaly	NOUN
fcis-31901	49	25	measurements	measurement	NOUN
fcis-31901	49	26	was	be	AUX
fcis-31901	49	27	introduced	introduce	VERB
fcis-31901	49	28	.	.	PUNCT
fcis-31901	50	1	this	this	DET
fcis-31901	50	2	dataset	dataset	NOUN
fcis-31901	50	3	was	be	AUX
fcis-31901	50	4	visualized	visualize	VERB
fcis-31901	50	5	to	to	PART
fcis-31901	50	6	examine	examine	VERB
fcis-31901	50	7	the	the	DET
fcis-31901	50	8	spatial	spatial	ADJ
fcis-31901	50	9	distribution	distribution	NOUN
fcis-31901	50	10	of	of	ADP
fcis-31901	50	11	anomalies	anomaly	NOUN
fcis-31901	50	12	and	and	CCONJ
fcis-31901	50	13	to	to	PART
fcis-31901	50	14	highlight	highlight	VERB
fcis-31901	50	15	potential	potential	ADJ
fcis-31901	50	16	regional	regional	ADJ
fcis-31901	50	17	heterogeneity	heterogeneity	NOUN
fcis-31901	50	18	,	,	PUNCT
fcis-31901	50	19	as	as	SCONJ
fcis-31901	50	20	shown	show	VERB
fcis-31901	50	21	in	in	ADP
fcis-31901	50	22	figure	figure	NOUN
fcis-31901	50	23	4	4	NUM
fcis-31901	50	24	.	.	PUNCT
fcis-31901	51	1	the	the	DET
fcis-31901	51	2	previously	previously	ADV
fcis-31901	51	3	established	establish	VERB
fcis-31901	51	4	best	well	ADV
fcis-31901	51	5	-	-	PUNCT
fcis-31901	51	6	performing	perform	VERB
fcis-31901	51	7	model	model	NOUN
fcis-31901	51	8	from	from	ADP
fcis-31901	51	9	problem	problem	NOUN
fcis-31901	51	10	2	2	NUM
fcis-31901	51	11	was	be	AUX
fcis-31901	51	12	directly	directly	ADV
fcis-31901	51	13	transferred	transfer	VERB
fcis-31901	51	14	to	to	ADP
fcis-31901	51	15	this	this	DET
fcis-31901	51	16	new	new	ADJ
fcis-31901	51	17	dataset	dataset	NOUN
fcis-31901	51	18	without	without	ADP
fcis-31901	51	19	additional	additional	ADJ
fcis-31901	51	20	retraining	retraining	NOUN
fcis-31901	51	21	.	.	PUNCT
fcis-31901	52	1	longitude	longitude	NOUN
fcis-31901	52	2	,	,	PUNCT
fcis-31901	52	3	latitude	latitude	NOUN
fcis-31901	52	4	,	,	PUNCT
fcis-31901	52	5	and	and	CCONJ
fcis-31901	52	6	gravity	gravity	NOUN
fcis-31901	52	7	anomaly	anomaly	NOUN
fcis-31901	52	8	values	value	NOUN
fcis-31901	52	9	were	be	AUX
fcis-31901	52	10	fed	feed	VERB
fcis-31901	52	11	as	as	ADP
fcis-31901	52	12	input	input	NOUN
fcis-31901	52	13	features	feature	NOUN
fcis-31901	52	14	,	,	PUNCT
fcis-31901	52	15	and	and	CCONJ
fcis-31901	52	16	the	the	DET
fcis-31901	52	17	model	model	NOUN
fcis-31901	52	18	produced	produce	VERB
fcis-31901	52	19	predicted	predict	VERB
fcis-31901	52	20	regional	regional	ADJ
fcis-31901	52	21	labels	label	NOUN
fcis-31901	52	22	for	for	ADP
fcis-31901	52	23	each	each	DET
fcis-31901	52	24	data	datum	NOUN
fcis-31901	52	25	point	point	NOUN
fcis-31901	52	26	.	.	PUNCT
fcis-31901	53	1	the	the	DET
fcis-31901	53	2	predicted	predict	VERB
fcis-31901	53	3	labels	label	NOUN
fcis-31901	53	4	were	be	AUX
fcis-31901	53	5	subsequently	subsequently	ADV
fcis-31901	53	6	integrated	integrate	VERB
fcis-31901	53	7	into	into	ADP
fcis-31901	53	8	the	the	DET
fcis-31901	53	9	dataset	dataset	NOUN
fcis-31901	53	10	to	to	PART
fcis-31901	53	11	facilitate	facilitate	VERB
fcis-31901	53	12	further	further	ADJ
fcis-31901	53	13	evaluation	evaluation	NOUN
fcis-31901	53	14	.	.	PUNCT
fcis-31901	54	1	to	to	PART
fcis-31901	54	2	assess	assess	VERB
fcis-31901	54	3	the	the	DET
fcis-31901	54	4	model	model	NOUN
fcis-31901	54	5	’s	’s	PART
fcis-31901	54	6	applicability	applicability	NOUN
fcis-31901	54	7	in	in	ADP
fcis-31901	54	8	a	a	DET
fcis-31901	54	9	cross	cross	ADJ
fcis-31901	54	10	-	-	ADJ
fcis-31901	54	11	dataset	dataset	ADJ
fcis-31901	54	12	context	context	NOUN
fcis-31901	54	13	,	,	PUNCT
fcis-31901	54	14	standard	standard	ADJ
fcis-31901	54	15	performance	performance	NOUN
fcis-31901	54	16	indicators	indicator	NOUN
fcis-31901	54	17	were	be	AUX
fcis-31901	54	18	computed	compute	VERB
fcis-31901	54	19	,	,	PUNCT
fcis-31901	54	20	including	include	VERB
fcis-31901	54	21	accuracy	accuracy	NOUN
fcis-31901	54	22	,	,	PUNCT
fcis-31901	54	23	precision	precision	NOUN
fcis-31901	54	24	,	,	PUNCT
fcis-31901	54	25	recall	recall	NOUN
fcis-31901	54	26	,	,	PUNCT
fcis-31901	54	27	and	and	CCONJ
fcis-31901	54	28	1f	1f	NUM
fcis-31901	54	29	-score	-score	NOUN
fcis-31901	54	30	.	.	PUNCT
fcis-31901	55	1	as	as	SCONJ
fcis-31901	55	2	summarized	summarize	VERB
fcis-31901	55	3	in	in	ADP
fcis-31901	55	4	table	table	NOUN
fcis-31901	55	5	1	1	NUM
fcis-31901	55	6	,	,	PUNCT
fcis-31901	55	7	the	the	DET
fcis-31901	55	8	model	model	NOUN
fcis-31901	55	9	maintained	maintain	VERB
fcis-31901	55	10	consistently	consistently	ADV
fcis-31901	55	11	high	high	ADJ
fcis-31901	55	12	predictive	predictive	ADJ
fcis-31901	55	13	performance	performance	NOUN
fcis-31901	55	14	,	,	PUNCT
fcis-31901	55	15	with	with	ADP
fcis-31901	55	16	accuracy	accuracy	NOUN
fcis-31901	55	17	reaching	reach	VERB
fcis-31901	55	18	0.99	0.99	NUM
fcis-31901	55	19	.	.	PUNCT
fcis-31901	56	1	these	these	DET
fcis-31901	56	2	results	result	NOUN
fcis-31901	56	3	demonstrate	demonstrate	VERB
fcis-31901	56	4	both	both	CCONJ
fcis-31901	56	5	the	the	DET
fcis-31901	56	6	robustness	robustness	NOUN
fcis-31901	56	7	and	and	CCONJ
fcis-31901	56	8	transferability	transferability	NOUN
fcis-31901	56	9	of	of	ADP
fcis-31901	56	10	the	the	DET
fcis-31901	56	11	proposed	propose	VERB
fcis-31901	56	12	regional	regional	ADJ
fcis-31901	56	13	classification	classification	NOUN
fcis-31901	56	14	framework	framework	NOUN
fcis-31901	56	15	,	,	PUNCT
fcis-31901	56	16	supporting	support	VERB
fcis-31901	56	17	its	its	PRON
fcis-31901	56	18	deployment	deployment	NOUN
fcis-31901	56	19	in	in	ADP
fcis-31901	56	20	broader	broad	ADJ
fcis-31901	56	21	anomalybased	anomalybase	VERB
fcis-31901	56	22	navigation	navigation	NOUN
fcis-31901	56	23	scenarios	scenario	NOUN
fcis-31901	56	24	.	.	PUNCT
fcis-31901	57	1	figure	figure	VERB
fcis-31901	57	2	4	4	NUM
fcis-31901	57	3	.	.	PUNCT
fcis-31901	58	1	distribution	distribution	NOUN
fcis-31901	58	2	of	of	ADP
fcis-31901	58	3	gravity	gravity	NOUN
fcis-31901	58	4	anomaly	anomaly	NOUN
fcis-31901	58	5	data	datum	NOUN
fcis-31901	58	6	in	in	ADP
fcis-31901	58	7	dataset	dataset	NOUN
fcis-31901	58	8	2	2	NUM
fcis-31901	58	9	49	49	NUM
fcis-31901	58	10	table	table	NOUN
fcis-31901	58	11	1	1	NUM
fcis-31901	58	12	.	.	PUNCT
fcis-31901	58	13	evaluation	evaluation	NOUN
fcis-31901	58	14	metrics	metric	NOUN
fcis-31901	58	15	of	of	ADP
fcis-31901	58	16	the	the	DET
fcis-31901	58	17	transferred	transfer	VERB
fcis-31901	58	18	classification	classification	NOUN
fcis-31901	58	19	model	model	NOUN
fcis-31901	58	20	on	on	ADP
fcis-31901	58	21	dataset	dataset	ADJ
fcis-31901	58	22	2	2	NUM
fcis-31901	58	23	class	class	NOUN
fcis-31901	58	24	precision	precision	NOUN
fcis-31901	58	25	recall	recall	NOUN
fcis-31901	58	26	f1	f1	NOUN
fcis-31901	58	27	-	-	PUNCT
fcis-31901	58	28	score	score	NOUN
fcis-31901	58	29	support	support	NOUN
fcis-31901	58	30	1	1	NUM
fcis-31901	58	31	0.99	0.99	NUM
fcis-31901	58	32	0.99	0.99	NUM
fcis-31901	58	33	0.99	0.99	NUM
fcis-31901	58	34	168	168	NUM
fcis-31901	58	35	2	2	NUM
fcis-31901	58	36	0.99	0.99	NUM
fcis-31901	58	37	0.99	0.99	NUM
fcis-31901	58	38	0.99	0.99	NUM
fcis-31901	58	39	525	525	NUM
fcis-31901	58	40	3	3	NUM
fcis-31901	58	41	0.99	0.99	NUM
fcis-31901	58	42	0.99	0.99	NUM
fcis-31901	58	43	0.99	0.99	NUM
fcis-31901	58	44	747	747	NUM
fcis-31901	58	45	4	4	NUM
fcis-31901	58	46	0.99	0.99	NUM
fcis-31901	58	47	0.99	0.99	NUM
fcis-31901	58	48	0.99	0.99	NUM
fcis-31901	58	49	695	695	NUM
fcis-31901	58	50	5	5	NUM
fcis-31901	58	51	0.99	0.99	NUM
fcis-31901	58	52	0.99	0.99	NUM
fcis-31901	58	53	0.99	0.99	NUM
fcis-31901	58	54	818	818	NUM
fcis-31901	58	55	5	5	NUM
fcis-31901	58	56	.	.	PUNCT
fcis-31901	58	57	conclusion	conclusion	NOUN
fcis-31901	58	58	this	this	DET
fcis-31901	58	59	paper	paper	NOUN
fcis-31901	58	60	proposes	propose	VERB
fcis-31901	58	61	a	a	DET
fcis-31901	58	62	hybrid	hybrid	ADJ
fcis-31901	58	63	computational	computational	ADJ
fcis-31901	58	64	framework	framework	NOUN
fcis-31901	58	65	that	that	PRON
fcis-31901	58	66	integrates	integrate	VERB
fcis-31901	58	67	ward	ward	NOUN
fcis-31901	58	68	-	-	PUNCT
fcis-31901	58	69	based	base	VERB
fcis-31901	58	70	hierarchical	hierarchical	ADJ
fcis-31901	58	71	clustering	clustering	NOUN
fcis-31901	58	72	with	with	ADP
fcis-31901	58	73	random	random	ADJ
fcis-31901	58	74	forest	forest	NOUN
fcis-31901	58	75	classification	classification	NOUN
fcis-31901	58	76	to	to	PART
fcis-31901	58	77	address	address	VERB
fcis-31901	58	78	the	the	DET
fcis-31901	58	79	problem	problem	NOUN
fcis-31901	58	80	of	of	ADP
fcis-31901	58	81	regional	regional	ADJ
fcis-31901	58	82	classification	classification	NOUN
fcis-31901	58	83	in	in	ADP
fcis-31901	58	84	gravity	gravity	NOUN
fcis-31901	58	85	anomaly	anomaly	NOUN
fcis-31901	58	86	fields	field	NOUN
fcis-31901	58	87	.	.	PUNCT
fcis-31901	59	1	the	the	DET
fcis-31901	59	2	method	method	NOUN
fcis-31901	59	3	achieves	achieve	VERB
fcis-31901	59	4	consistently	consistently	ADV
fcis-31901	59	5	high	high	ADJ
fcis-31901	59	6	accuracy	accuracy	NOUN
fcis-31901	59	7	,	,	PUNCT
fcis-31901	59	8	reaching	reach	VERB
fcis-31901	59	9	0.99	0.99	NUM
fcis-31901	59	10	in	in	ADP
fcis-31901	59	11	crossdataset	crossdataset	NOUN
fcis-31901	59	12	evaluation	evaluation	NOUN
fcis-31901	59	13	,	,	PUNCT
fcis-31901	59	14	and	and	CCONJ
fcis-31901	59	15	demonstrates	demonstrate	VERB
fcis-31901	59	16	robustness	robustness	NOUN
fcis-31901	59	17	compared	compare	VERB
fcis-31901	59	18	to	to	ADP
fcis-31901	59	19	alternative	alternative	ADJ
fcis-31901	59	20	classifiers	classifier	NOUN
fcis-31901	59	21	such	such	ADJ
fcis-31901	59	22	as	as	ADP
fcis-31901	59	23	svm	svm	PROPN
fcis-31901	59	24	,	,	PUNCT
fcis-31901	59	25	knn	knn	PROPN
fcis-31901	59	26	,	,	PUNCT
fcis-31901	59	27	and	and	CCONJ
fcis-31901	59	28	decision	decision	NOUN
fcis-31901	59	29	trees	tree	NOUN
fcis-31901	59	30	.	.	PUNCT
fcis-31901	60	1	however	however	ADV
fcis-31901	60	2	,	,	PUNCT
fcis-31901	60	3	the	the	DET
fcis-31901	60	4	framework	framework	NOUN
fcis-31901	60	5	may	may	AUX
fcis-31901	60	6	be	be	AUX
fcis-31901	60	7	limited	limit	VERB
fcis-31901	60	8	by	by	ADP
fcis-31901	60	9	its	its	PRON
fcis-31901	60	10	reliance	reliance	NOUN
fcis-31901	60	11	on	on	ADP
fcis-31901	60	12	fixed	fix	VERB
fcis-31901	60	13	feature	feature	NOUN
fcis-31901	60	14	sets	set	NOUN
fcis-31901	60	15	and	and	CCONJ
fcis-31901	60	16	by	by	ADP
fcis-31901	60	17	potential	potential	ADJ
fcis-31901	60	18	sensitivity	sensitivity	NOUN
fcis-31901	60	19	to	to	ADP
fcis-31901	60	20	data	datum	NOUN
fcis-31901	60	21	imbalance	imbalance	NOUN
fcis-31901	60	22	in	in	ADP
fcis-31901	60	23	larger	large	ADJ
fcis-31901	60	24	-	-	PUNCT
fcis-31901	60	25	scale	scale	NOUN
fcis-31901	60	26	applications	application	NOUN
fcis-31901	60	27	.	.	PUNCT
fcis-31901	61	1	future	future	ADJ
fcis-31901	61	2	work	work	NOUN
fcis-31901	61	3	will	will	AUX
fcis-31901	61	4	focus	focus	VERB
fcis-31901	61	5	on	on	ADP
fcis-31901	61	6	incorporating	incorporate	VERB
fcis-31901	61	7	advanced	advanced	ADJ
fcis-31901	61	8	feature	feature	NOUN
fcis-31901	61	9	engineering	engineering	NOUN
fcis-31901	61	10	and	and	CCONJ
fcis-31901	61	11	deep	deep	ADJ
fcis-31901	61	12	learning	learning	NOUN
fcis-31901	61	13	architectures	architecture	NOUN
fcis-31901	61	14	to	to	PART
fcis-31901	61	15	enhance	enhance	VERB
fcis-31901	61	16	adaptability	adaptability	NOUN
fcis-31901	61	17	,	,	PUNCT
fcis-31901	61	18	while	while	SCONJ
fcis-31901	61	19	extending	extend	VERB
fcis-31901	61	20	the	the	DET
fcis-31901	61	21	approach	approach	NOUN
fcis-31901	61	22	to	to	ADP
fcis-31901	61	23	real	real	ADJ
fcis-31901	61	24	-	-	PUNCT
fcis-31901	61	25	time	time	NOUN
fcis-31901	61	26	anomaly	anomaly	NOUN
fcis-31901	61	27	detection	detection	NOUN
fcis-31901	61	28	scenarios	scenario	NOUN
fcis-31901	61	29	.	.	PUNCT
fcis-31901	62	1	overall	overall	ADV
fcis-31901	62	2	,	,	PUNCT
fcis-31901	62	3	the	the	DET
fcis-31901	62	4	framework	framework	NOUN
fcis-31901	62	5	provides	provide	VERB
fcis-31901	62	6	a	a	DET
fcis-31901	62	7	scalable	scalable	ADJ
fcis-31901	62	8	and	and	CCONJ
fcis-31901	62	9	generalizable	generalizable	ADJ
fcis-31901	62	10	solution	solution	NOUN
fcis-31901	62	11	for	for	ADP
fcis-31901	62	12	reliable	reliable	ADJ
fcis-31901	62	13	anomaly	anomaly	NOUN
fcis-31901	62	14	-	-	PUNCT
fcis-31901	62	15	based	base	VERB
fcis-31901	62	16	regional	regional	ADJ
fcis-31901	62	17	prediction	prediction	NOUN
fcis-31901	62	18	in	in	ADP
fcis-31901	62	19	computational	computational	ADJ
fcis-31901	62	20	navigation	navigation	NOUN
fcis-31901	62	21	systems	system	NOUN
fcis-31901	62	22	.	.	PUNCT
fcis-31901	63	1	acknowledgments	acknowledgment	NOUN
fcis-31901	63	2	the	the	DET
fcis-31901	63	3	authors	author	NOUN
fcis-31901	63	4	gratefully	gratefully	ADV
fcis-31901	63	5	acknowledge	acknowledge	VERB
fcis-31901	63	6	the	the	DET
fcis-31901	63	7	financial	financial	ADJ
fcis-31901	63	8	support	support	NOUN
fcis-31901	63	9	from	from	ADP
fcis-31901	63	10	xxx	xxx	NOUN
fcis-31901	63	11	funds	fund	NOUN
fcis-31901	63	12	.	.	PUNCT
fcis-31901	64	1	references	reference	NOUN
fcis-31901	64	2	[	[	X
fcis-31901	64	3	1	1	NUM
fcis-31901	64	4	]	]	PUNCT
fcis-31901	64	5	pham	pham	PROPN
fcis-31901	64	6	n	n	PROPN
fcis-31901	64	7	p.	p.	NOUN
fcis-31901	64	8	deep	deep	ADJ
fcis-31901	64	9	learning	learning	NOUN
fcis-31901	64	10	for	for	ADP
fcis-31901	64	11	automatic	automatic	ADJ
fcis-31901	64	12	geophysical	geophysical	ADJ
fcis-31901	64	13	interpretation	interpretation	NOUN
fcis-31901	64	14	with	with	ADP
fcis-31901	64	15	uncertainty	uncertainty	NOUN
fcis-31901	64	16	quantification[m	quantification[m	ADJ
fcis-31901	64	17	]	]	PUNCT
fcis-31901	64	18	.	.	PUNCT
fcis-31901	65	1	the	the	DET
fcis-31901	65	2	university	university	PROPN
fcis-31901	65	3	of	of	ADP
fcis-31901	65	4	texas	texas	PROPN
fcis-31901	65	5	at	at	ADP
fcis-31901	65	6	austin	austin	PROPN
fcis-31901	65	7	,	,	PUNCT
fcis-31901	65	8	2022	2022	NUM
fcis-31901	65	9	.	.	PUNCT
fcis-31901	66	1	[	[	X
fcis-31901	66	2	2	2	X
fcis-31901	66	3	]	]	X
fcis-31901	66	4	lee	lee	PROPN
fcis-31901	66	5	i.	i.	PROPN
fcis-31901	66	6	geospatial	geospatial	PROPN
fcis-31901	66	7	clustering	cluster	VERB
fcis-31901	66	8	in	in	ADP
fcis-31901	66	9	data	data	NOUN
fcis-31901	66	10	-	-	PUNCT
fcis-31901	66	11	rich	rich	ADJ
fcis-31901	66	12	environments	environment	NOUN
fcis-31901	66	13	:	:	PUNCT
fcis-31901	66	14	features	feature	NOUN
fcis-31901	66	15	and	and	CCONJ
fcis-31901	66	16	issues[c]//international	issues[c]//international	ADJ
fcis-31901	66	17	conference	conference	NOUN
fcis-31901	66	18	on	on	ADP
fcis-31901	66	19	knowledge	knowledge	NOUN
fcis-31901	66	20	-	-	PUNCT
fcis-31901	66	21	based	base	VERB
fcis-31901	66	22	and	and	CCONJ
fcis-31901	66	23	intelligent	intelligent	ADJ
fcis-31901	66	24	information	information	NOUN
fcis-31901	66	25	and	and	CCONJ
fcis-31901	66	26	engineering	engineering	NOUN
fcis-31901	66	27	systems	system	NOUN
fcis-31901	66	28	.	.	PUNCT
fcis-31901	67	1	berlin	berlin	PROPN
fcis-31901	67	2	,	,	PUNCT
fcis-31901	67	3	heidelberg	heidelberg	PROPN
fcis-31901	67	4	:	:	PUNCT
fcis-31901	67	5	springer	springer	PROPN
fcis-31901	67	6	berlin	berlin	PROPN
fcis-31901	67	7	heidelberg	heidelberg	PROPN
fcis-31901	67	8	,	,	PUNCT
fcis-31901	67	9	2005	2005	NUM
fcis-31901	67	10	:	:	PUNCT
fcis-31901	67	11	336	336	NUM
fcis-31901	67	12	-	-	SYM
fcis-31901	67	13	342	342	NUM
fcis-31901	67	14	.	.	PUNCT
fcis-31901	68	1	[	[	X
fcis-31901	68	2	3	3	X
fcis-31901	68	3	]	]	X
fcis-31901	68	4	zhang	zhang	PROPN
fcis-31901	68	5	d	d	PROPN
fcis-31901	68	6	,	,	PUNCT
fcis-31901	68	7	lee	lee	PROPN
fcis-31901	68	8	k	k	PROPN
fcis-31901	68	9	,	,	PUNCT
fcis-31901	68	10	lee	lee	PROPN
fcis-31901	68	11	i.	i.	PROPN
fcis-31901	68	12	hierarchical	hierarchical	PROPN
fcis-31901	68	13	trajectory	trajectory	NOUN
fcis-31901	68	14	clustering	cluster	VERB
fcis-31901	68	15	for	for	ADP
fcis-31901	68	16	spatio	spatio	NOUN
fcis-31901	68	17	-	-	PUNCT
fcis-31901	68	18	temporal	temporal	ADJ
fcis-31901	68	19	periodic	periodic	ADJ
fcis-31901	68	20	pattern	pattern	NOUN
fcis-31901	68	21	mining[j	mining[j	NOUN
fcis-31901	68	22	]	]	PUNCT
fcis-31901	68	23	.	.	PUNCT
fcis-31901	69	1	expert	expert	NOUN
fcis-31901	69	2	systems	system	NOUN
fcis-31901	69	3	with	with	ADP
fcis-31901	69	4	applications	application	NOUN
fcis-31901	69	5	,	,	PUNCT
fcis-31901	69	6	2018	2018	NUM
fcis-31901	69	7	,	,	PUNCT
fcis-31901	69	8	92	92	NUM
fcis-31901	69	9	:	:	SYM
fcis-31901	69	10	1	1	NUM
fcis-31901	69	11	-	-	SYM
fcis-31901	69	12	11	11	NUM
fcis-31901	69	13	.	.	PUNCT
fcis-31901	70	1	[	[	X
fcis-31901	70	2	4	4	X
fcis-31901	70	3	]	]	PUNCT
fcis-31901	70	4	talebi	talebi	PROPN
fcis-31901	70	5	h	h	NOUN
fcis-31901	70	6	,	,	PUNCT
fcis-31901	70	7	peeters	peeter	NOUN
fcis-31901	70	8	l	l	PROPN
fcis-31901	70	9	j	j	PROPN
fcis-31901	70	10	m	m	PROPN
fcis-31901	70	11	,	,	PUNCT
fcis-31901	70	12	otto	otto	PROPN
fcis-31901	70	13	a	a	PROPN
fcis-31901	70	14	,	,	PUNCT
fcis-31901	70	15	et	et	PROPN
fcis-31901	70	16	al	al	PROPN
fcis-31901	70	17	.	.	PUNCT
fcis-31901	71	1	a	a	DET
fcis-31901	71	2	truly	truly	ADV
fcis-31901	71	3	spatial	spatial	ADJ
fcis-31901	71	4	random	random	ADJ
fcis-31901	71	5	forests	forest	NOUN
fcis-31901	71	6	algorithm	algorithm	NOUN
fcis-31901	71	7	for	for	ADP
fcis-31901	71	8	geoscience	geoscience	NOUN
fcis-31901	71	9	data	datum	NOUN
fcis-31901	71	10	analysis	analysis	NOUN
fcis-31901	71	11	and	and	CCONJ
fcis-31901	71	12	modelling[j	modelling[j	NOUN
fcis-31901	71	13	]	]	PUNCT
fcis-31901	71	14	.	.	PUNCT
fcis-31901	72	1	mathematical	mathematical	ADJ
fcis-31901	72	2	geosciences	geoscience	NOUN
fcis-31901	72	3	,	,	PUNCT
fcis-31901	72	4	2022	2022	NUM
fcis-31901	72	5	,	,	PUNCT
fcis-31901	72	6	54(1	54(1	NUM
fcis-31901	72	7	):	):	PUNCT
fcis-31901	72	8	1	1	NUM
fcis-31901	72	9	-	-	SYM
fcis-31901	72	10	22	22	NUM
fcis-31901	72	11	.	.	PUNCT
fcis-31901	73	1	[	[	X
fcis-31901	73	2	5	5	X
fcis-31901	73	3	]	]	PUNCT
fcis-31901	73	4	kellenberger	kellenberger	NOUN
fcis-31901	73	5	b	b	PROPN
fcis-31901	73	6	,	,	PUNCT
fcis-31901	73	7	tasar	tasar	ADJ
fcis-31901	73	8	o	o	NOUN
fcis-31901	73	9	,	,	PUNCT
fcis-31901	73	10	bhushan	bhushan	PROPN
fcis-31901	73	11	damodaran	damodaran	VERB
fcis-31901	73	12	b	b	PROPN
fcis-31901	73	13	,	,	PUNCT
fcis-31901	73	14	et	et	PROPN
fcis-31901	73	15	al	al	PROPN
fcis-31901	73	16	.	.	PUNCT
fcis-31901	74	1	deep	deep	ADJ
fcis-31901	74	2	domain	domain	NOUN
fcis-31901	74	3	adaptation	adaptation	NOUN
fcis-31901	74	4	in	in	ADP
fcis-31901	74	5	earth	earth	NOUN
fcis-31901	74	6	observation[j	observation[j	PROPN
fcis-31901	74	7	]	]	PUNCT
fcis-31901	74	8	.	.	PUNCT
fcis-31901	75	1	deep	deep	ADJ
fcis-31901	75	2	learning	learning	NOUN
fcis-31901	75	3	for	for	ADP
fcis-31901	75	4	the	the	DET
fcis-31901	75	5	earth	earth	NOUN
fcis-31901	75	6	sciences	science	NOUN
fcis-31901	75	7	:	:	PUNCT
fcis-31901	75	8	a	a	DET
fcis-31901	75	9	comprehensive	comprehensive	ADJ
fcis-31901	75	10	approach	approach	NOUN
fcis-31901	75	11	to	to	ADP
fcis-31901	75	12	remote	remote	ADJ
fcis-31901	75	13	sensing	sensing	NOUN
fcis-31901	75	14	,	,	PUNCT
fcis-31901	75	15	climate	climate	NOUN
fcis-31901	75	16	science	science	NOUN
fcis-31901	75	17	,	,	PUNCT
fcis-31901	75	18	and	and	CCONJ
fcis-31901	75	19	geosciences	geoscience	NOUN
fcis-31901	75	20	,	,	PUNCT
fcis-31901	75	21	2021	2021	NUM
fcis-31901	75	22	:	:	PUNCT
fcis-31901	75	23	90	90	NUM
fcis-31901	75	24	-	-	SYM
fcis-31901	75	25	104	104	NUM
fcis-31901	75	26	.	.	PUNCT
fcis-31901	76	1	[	[	X
fcis-31901	76	2	6	6	NUM
fcis-31901	76	3	]	]	PUNCT
fcis-31901	76	4	ott	ott	PROPN
fcis-31901	76	5	f.	f.	PROPN
fcis-31901	76	6	representation	representation	PROPN
fcis-31901	76	7	learning	learn	VERB
fcis-31901	76	8	for	for	ADP
fcis-31901	76	9	domain	domain	NOUN
fcis-31901	76	10	adaptation	adaptation	NOUN
fcis-31901	76	11	and	and	CCONJ
fcis-31901	76	12	crossmodal	crossmodal	ADJ
fcis-31901	76	13	retrieval[d	retrieval[d	NOUN
fcis-31901	76	14	]	]	PUNCT
fcis-31901	76	15	.	.	PUNCT
fcis-31901	77	1	lmu	lmu	NOUN
fcis-31901	77	2	,	,	PUNCT
fcis-31901	77	3	2023	2023	NUM
fcis-31901	77	4	.	.	PUNCT