id	sid	tid	token	lemma	pos
fcis-30506	1	1	frontiers	frontier	NOUN
fcis-30506	1	2	in	in	ADP
fcis-30506	1	3	computing	computing	NOUN
fcis-30506	1	4	and	and	CCONJ
fcis-30506	1	5	intelligent	intelligent	ADJ
fcis-30506	1	6	systems	system	NOUN
fcis-30506	1	7	issn	issn	VERB
fcis-30506	1	8	:	:	PUNCT
fcis-30506	1	9	2832	2832	NUM
fcis-30506	1	10	-	-	SYM
fcis-30506	1	11	6024	6024	NUM
fcis-30506	1	12	|	|	NOUN
fcis-30506	1	13	vol	vol	NOUN
fcis-30506	1	14	.	.	PROPN
fcis-30506	2	1	12	12	NUM
fcis-30506	2	2	,	,	PUNCT
fcis-30506	2	3	no	no	INTJ
fcis-30506	2	4	.	.	NOUN
fcis-30506	2	5	1	1	NUM
fcis-30506	2	6	,	,	PUNCT
fcis-30506	2	7	2025	2025	NUM
fcis-30506	2	8	64	64	NUM
fcis-30506	2	9	spatial	spatial	ADJ
fcis-30506	2	10	prediction	prediction	NOUN
fcis-30506	2	11	of	of	ADP
fcis-30506	2	12	mortality	mortality	NOUN
fcis-30506	2	13	based	base	VERB
fcis-30506	2	14	on	on	ADP
fcis-30506	2	15	double‐layer	double‐layer	NOUN
fcis-30506	2	16	gaussian	gaussian	NOUN
fcis-30506	2	17	process	process	NOUN
fcis-30506	2	18	ensemble	ensemble	ADJ
fcis-30506	2	19	regression	regression	NOUN
fcis-30506	2	20	model	model	NOUN
fcis-30506	2	21	shengxian	shengxian	PROPN
fcis-30506	2	22	wang	wang	PROPN
fcis-30506	2	23	*	*	PUNCT
fcis-30506	2	24	department	department	PROPN
fcis-30506	2	25	of	of	ADP
fcis-30506	2	26	economics	economic	NOUN
fcis-30506	2	27	,	,	PUNCT
fcis-30506	2	28	beijing	beijing	PROPN
fcis-30506	2	29	technology	technology	PROPN
fcis-30506	2	30	and	and	CCONJ
fcis-30506	2	31	business	business	NOUN
fcis-30506	2	32	university	university	PROPN
fcis-30506	2	33	,	,	PUNCT
fcis-30506	2	34	beijing	beijing	PROPN
fcis-30506	2	35	,	,	PUNCT
fcis-30506	2	36	china	china	PROPN
fcis-30506	2	37	*	*	PUNCT
fcis-30506	2	38	corresponding	correspond	VERB
fcis-30506	2	39	author	author	NOUN
fcis-30506	2	40	email	email	NOUN
fcis-30506	2	41	:	:	PUNCT
fcis-30506	3	1	1131452764@qq.com	1131452764@qq.com	NUM
fcis-30506	3	2	abstract	abstract	ADJ
fcis-30506	3	3	:	:	PUNCT
fcis-30506	3	4	mortality	mortality	NOUN
fcis-30506	3	5	rate	rate	NOUN
fcis-30506	3	6	prediction	prediction	NOUN
fcis-30506	3	7	is	be	AUX
fcis-30506	3	8	one	one	NUM
fcis-30506	3	9	of	of	ADP
fcis-30506	3	10	the	the	DET
fcis-30506	3	11	core	core	NOUN
fcis-30506	3	12	aspects	aspect	NOUN
fcis-30506	3	13	of	of	ADP
fcis-30506	3	14	risk	risk	NOUN
fcis-30506	3	15	pricing	pricing	NOUN
fcis-30506	3	16	and	and	CCONJ
fcis-30506	3	17	product	product	NOUN
fcis-30506	3	18	design	design	NOUN
fcis-30506	3	19	in	in	ADP
fcis-30506	3	20	the	the	DET
fcis-30506	3	21	insurance	insurance	NOUN
fcis-30506	3	22	industry	industry	NOUN
fcis-30506	3	23	.	.	PUNCT
fcis-30506	4	1	its	its	PRON
fcis-30506	4	2	accuracy	accuracy	NOUN
fcis-30506	4	3	directly	directly	ADV
fcis-30506	4	4	determines	determine	VERB
fcis-30506	4	5	the	the	DET
fcis-30506	4	6	rationality	rationality	NOUN
fcis-30506	4	7	of	of	ADP
fcis-30506	4	8	premiums	premium	NOUN
fcis-30506	4	9	,	,	PUNCT
fcis-30506	4	10	the	the	DET
fcis-30506	4	11	adequacy	adequacy	NOUN
fcis-30506	4	12	of	of	ADP
fcis-30506	4	13	reserves	reserve	NOUN
fcis-30506	4	14	,	,	PUNCT
fcis-30506	4	15	and	and	CCONJ
fcis-30506	4	16	the	the	DET
fcis-30506	4	17	solvency	solvency	NOUN
fcis-30506	4	18	assessment	assessment	NOUN
fcis-30506	4	19	for	for	ADP
fcis-30506	4	20	life	life	NOUN
fcis-30506	4	21	insurance	insurance	NOUN
fcis-30506	4	22	,	,	PUNCT
fcis-30506	4	23	annuity	annuity	NOUN
fcis-30506	4	24	products	product	NOUN
fcis-30506	4	25	and	and	CCONJ
fcis-30506	4	26	other	other	ADJ
fcis-30506	4	27	personal	personal	ADJ
fcis-30506	4	28	insurance	insurance	NOUN
fcis-30506	4	29	products	product	NOUN
fcis-30506	4	30	.	.	PUNCT
fcis-30506	5	1	currently	currently	ADV
fcis-30506	5	2	,	,	PUNCT
fcis-30506	5	3	there	there	PRON
fcis-30506	5	4	is	be	VERB
fcis-30506	5	5	temporal	temporal	ADJ
fcis-30506	5	6	and	and	CCONJ
fcis-30506	5	7	spatial	spatial	ADJ
fcis-30506	5	8	heterogeneity	heterogeneity	NOUN
fcis-30506	5	9	in	in	ADP
fcis-30506	5	10	china	china	PROPN
fcis-30506	5	11	's	's	PART
fcis-30506	5	12	population	population	NOUN
fcis-30506	5	13	mortality	mortality	NOUN
fcis-30506	5	14	rate	rate	NOUN
fcis-30506	5	15	,	,	PUNCT
fcis-30506	5	16	and	and	CCONJ
fcis-30506	5	17	implementing	implement	VERB
fcis-30506	5	18	a	a	DET
fcis-30506	5	19	traditional	traditional	ADJ
fcis-30506	5	20	unified	unified	ADJ
fcis-30506	5	21	rate	rate	NOUN
fcis-30506	5	22	system	system	NOUN
fcis-30506	5	23	is	be	AUX
fcis-30506	5	24	difficult	difficult	ADJ
fcis-30506	5	25	to	to	PART
fcis-30506	5	26	match	match	VERB
fcis-30506	5	27	the	the	DET
fcis-30506	5	28	actual	actual	ADJ
fcis-30506	5	29	risk	risk	NOUN
fcis-30506	5	30	distribution	distribution	NOUN
fcis-30506	5	31	,	,	PUNCT
fcis-30506	5	32	which	which	PRON
fcis-30506	5	33	may	may	AUX
fcis-30506	5	34	lead	lead	VERB
fcis-30506	5	35	to	to	ADP
fcis-30506	5	36	systematic	systematic	ADJ
fcis-30506	5	37	pricing	pricing	NOUN
fcis-30506	5	38	deviations	deviation	NOUN
fcis-30506	5	39	and	and	CCONJ
fcis-30506	5	40	other	other	ADJ
fcis-30506	5	41	problems	problem	NOUN
fcis-30506	5	42	.	.	PUNCT
fcis-30506	6	1	this	this	DET
fcis-30506	6	2	paper	paper	NOUN
fcis-30506	6	3	proposes	propose	VERB
fcis-30506	6	4	an	an	DET
fcis-30506	6	5	ensemble	ensemble	ADJ
fcis-30506	6	6	learning	learning	NOUN
fcis-30506	6	7	model	model	NOUN
fcis-30506	6	8	based	base	VERB
fcis-30506	6	9	on	on	ADP
fcis-30506	6	10	gaussian	gaussian	ADJ
fcis-30506	6	11	process	process	NOUN
fcis-30506	6	12	regression	regression	NOUN
fcis-30506	6	13	,	,	PUNCT
fcis-30506	6	14	integrating	integrate	VERB
fcis-30506	6	15	regional	regional	ADJ
fcis-30506	6	16	factors	factor	NOUN
fcis-30506	6	17	as	as	ADP
fcis-30506	6	18	feature	feature	NOUN
fcis-30506	6	19	variables	variable	NOUN
fcis-30506	6	20	.	.	PUNCT
fcis-30506	7	1	on	on	ADP
fcis-30506	7	2	the	the	DET
fcis-30506	7	3	one	one	NUM
fcis-30506	7	4	hand	hand	NOUN
fcis-30506	7	5	,	,	PUNCT
fcis-30506	7	6	it	it	PRON
fcis-30506	7	7	uses	use	VERB
fcis-30506	7	8	ensemble	ensemble	ADJ
fcis-30506	7	9	learning	learning	NOUN
fcis-30506	7	10	methods	method	NOUN
fcis-30506	7	11	to	to	PART
fcis-30506	7	12	further	far	ADV
fcis-30506	7	13	optimize	optimize	VERB
fcis-30506	7	14	the	the	DET
fcis-30506	7	15	prediction	prediction	NOUN
fcis-30506	7	16	results	result	NOUN
fcis-30506	7	17	of	of	ADP
fcis-30506	7	18	the	the	DET
fcis-30506	7	19	gaussian	gaussian	ADJ
fcis-30506	7	20	process	process	NOUN
fcis-30506	7	21	regression	regression	NOUN
fcis-30506	7	22	model	model	NOUN
fcis-30506	7	23	;	;	PUNCT
fcis-30506	7	24	on	on	ADP
fcis-30506	7	25	the	the	DET
fcis-30506	7	26	other	other	ADJ
fcis-30506	7	27	hand	hand	NOUN
fcis-30506	7	28	,	,	PUNCT
fcis-30506	7	29	it	it	PRON
fcis-30506	7	30	uses	use	VERB
fcis-30506	7	31	gaussian	gaussian	ADJ
fcis-30506	7	32	process	process	NOUN
fcis-30506	7	33	regression	regression	NOUN
fcis-30506	7	34	to	to	PART
fcis-30506	7	35	quantitatively	quantitatively	ADV
fcis-30506	7	36	estimate	estimate	VERB
fcis-30506	7	37	the	the	DET
fcis-30506	7	38	uncertainty	uncertainty	NOUN
fcis-30506	7	39	of	of	ADP
fcis-30506	7	40	future	future	ADJ
fcis-30506	7	41	predictions	prediction	NOUN
fcis-30506	7	42	.	.	PUNCT
fcis-30506	8	1	in	in	ADP
fcis-30506	8	2	the	the	DET
fcis-30506	8	3	actual	actual	ADJ
fcis-30506	8	4	data	datum	NOUN
fcis-30506	8	5	analysis	analysis	NOUN
fcis-30506	8	6	,	,	PUNCT
fcis-30506	8	7	this	this	DET
fcis-30506	8	8	paper	paper	NOUN
fcis-30506	8	9	first	first	ADV
fcis-30506	8	10	verified	verify	VERB
fcis-30506	8	11	that	that	SCONJ
fcis-30506	8	12	there	there	PRON
fcis-30506	8	13	are	be	VERB
fcis-30506	8	14	differences	difference	NOUN
fcis-30506	8	15	in	in	ADP
fcis-30506	8	16	population	population	NOUN
fcis-30506	8	17	mortality	mortality	NOUN
fcis-30506	8	18	rates	rate	NOUN
fcis-30506	8	19	among	among	ADP
fcis-30506	8	20	31	31	NUM
fcis-30506	8	21	provinces	province	NOUN
fcis-30506	8	22	in	in	ADP
fcis-30506	8	23	china	china	PROPN
fcis-30506	8	24	;	;	PUNCT
fcis-30506	8	25	secondly	secondly	ADV
fcis-30506	8	26	,	,	PUNCT
fcis-30506	8	27	it	it	PRON
fcis-30506	8	28	conducted	conduct	VERB
fcis-30506	8	29	a	a	DET
fcis-30506	8	30	monte	monte	PROPN
fcis-30506	8	31	carlo	carlo	NOUN
fcis-30506	8	32	experiment	experiment	NOUN
fcis-30506	8	33	on	on	ADP
fcis-30506	8	34	the	the	DET
fcis-30506	8	35	proposed	propose	VERB
fcis-30506	8	36	method	method	NOUN
fcis-30506	8	37	and	and	CCONJ
fcis-30506	8	38	found	find	VERB
fcis-30506	8	39	that	that	SCONJ
fcis-30506	8	40	this	this	DET
fcis-30506	8	41	method	method	NOUN
fcis-30506	8	42	can	can	AUX
fcis-30506	8	43	fit	fit	VERB
fcis-30506	8	44	nonlinear	nonlinear	ADJ
fcis-30506	8	45	functions	function	NOUN
fcis-30506	8	46	;	;	PUNCT
fcis-30506	8	47	then	then	ADV
fcis-30506	8	48	,	,	PUNCT
fcis-30506	8	49	based	base	VERB
fcis-30506	8	50	on	on	ADP
fcis-30506	8	51	the	the	DET
fcis-30506	8	52	historical	historical	ADJ
fcis-30506	8	53	mortality	mortality	NOUN
fcis-30506	8	54	rates	rate	NOUN
fcis-30506	8	55	of	of	ADP
fcis-30506	8	56	31	31	NUM
fcis-30506	8	57	provinces	province	NOUN
fcis-30506	8	58	in	in	ADP
fcis-30506	8	59	china	china	PROPN
fcis-30506	8	60	,	,	PUNCT
fcis-30506	8	61	it	it	PRON
fcis-30506	8	62	conducted	conduct	VERB
fcis-30506	8	63	real	real	ADJ
fcis-30506	8	64	data	datum	NOUN
fcis-30506	8	65	analysis	analysis	NOUN
fcis-30506	8	66	and	and	CCONJ
fcis-30506	8	67	prediction	prediction	NOUN
fcis-30506	8	68	to	to	PART
fcis-30506	8	69	obtain	obtain	VERB
fcis-30506	8	70	mortality	mortality	NOUN
fcis-30506	8	71	rate	rate	NOUN
fcis-30506	8	72	prediction	prediction	NOUN
fcis-30506	8	73	intervals	interval	NOUN
fcis-30506	8	74	for	for	ADP
fcis-30506	8	75	uncertainty	uncertainty	NOUN
fcis-30506	8	76	quantification	quantification	NOUN
fcis-30506	8	77	estimation	estimation	NOUN
fcis-30506	8	78	.	.	PUNCT
fcis-30506	9	1	from	from	ADP
fcis-30506	9	2	the	the	DET
fcis-30506	9	3	prediction	prediction	NOUN
fcis-30506	9	4	results	result	NOUN
fcis-30506	9	5	,	,	PUNCT
fcis-30506	9	6	this	this	DET
fcis-30506	9	7	method	method	NOUN
fcis-30506	9	8	can	can	AUX
fcis-30506	9	9	well	well	ADV
fcis-30506	9	10	support	support	VERB
fcis-30506	9	11	spatial	spatial	ADJ
fcis-30506	9	12	prediction	prediction	NOUN
fcis-30506	9	13	of	of	ADP
fcis-30506	9	14	population	population	NOUN
fcis-30506	9	15	mortality	mortality	NOUN
fcis-30506	9	16	rates	rate	NOUN
fcis-30506	9	17	,	,	PUNCT
fcis-30506	9	18	thereby	thereby	ADV
fcis-30506	9	19	providing	provide	VERB
fcis-30506	9	20	more	more	ADV
fcis-30506	9	21	effective	effective	ADJ
fcis-30506	9	22	decision	decision	NOUN
fcis-30506	9	23	-	-	PUNCT
fcis-30506	9	24	making	making	NOUN
fcis-30506	9	25	basis	basis	NOUN
fcis-30506	9	26	for	for	SCONJ
fcis-30506	9	27	insurance	insurance	NOUN
fcis-30506	9	28	companies	company	NOUN
fcis-30506	9	29	to	to	PART
fcis-30506	9	30	set	set	VERB
fcis-30506	9	31	premiums	premium	NOUN
fcis-30506	9	32	for	for	ADP
fcis-30506	9	33	personal	personal	ADJ
fcis-30506	9	34	insurance	insurance	NOUN
fcis-30506	9	35	products	product	NOUN
fcis-30506	9	36	.	.	PUNCT
fcis-30506	10	1	keywords	keyword	NOUN
fcis-30506	10	2	:	:	PUNCT
fcis-30506	10	3	gaussian	gaussian	ADJ
fcis-30506	10	4	process	process	NOUN
fcis-30506	10	5	regression	regression	NOUN
fcis-30506	10	6	model	model	NOUN
fcis-30506	10	7	(	(	PUNCT
fcis-30506	10	8	gpr	gpr	PROPN
fcis-30506	10	9	)	)	PUNCT
fcis-30506	10	10	;	;	PUNCT
fcis-30506	10	11	regional	regional	ADJ
fcis-30506	10	12	mortality	mortality	NOUN
fcis-30506	10	13	prediction	prediction	NOUN
fcis-30506	10	14	;	;	PUNCT
fcis-30506	10	15	uncertainty	uncertainty	NOUN
fcis-30506	10	16	quantification	quantification	NOUN
fcis-30506	10	17	;	;	PUNCT
fcis-30506	10	18	stacking	stack	VERB
fcis-30506	10	19	ensemble	ensemble	ADJ
fcis-30506	10	20	learning	learning	NOUN
fcis-30506	10	21	.	.	PUNCT
fcis-30506	11	1	1	1	X
fcis-30506	11	2	.	.	X
fcis-30506	11	3	introduction	introduction	NOUN
fcis-30506	11	4	the	the	DET
fcis-30506	11	5	mortality	mortality	NOUN
fcis-30506	11	6	rate	rate	NOUN
fcis-30506	11	7	prediction	prediction	NOUN
fcis-30506	11	8	is	be	AUX
fcis-30506	11	9	the	the	DET
fcis-30506	11	10	core	core	ADJ
fcis-30506	11	11	cornerstone	cornerstone	NOUN
fcis-30506	11	12	of	of	ADP
fcis-30506	11	13	the	the	DET
fcis-30506	11	14	insurance	insurance	NOUN
fcis-30506	11	15	industry	industry	NOUN
fcis-30506	11	16	,	,	PUNCT
fcis-30506	11	17	directly	directly	ADV
fcis-30506	11	18	determining	determine	VERB
fcis-30506	11	19	the	the	DET
fcis-30506	11	20	pricing	pricing	NOUN
fcis-30506	11	21	of	of	ADP
fcis-30506	11	22	personal	personal	ADJ
fcis-30506	11	23	insurance	insurance	NOUN
fcis-30506	11	24	products	product	NOUN
fcis-30506	11	25	,	,	PUNCT
fcis-30506	11	26	capital	capital	NOUN
fcis-30506	11	27	reserves	reserve	NOUN
fcis-30506	11	28	,	,	PUNCT
fcis-30506	11	29	and	and	CCONJ
fcis-30506	11	30	the	the	DET
fcis-30506	11	31	longterm	longterm	ADJ
fcis-30506	11	32	operational	operational	ADJ
fcis-30506	11	33	stability	stability	NOUN
fcis-30506	11	34	.	.	PUNCT
fcis-30506	12	1	as	as	ADP
fcis-30506	12	2	the	the	DET
fcis-30506	12	3	underlying	underlie	VERB
fcis-30506	12	4	logic	logic	NOUN
fcis-30506	12	5	of	of	ADP
fcis-30506	12	6	actuarial	actuarial	ADJ
fcis-30506	12	7	science	science	NOUN
fcis-30506	12	8	,	,	PUNCT
fcis-30506	12	9	mortality	mortality	NOUN
fcis-30506	12	10	rate	rate	NOUN
fcis-30506	12	11	data	datum	NOUN
fcis-30506	12	12	provide	provide	VERB
fcis-30506	12	13	key	key	ADJ
fcis-30506	12	14	parameters	parameter	NOUN
fcis-30506	12	15	for	for	ADP
fcis-30506	12	16	the	the	DET
fcis-30506	12	17	rate	rate	NOUN
fcis-30506	12	18	determination	determination	NOUN
fcis-30506	12	19	of	of	ADP
fcis-30506	12	20	personal	personal	ADJ
fcis-30506	12	21	insurance	insurance	NOUN
fcis-30506	12	22	products	product	NOUN
fcis-30506	12	23	such	such	ADJ
fcis-30506	12	24	as	as	ADP
fcis-30506	12	25	life	life	NOUN
fcis-30506	12	26	insurance	insurance	NOUN
fcis-30506	12	27	,	,	PUNCT
fcis-30506	12	28	annuity	annuity	NOUN
fcis-30506	12	29	,	,	PUNCT
fcis-30506	12	30	and	and	CCONJ
fcis-30506	12	31	health	health	NOUN
fcis-30506	12	32	insurance	insurance	NOUN
fcis-30506	12	33	.	.	PUNCT
fcis-30506	13	1	its	its	PRON
fcis-30506	13	2	accuracy	accuracy	NOUN
fcis-30506	13	3	directly	directly	ADV
fcis-30506	13	4	affects	affect	VERB
fcis-30506	13	5	the	the	DET
fcis-30506	13	6	risk	risk	NOUN
fcis-30506	13	7	exposure	exposure	NOUN
fcis-30506	13	8	and	and	CCONJ
fcis-30506	13	9	profit	profit	NOUN
fcis-30506	13	10	structure	structure	NOUN
fcis-30506	13	11	of	of	ADP
fcis-30506	13	12	insurance	insurance	NOUN
fcis-30506	13	13	companies	company	NOUN
fcis-30506	13	14	overestimating	overestimate	VERB
fcis-30506	13	15	the	the	DET
fcis-30506	13	16	mortality	mortality	NOUN
fcis-30506	13	17	rate	rate	NOUN
fcis-30506	13	18	will	will	AUX
fcis-30506	13	19	lead	lead	VERB
fcis-30506	13	20	to	to	ADP
fcis-30506	13	21	overly	overly	ADV
fcis-30506	13	22	high	high	ADJ
fcis-30506	13	23	premium	premium	NOUN
fcis-30506	13	24	pricing	pricing	NOUN
fcis-30506	13	25	,	,	PUNCT
fcis-30506	13	26	weakening	weaken	VERB
fcis-30506	13	27	market	market	NOUN
fcis-30506	13	28	competitiveness	competitiveness	NOUN
fcis-30506	13	29	;	;	PUNCT
fcis-30506	13	30	underestimating	underestimate	VERB
fcis-30506	13	31	it	it	PRON
fcis-30506	13	32	may	may	AUX
fcis-30506	13	33	trigger	trigger	VERB
fcis-30506	13	34	systemic	systemic	ADJ
fcis-30506	13	35	interest	interest	NOUN
fcis-30506	13	36	rate	rate	NOUN
fcis-30506	13	37	losses	loss	NOUN
fcis-30506	13	38	or	or	CCONJ
fcis-30506	13	39	even	even	ADV
fcis-30506	13	40	solvency	solvency	NOUN
fcis-30506	13	41	crises	crisis	NOUN
fcis-30506	13	42	.	.	PUNCT
fcis-30506	14	1	there	there	PRON
fcis-30506	14	2	have	have	AUX
fcis-30506	14	3	been	be	AUX
fcis-30506	14	4	numerous	numerous	ADJ
fcis-30506	14	5	studies	study	NOUN
fcis-30506	14	6	on	on	ADP
fcis-30506	14	7	mortality	mortality	NOUN
fcis-30506	14	8	prediction	prediction	NOUN
fcis-30506	14	9	models	model	NOUN
fcis-30506	14	10	both	both	CCONJ
fcis-30506	14	11	at	at	ADP
fcis-30506	14	12	home	home	NOUN
fcis-30506	14	13	and	and	CCONJ
fcis-30506	14	14	abroad	abroad	ADV
fcis-30506	14	15	,	,	PUNCT
fcis-30506	14	16	mainly	mainly	ADV
fcis-30506	14	17	divided	divide	VERB
fcis-30506	14	18	into	into	ADP
fcis-30506	14	19	static	static	ADJ
fcis-30506	14	20	mortality	mortality	NOUN
fcis-30506	14	21	models	model	NOUN
fcis-30506	14	22	and	and	CCONJ
fcis-30506	14	23	dynamic	dynamic	ADJ
fcis-30506	14	24	mortality	mortality	NOUN
fcis-30506	14	25	models	model	NOUN
fcis-30506	14	26	.	.	PUNCT
fcis-30506	15	1	the	the	DET
fcis-30506	15	2	static	static	ADJ
fcis-30506	15	3	mortality	mortality	NOUN
fcis-30506	15	4	model	model	NOUN
fcis-30506	15	5	encompasses	encompass	VERB
fcis-30506	15	6	the	the	DET
fcis-30506	15	7	de	de	PROPN
fcis-30506	15	8	moivre	moivre	NOUN
fcis-30506	15	9	model	model	NOUN
fcis-30506	15	10	(	(	PUNCT
fcis-30506	15	11	1729	1729	NUM
fcis-30506	15	12	)	)	PUNCT
fcis-30506	15	13	,	,	PUNCT
fcis-30506	15	14	the	the	DET
fcis-30506	15	15	gompertz	gompertz	NOUN
fcis-30506	15	16	model	model	NOUN
fcis-30506	15	17	(	(	PUNCT
fcis-30506	15	18	1825	1825	NUM
fcis-30506	15	19	)	)	PUNCT
fcis-30506	15	20	,	,	PUNCT
fcis-30506	15	21	the	the	DET
fcis-30506	15	22	makeham	makeham	PROPN
fcis-30506	15	23	model	model	NOUN
fcis-30506	15	24	(	(	PUNCT
fcis-30506	15	25	1860	1860	NUM
fcis-30506	15	26	)	)	PUNCT
fcis-30506	15	27	,	,	PUNCT
fcis-30506	15	28	and	and	CCONJ
fcis-30506	15	29	other	other	ADJ
fcis-30506	15	30	classic	classic	ADJ
fcis-30506	15	31	models	model	NOUN
fcis-30506	15	32	.	.	PUNCT
fcis-30506	16	1	however	however	ADV
fcis-30506	16	2	,	,	PUNCT
fcis-30506	16	3	the	the	DET
fcis-30506	16	4	static	static	ADJ
fcis-30506	16	5	mortality	mortality	NOUN
fcis-30506	16	6	rate	rate	NOUN
fcis-30506	16	7	model	model	NOUN
fcis-30506	16	8	has	have	VERB
fcis-30506	16	9	certain	certain	ADJ
fcis-30506	16	10	limitations	limitation	NOUN
fcis-30506	16	11	.	.	PUNCT
fcis-30506	17	1	it	it	PRON
fcis-30506	17	2	fails	fail	VERB
fcis-30506	17	3	to	to	PART
fcis-30506	17	4	take	take	VERB
fcis-30506	17	5	into	into	ADP
fcis-30506	17	6	account	account	NOUN
fcis-30506	17	7	the	the	DET
fcis-30506	17	8	changing	change	VERB
fcis-30506	17	9	factors	factor	NOUN
fcis-30506	17	10	of	of	ADP
fcis-30506	17	11	future	future	ADJ
fcis-30506	17	12	mortality	mortality	NOUN
fcis-30506	17	13	rates	rate	NOUN
fcis-30506	17	14	.	.	PUNCT
fcis-30506	18	1	as	as	ADP
fcis-30506	18	2	a	a	DET
fcis-30506	18	3	result	result	NOUN
fcis-30506	18	4	,	,	PUNCT
fcis-30506	18	5	such	such	ADJ
fcis-30506	18	6	models	model	NOUN
fcis-30506	18	7	are	be	AUX
fcis-30506	18	8	usually	usually	ADV
fcis-30506	18	9	only	only	ADV
fcis-30506	18	10	applicable	applicable	ADJ
fcis-30506	18	11	for	for	ADP
fcis-30506	18	12	fitting	fit	VERB
fcis-30506	18	13	existing	exist	VERB
fcis-30506	18	14	data	datum	NOUN
fcis-30506	18	15	and	and	CCONJ
fcis-30506	18	16	are	be	AUX
fcis-30506	18	17	difficult	difficult	ADJ
fcis-30506	18	18	to	to	PART
fcis-30506	18	19	play	play	VERB
fcis-30506	18	20	an	an	DET
fcis-30506	18	21	effective	effective	ADJ
fcis-30506	18	22	role	role	NOUN
fcis-30506	18	23	in	in	ADP
fcis-30506	18	24	predicting	predict	VERB
fcis-30506	18	25	future	future	ADJ
fcis-30506	18	26	mortality	mortality	NOUN
fcis-30506	18	27	rates	rate	NOUN
fcis-30506	18	28	.	.	PUNCT
fcis-30506	19	1	the	the	DET
fcis-30506	19	2	dynamic	dynamic	ADJ
fcis-30506	19	3	mortality	mortality	NOUN
fcis-30506	19	4	models	model	NOUN
fcis-30506	19	5	include	include	VERB
fcis-30506	19	6	the	the	DET
fcis-30506	19	7	lee	lee	PROPN
fcis-30506	19	8	-	-	PUNCT
fcis-30506	19	9	carter	carter	PROPN
fcis-30506	19	10	model	model	NOUN
fcis-30506	19	11	(	(	PUNCT
fcis-30506	19	12	1992	1992	NUM
fcis-30506	19	13	)	)	PUNCT
fcis-30506	19	14	,	,	PUNCT
fcis-30506	19	15	the	the	DET
fcis-30506	19	16	cbd	cbd	NOUN
fcis-30506	19	17	model	model	NOUN
fcis-30506	19	18	(	(	PUNCT
fcis-30506	19	19	2006	2006	NUM
fcis-30506	19	20	)	)	PUNCT
fcis-30506	19	21	,	,	PUNCT
fcis-30506	19	22	the	the	DET
fcis-30506	19	23	renshaw	renshaw	NOUN
fcis-30506	19	24	-	-	PUNCT
fcis-30506	19	25	haberman	haberman	NOUN
fcis-30506	19	26	model	model	NOUN
fcis-30506	19	27	(	(	PUNCT
fcis-30506	19	28	2003	2003	NUM
fcis-30506	19	29	)	)	PUNCT
fcis-30506	19	30	and	and	CCONJ
fcis-30506	19	31	other	other	ADJ
fcis-30506	19	32	models	model	NOUN
fcis-30506	19	33	.	.	PUNCT
fcis-30506	20	1	among	among	ADP
fcis-30506	20	2	them	they	PRON
fcis-30506	20	3	,	,	PUNCT
fcis-30506	20	4	the	the	DET
fcis-30506	20	5	lee	lee	PROPN
fcis-30506	20	6	-	-	PUNCT
fcis-30506	20	7	carter	carter	PROPN
fcis-30506	20	8	model	model	NOUN
fcis-30506	20	9	and	and	CCONJ
fcis-30506	20	10	the	the	DET
fcis-30506	20	11	cbd	cbd	NOUN
fcis-30506	20	12	model	model	NOUN
fcis-30506	20	13	respectively	respectively	ADV
fcis-30506	20	14	conduct	conduct	VERB
fcis-30506	20	15	modeling	modeling	NOUN
fcis-30506	20	16	work	work	NOUN
fcis-30506	20	17	by	by	ADP
fcis-30506	20	18	applying	apply	VERB
fcis-30506	20	19	the	the	DET
fcis-30506	20	20	logarithmic	logarithmic	ADJ
fcis-30506	20	21	transformation	transformation	NOUN
fcis-30506	20	22	and	and	CCONJ
fcis-30506	20	23	logistic	logistic	ADJ
fcis-30506	20	24	transformation	transformation	NOUN
fcis-30506	20	25	to	to	ADP
fcis-30506	20	26	mortality	mortality	NOUN
fcis-30506	20	27	rates	rate	NOUN
fcis-30506	20	28	.	.	PUNCT
fcis-30506	21	1	these	these	DET
fcis-30506	21	2	two	two	NUM
fcis-30506	21	3	models	model	NOUN
fcis-30506	21	4	have	have	VERB
fcis-30506	21	5	significant	significant	ADJ
fcis-30506	21	6	advantages	advantage	NOUN
fcis-30506	21	7	and	and	CCONJ
fcis-30506	21	8	are	be	AUX
fcis-30506	21	9	widely	widely	ADV
fcis-30506	21	10	used	use	VERB
fcis-30506	21	11	in	in	ADP
fcis-30506	21	12	related	related	ADJ
fcis-30506	21	13	research	research	NOUN
fcis-30506	21	14	.	.	PUNCT
fcis-30506	22	1	milidonis	milidonis	PROPN
fcis-30506	22	2	et	et	PROPN
fcis-30506	22	3	al	al	PROPN
fcis-30506	22	4	.	.	PROPN
fcis-30506	23	1	(	(	PUNCT
fcis-30506	23	2	2011	2011	NUM
fcis-30506	23	3	)	)	PUNCT
fcis-30506	23	4	proposed	propose	VERB
fcis-30506	23	5	the	the	DET
fcis-30506	23	6	markov	markov	NOUN
fcis-30506	23	7	mechanism	mechanism	NOUN
fcis-30506	23	8	transition	transition	NOUN
fcis-30506	23	9	stochastic	stochastic	NOUN
fcis-30506	23	10	mortality	mortality	NOUN
fcis-30506	23	11	model	model	NOUN
fcis-30506	23	12	,	,	PUNCT
fcis-30506	23	13	which	which	PRON
fcis-30506	23	14	expanded	expand	VERB
fcis-30506	23	15	the	the	DET
fcis-30506	23	16	lee	lee	PROPN
fcis-30506	23	17	-	-	PUNCT
fcis-30506	23	18	carter	carter	PROPN
fcis-30506	23	19	model	model	NOUN
fcis-30506	23	20	through	through	ADP
fcis-30506	23	21	mechanism	mechanism	NOUN
fcis-30506	23	22	transition	transition	NOUN
fcis-30506	23	23	and	and	CCONJ
fcis-30506	23	24	conducted	conduct	VERB
fcis-30506	23	25	empirical	empirical	ADJ
fcis-30506	23	26	research	research	NOUN
fcis-30506	23	27	analysis	analysis	NOUN
fcis-30506	23	28	using	use	VERB
fcis-30506	23	29	us	we	PRON
fcis-30506	23	30	population	population	NOUN
fcis-30506	23	31	data.[10	data.[10	PROPN
fcis-30506	23	32	]	]	PUNCT
fcis-30506	24	1	wang	wang	PROPN
fcis-30506	24	2	tingting	tingting	PROPN
fcis-30506	24	3	(	(	PUNCT
fcis-30506	24	4	2014	2014	NUM
fcis-30506	24	5	)	)	PUNCT
fcis-30506	24	6	used	use	VERB
fcis-30506	24	7	the	the	DET
fcis-30506	24	8	leecarter	leecarter	NOUN
fcis-30506	24	9	model	model	NOUN
fcis-30506	24	10	as	as	ADP
fcis-30506	24	11	the	the	DET
fcis-30506	24	12	foundation	foundation	NOUN
fcis-30506	24	13	and	and	CCONJ
fcis-30506	24	14	took	take	VERB
fcis-30506	24	15	the	the	DET
fcis-30506	24	16	male	male	ADJ
fcis-30506	24	17	mortality	mortality	NOUN
fcis-30506	24	18	rates	rate	NOUN
fcis-30506	24	19	of	of	ADP
fcis-30506	24	20	china	china	PROPN
fcis-30506	24	21	and	and	CCONJ
fcis-30506	24	22	japan	japan	PROPN
fcis-30506	24	23	as	as	ADP
fcis-30506	24	24	the	the	DET
fcis-30506	24	25	data	data	NOUN
fcis-30506	24	26	samples	sample	NOUN
fcis-30506	24	27	for	for	ADP
fcis-30506	24	28	empirical	empirical	ADJ
fcis-30506	24	29	research	research	NOUN
fcis-30506	24	30	.	.	PUNCT
fcis-30506	25	1	by	by	ADP
fcis-30506	25	2	using	use	VERB
fcis-30506	25	3	the	the	DET
fcis-30506	25	4	cointegration	cointegration	NOUN
fcis-30506	25	5	theory	theory	NOUN
fcis-30506	25	6	,	,	PUNCT
fcis-30506	25	7	she	she	PRON
fcis-30506	25	8	constructed	construct	VERB
fcis-30506	25	9	the	the	DET
fcis-30506	25	10	china	china	PROPN
fcis-30506	25	11	-	-	PUNCT
fcis-30506	25	12	japan	japan	PROPN
fcis-30506	25	13	mortality	mortality	PROPN
fcis-30506	25	14	time	time	NOUN
fcis-30506	25	15	factor	factor	NOUN
fcis-30506	25	16	cointegration	cointegration	NOUN
fcis-30506	25	17	model	model	NOUN
fcis-30506	25	18	.	.	PUNCT
fcis-30506	26	1	this	this	DET
fcis-30506	26	2	model	model	NOUN
fcis-30506	26	3	achieved	achieve	VERB
fcis-30506	26	4	the	the	DET
fcis-30506	26	5	prediction	prediction	NOUN
fcis-30506	26	6	of	of	ADP
fcis-30506	26	7	future	future	ADJ
fcis-30506	26	8	male	male	ADJ
fcis-30506	26	9	mortality	mortality	NOUN
fcis-30506	26	10	rates	rate	NOUN
fcis-30506	26	11	in	in	ADP
fcis-30506	26	12	china	china	PROPN
fcis-30506	26	13	by	by	ADP
fcis-30506	26	14	correcting	correct	VERB
fcis-30506	26	15	the	the	DET
fcis-30506	26	16	errors	error	NOUN
fcis-30506	26	17	of	of	ADP
fcis-30506	26	18	the	the	DET
fcis-30506	26	19	predicted	predict	VERB
fcis-30506	26	20	values	value	NOUN
fcis-30506	26	21	of	of	ADP
fcis-30506	26	22	japanese	japanese	ADJ
fcis-30506	26	23	male	male	ADJ
fcis-30506	26	24	mortality	mortality	NOUN
fcis-30506	26	25	rates	rate	NOUN
fcis-30506	26	26	,	,	PUNCT
fcis-30506	26	27	providing	provide	VERB
fcis-30506	26	28	a	a	DET
fcis-30506	26	29	new	new	ADJ
fcis-30506	26	30	idea	idea	NOUN
fcis-30506	26	31	for	for	ADP
fcis-30506	26	32	the	the	DET
fcis-30506	26	33	research	research	NOUN
fcis-30506	26	34	on	on	ADP
fcis-30506	26	35	mortality	mortality	NOUN
fcis-30506	26	36	rate	rate	NOUN
fcis-30506	26	37	prediction	prediction	NOUN
fcis-30506	26	38	in	in	ADP
fcis-30506	26	39	china.[2	china.[2	NOUN
fcis-30506	26	40	]	]	X
fcis-30506	26	41	wang	wang	PROPN
fcis-30506	26	42	zhigang	zhigang	PROPN
fcis-30506	26	43	et	et	PROPN
fcis-30506	26	44	al	al	PROPN
fcis-30506	26	45	.	.	PROPN
fcis-30506	27	1	(	(	PUNCT
fcis-30506	27	2	2016	2016	NUM
fcis-30506	27	3	)	)	PUNCT
fcis-30506	27	4	provided	provide	VERB
fcis-30506	27	5	a	a	DET
fcis-30506	27	6	comprehensive	comprehensive	ADJ
fcis-30506	27	7	and	and	CCONJ
fcis-30506	27	8	complete	complete	ADJ
fcis-30506	27	9	theoretical	theoretical	ADJ
fcis-30506	27	10	elaboration	elaboration	NOUN
fcis-30506	27	11	of	of	ADP
fcis-30506	27	12	the	the	DET
fcis-30506	27	13	lee	lee	PROPN
fcis-30506	27	14	-	-	PUNCT
fcis-30506	27	15	carter	carter	PROPN
fcis-30506	27	16	model	model	NOUN
fcis-30506	27	17	,	,	PUNCT
fcis-30506	27	18	gave	give	VERB
fcis-30506	27	19	the	the	DET
fcis-30506	27	20	expressions	expression	NOUN
fcis-30506	27	21	of	of	ADP
fcis-30506	27	22	the	the	DET
fcis-30506	27	23	distribution	distribution	NOUN
fcis-30506	27	24	and	and	CCONJ
fcis-30506	27	25	prediction	prediction	NOUN
fcis-30506	27	26	of	of	ADP
fcis-30506	27	27	the	the	DET
fcis-30506	27	28	leecarter	leecarter	NOUN
fcis-30506	27	29	model	model	NOUN
fcis-30506	27	30	,	,	PUNCT
fcis-30506	27	31	and	and	CCONJ
fcis-30506	27	32	proved	prove	VERB
fcis-30506	27	33	that	that	SCONJ
fcis-30506	27	34	compared	compare	VERB
fcis-30506	27	35	with	with	ADP
fcis-30506	27	36	the	the	DET
fcis-30506	27	37	traditional	traditional	ADJ
fcis-30506	27	38	interval	interval	NOUN
fcis-30506	27	39	prediction	prediction	NOUN
fcis-30506	27	40	expressions	expression	NOUN
fcis-30506	27	41	,	,	PUNCT
fcis-30506	27	42	the	the	DET
fcis-30506	27	43	interval	interval	NOUN
fcis-30506	27	44	prediction	prediction	NOUN
fcis-30506	27	45	expressions	expression	NOUN
fcis-30506	27	46	obtained	obtain	VERB
fcis-30506	27	47	based	base	VERB
fcis-30506	27	48	on	on	ADP
fcis-30506	27	49	the	the	DET
fcis-30506	27	50	complete	complete	ADJ
fcis-30506	27	51	theoretical	theoretical	ADJ
fcis-30506	27	52	model	model	NOUN
fcis-30506	27	53	were	be	AUX
fcis-30506	27	54	more	more	ADJ
fcis-30506	27	55	ideal.[3	ideal.[3	NOUN
fcis-30506	27	56	]	]	PUNCT
fcis-30506	27	57	li	li	PROPN
fcis-30506	27	58	yaojie	yaojie	PROPN
fcis-30506	27	59	(	(	PUNCT
fcis-30506	27	60	2016	2016	NUM
fcis-30506	27	61	)	)	PUNCT
fcis-30506	27	62	used	use	VERB
fcis-30506	27	63	the	the	DET
fcis-30506	27	64	lee	lee	PROPN
fcis-30506	27	65	-	-	PUNCT
fcis-30506	27	66	carter	carter	PROPN
fcis-30506	27	67	model	model	NOUN
fcis-30506	27	68	to	to	PART
fcis-30506	27	69	calculate	calculate	VERB
fcis-30506	27	70	mortality	mortality	NOUN
fcis-30506	27	71	rates	rate	NOUN
fcis-30506	27	72	and	and	CCONJ
fcis-30506	27	73	conducted	conduct	VERB
fcis-30506	27	74	unbiased	unbiased	ADJ
fcis-30506	27	75	prediction	prediction	NOUN
fcis-30506	27	76	of	of	ADP
fcis-30506	27	77	the	the	DET
fcis-30506	27	78	results	result	NOUN
fcis-30506	27	79	through	through	ADP
fcis-30506	27	80	random	random	ADJ
fcis-30506	27	81	simulation	simulation	NOUN
fcis-30506	27	82	methods	method	NOUN
fcis-30506	27	83	,	,	PUNCT
fcis-30506	27	84	effectively	effectively	ADV
fcis-30506	27	85	solving	solve	VERB
fcis-30506	27	86	the	the	DET
fcis-30506	27	87	problem	problem	NOUN
fcis-30506	27	88	of	of	ADP
fcis-30506	27	89	underestimated	underestimated	ADJ
fcis-30506	27	90	bias	bias	NOUN
fcis-30506	27	91	in	in	ADP
fcis-30506	27	92	mortality	mortality	NOUN
fcis-30506	27	93	rate	rate	NOUN
fcis-30506	27	94	prediction	prediction	NOUN
fcis-30506	27	95	caused	cause	VERB
fcis-30506	27	96	by	by	ADP
fcis-30506	27	97	simple	simple	ADJ
fcis-30506	27	98	extrapolation	extrapolation	NOUN
fcis-30506	27	99	models.[6	models.[6	NOUN
fcis-30506	27	100	]	]	PUNCT
fcis-30506	27	101	after	after	ADP
fcis-30506	27	102	a	a	DET
fcis-30506	27	103	comparative	comparative	ADJ
fcis-30506	27	104	analysis	analysis	NOUN
fcis-30506	27	105	of	of	ADP
fcis-30506	27	106	eight	eight	NUM
fcis-30506	27	107	commonly	commonly	ADV
fcis-30506	27	108	used	use	VERB
fcis-30506	27	109	mortality	mortality	NOUN
fcis-30506	27	110	models	model	NOUN
fcis-30506	27	111	,	,	PUNCT
fcis-30506	27	112	including	include	VERB
fcis-30506	27	113	the	the	DET
fcis-30506	27	114	lee	lee	PROPN
fcis-30506	27	115	-	-	PUNCT
fcis-30506	27	116	carter	carter	PROPN
fcis-30506	27	117	model	model	NOUN
fcis-30506	27	118	,	,	PUNCT
fcis-30506	27	119	the	the	DET
fcis-30506	27	120	cbd	cbd	NOUN
fcis-30506	27	121	model	model	NOUN
fcis-30506	27	122	,	,	PUNCT
fcis-30506	27	123	the	the	DET
fcis-30506	27	124	rh	rh	PROPN
fcis-30506	27	125	model	model	NOUN
fcis-30506	27	126	,	,	PUNCT
fcis-30506	27	127	etc	etc	X
fcis-30506	27	128	.	.	X
fcis-30506	27	129	wang	wang	PROPN
fcis-30506	27	130	xiaojun	xiaojun	PROPN
fcis-30506	27	131	and	and	CCONJ
fcis-30506	27	132	lu	lu	PROPN
fcis-30506	27	133	qian	qian	PROPN
fcis-30506	27	134	(	(	PUNCT
fcis-30506	27	135	2020	2020	NUM
fcis-30506	27	136	)	)	PUNCT
fcis-30506	27	137	found	find	VERB
fcis-30506	27	138	that	that	SCONJ
fcis-30506	27	139	the	the	DET
fcis-30506	27	140	cbd	cbd	NOUN
fcis-30506	27	141	model	model	NOUN
fcis-30506	27	142	performed	perform	VERB
fcis-30506	27	143	excellently	excellently	ADV
fcis-30506	27	144	in	in	ADP
fcis-30506	27	145	fitting	fitting	ADJ
fcis-30506	27	146	and	and	CCONJ
fcis-30506	27	147	predicting	predict	VERB
fcis-30506	27	148	the	the	DET
fcis-30506	27	149	mortality	mortality	NOUN
fcis-30506	27	150	rate	rate	NOUN
fcis-30506	27	151	of	of	ADP
fcis-30506	27	152	the	the	DET
fcis-30506	27	153	elderly	elderly	ADJ
fcis-30506	27	154	in	in	ADP
fcis-30506	27	155	the	the	DET
fcis-30506	27	156	china	china	PROPN
fcis-30506	27	157	’s	’s	PART
fcis-30506	27	158	mainland	mainland	NOUN
fcis-30506	27	159	.	.	PUNCT
fcis-30506	28	1	its	its	PRON
fcis-30506	28	2	prediction	prediction	NOUN
fcis-30506	28	3	interval	interval	NOUN
fcis-30506	28	4	was	be	AUX
fcis-30506	28	5	reasonable	reasonable	ADJ
fcis-30506	28	6	,	,	PUNCT
fcis-30506	28	7	and	and	CCONJ
fcis-30506	28	8	the	the	DET
fcis-30506	28	9	survival	survival	NOUN
fcis-30506	28	10	curve	curve	NOUN
fcis-30506	28	11	conformed	conform	VERB
fcis-30506	28	12	to	to	ADP
fcis-30506	28	13	the	the	DET
fcis-30506	28	14	actual	actual	ADJ
fcis-30506	28	15	situation	situation	NOUN
fcis-30506	28	16	.	.	PUNCT
fcis-30506	29	1	in	in	ADP
fcis-30506	29	2	response	response	NOUN
fcis-30506	29	3	to	to	ADP
fcis-30506	29	4	the	the	DET
fcis-30506	29	5	problems	problem	NOUN
fcis-30506	29	6	of	of	ADP
fcis-30506	29	7	limited	limited	ADJ
fcis-30506	29	8	data	datum	NOUN
fcis-30506	29	9	volume	volume	NOUN
fcis-30506	29	10	and	and	CCONJ
fcis-30506	29	11	large	large	ADJ
fcis-30506	29	12	fluctuations	fluctuation	NOUN
fcis-30506	29	13	in	in	ADP
fcis-30506	29	14	the	the	DET
fcis-30506	29	15	mortality	mortality	NOUN
fcis-30506	29	16	rate	rate	NOUN
fcis-30506	29	17	of	of	ADP
fcis-30506	29	18	people	people	NOUN
fcis-30506	29	19	above	above	ADP
fcis-30506	29	20	the	the	DET
fcis-30506	29	21	retirement	retirement	NOUN
fcis-30506	29	22	age	age	NOUN
fcis-30506	29	23	in	in	ADP
fcis-30506	29	24	the	the	DET
fcis-30506	29	25	china	china	PROPN
fcis-30506	29	26	’s	’s	PART
fcis-30506	29	27	mainland	mainland	NOUN
fcis-30506	29	28	.	.	PUNCT
fcis-30506	30	1	[	[	X
fcis-30506	30	2	5]wang	5]wang	NUM
fcis-30506	30	3	xiaojun	xiaojun	PROPN
fcis-30506	30	4	and	and	CCONJ
fcis-30506	30	5	zhao	zhao	PROPN
fcis-30506	30	6	xiaoyue	xiaoyue	PROPN
fcis-30506	30	7	(	(	PUNCT
fcis-30506	30	8	2021	2021	NUM
fcis-30506	30	9	)	)	PUNCT
fcis-30506	30	10	adopted	adopt	VERB
fcis-30506	30	11	the	the	DET
fcis-30506	30	12	cbd	cbd	NOUN
fcis-30506	30	13	model	model	NOUN
fcis-30506	30	14	to	to	PART
fcis-30506	30	15	construct	construct	VERB
fcis-30506	30	16	a	a	DET
fcis-30506	30	17	65	65	NUM
fcis-30506	30	18	logistic	logistic	ADJ
fcis-30506	30	19	multi	multi	ADJ
fcis-30506	30	20	-	-	ADJ
fcis-30506	30	21	population	population	ADJ
fcis-30506	30	22	model	model	NOUN
fcis-30506	30	23	suitable	suitable	ADJ
fcis-30506	30	24	for	for	ADP
fcis-30506	30	25	the	the	DET
fcis-30506	30	26	mortality	mortality	NOUN
fcis-30506	30	27	rate	rate	NOUN
fcis-30506	30	28	of	of	ADP
fcis-30506	30	29	the	the	DET
fcis-30506	30	30	elderly.[4	elderly.[4	NOUN
fcis-30506	30	31	]	]	PUNCT
fcis-30506	30	32	with	with	ADP
fcis-30506	30	33	the	the	DET
fcis-30506	30	34	advent	advent	NOUN
fcis-30506	30	35	of	of	ADP
fcis-30506	30	36	the	the	DET
fcis-30506	30	37	era	era	NOUN
fcis-30506	30	38	of	of	ADP
fcis-30506	30	39	big	big	ADJ
fcis-30506	30	40	data	datum	NOUN
fcis-30506	30	41	,	,	PUNCT
fcis-30506	30	42	machine	machine	NOUN
fcis-30506	30	43	learning	learning	NOUN
fcis-30506	30	44	models	model	NOUN
fcis-30506	30	45	have	have	AUX
fcis-30506	30	46	gained	gain	VERB
fcis-30506	30	47	favor	favor	NOUN
fcis-30506	30	48	among	among	ADP
fcis-30506	30	49	researchers	researcher	NOUN
fcis-30506	30	50	.	.	PUNCT
fcis-30506	31	1	qiao	qiao	PROPN
fcis-30506	31	2	chunjuan	chunjuan	PROPN
fcis-30506	31	3	et	et	PROPN
fcis-30506	31	4	al	al	PROPN
fcis-30506	31	5	.	.	PROPN
fcis-30506	31	6	(	(	PUNCT
fcis-30506	31	7	2020	2020	NUM
fcis-30506	31	8	)	)	PUNCT
fcis-30506	31	9	analyzed	analyze	VERB
fcis-30506	31	10	the	the	DET
fcis-30506	31	11	influencing	influence	VERB
fcis-30506	31	12	factors	factor	NOUN
fcis-30506	31	13	of	of	ADP
fcis-30506	31	14	the	the	DET
fcis-30506	31	15	health	health	NOUN
fcis-30506	31	16	status	status	NOUN
fcis-30506	31	17	of	of	ADP
fcis-30506	31	18	the	the	DET
fcis-30506	31	19	elderly	elderly	ADJ
fcis-30506	31	20	through	through	ADP
fcis-30506	31	21	the	the	DET
fcis-30506	31	22	xgboost	xgboost	PROPN
fcis-30506	31	23	algorithm	algorithm	PROPN
fcis-30506	31	24	,	,	PUNCT
fcis-30506	31	25	established	establish	VERB
fcis-30506	31	26	a	a	DET
fcis-30506	31	27	two	two	NUM
fcis-30506	31	28	-	-	PUNCT
fcis-30506	31	29	year	year	NOUN
fcis-30506	31	30	transition	transition	NOUN
fcis-30506	31	31	probability	probability	NOUN
fcis-30506	31	32	matrix	matrix	NOUN
fcis-30506	31	33	for	for	ADP
fcis-30506	31	34	the	the	DET
fcis-30506	31	35	elderly	elderly	ADJ
fcis-30506	31	36	,	,	PUNCT
fcis-30506	31	37	and	and	CCONJ
fcis-30506	31	38	established	establish	VERB
fcis-30506	31	39	a	a	DET
fcis-30506	31	40	bp	bp	PROPN
fcis-30506	31	41	neural	neural	ADJ
fcis-30506	31	42	network	network	NOUN
fcis-30506	31	43	model	model	NOUN
fcis-30506	31	44	to	to	PART
fcis-30506	31	45	obtain	obtain	VERB
fcis-30506	31	46	the	the	DET
fcis-30506	31	47	one	one	NUM
fcis-30506	31	48	-	-	PUNCT
fcis-30506	31	49	step	step	NOUN
fcis-30506	31	50	transition	transition	NOUN
fcis-30506	31	51	probability	probability	NOUN
fcis-30506	31	52	matrix	matrix	NOUN
fcis-30506	31	53	for	for	ADP
fcis-30506	31	54	the	the	DET
fcis-30506	31	55	elderly.[8	elderly.[8	NOUN
fcis-30506	31	56	]	]	X
fcis-30506	31	57	subsequently	subsequently	ADV
fcis-30506	31	58	,	,	PUNCT
fcis-30506	31	59	qiao	qiao	PROPN
fcis-30506	31	60	chunjuan	chunjuan	PROPN
fcis-30506	31	61	et	et	PROPN
fcis-30506	31	62	al	al	PROPN
fcis-30506	31	63	.	.	PROPN
fcis-30506	32	1	(	(	PUNCT
fcis-30506	32	2	2023	2023	NUM
fcis-30506	32	3	)	)	PUNCT
fcis-30506	32	4	adopted	adopt	VERB
fcis-30506	32	5	a	a	DET
fcis-30506	32	6	deep	deep	ADJ
fcis-30506	32	7	neural	neural	ADJ
fcis-30506	32	8	network	network	NOUN
fcis-30506	32	9	model	model	NOUN
fcis-30506	32	10	to	to	PART
fcis-30506	32	11	price	price	NOUN
fcis-30506	32	12	longterm	longterm	PROPN
fcis-30506	32	13	care	care	NOUN
fcis-30506	32	14	insurance	insurance	NOUN
fcis-30506	32	15	products	product	NOUN
fcis-30506	32	16	.	.	PUNCT
fcis-30506	33	1	[	[	X
fcis-30506	33	2	9	9	NUM
fcis-30506	33	3	]	]	X
fcis-30506	33	4	the	the	DET
fcis-30506	33	5	achievements	achievement	NOUN
fcis-30506	33	6	of	of	ADP
fcis-30506	33	7	scholars	scholar	NOUN
fcis-30506	33	8	at	at	ADP
fcis-30506	33	9	home	home	NOUN
fcis-30506	33	10	and	and	CCONJ
fcis-30506	33	11	abroad	abroad	ADV
fcis-30506	33	12	on	on	ADP
fcis-30506	33	13	mortality	mortality	NOUN
fcis-30506	33	14	prediction	prediction	NOUN
fcis-30506	33	15	models	model	NOUN
fcis-30506	33	16	are	be	AUX
fcis-30506	33	17	quite	quite	ADV
fcis-30506	33	18	abundant	abundant	ADJ
fcis-30506	33	19	.	.	PUNCT
fcis-30506	34	1	however	however	ADV
fcis-30506	34	2	,	,	PUNCT
fcis-30506	34	3	there	there	PRON
fcis-30506	34	4	are	be	VERB
fcis-30506	34	5	still	still	ADV
fcis-30506	34	6	relatively	relatively	ADV
fcis-30506	34	7	few	few	ADJ
fcis-30506	34	8	studies	study	NOUN
fcis-30506	34	9	on	on	ADP
fcis-30506	34	10	the	the	DET
fcis-30506	34	11	nonlinear	nonlinear	ADJ
fcis-30506	34	12	dynamic	dynamic	ADJ
fcis-30506	34	13	trend	trend	NOUN
fcis-30506	34	14	of	of	ADP
fcis-30506	34	15	mortality	mortality	NOUN
fcis-30506	34	16	rates	rate	NOUN
fcis-30506	34	17	and	and	CCONJ
fcis-30506	34	18	the	the	DET
fcis-30506	34	19	integration	integration	NOUN
fcis-30506	34	20	of	of	ADP
fcis-30506	34	21	geographical	geographical	ADJ
fcis-30506	34	22	factors	factor	NOUN
fcis-30506	34	23	into	into	ADP
fcis-30506	34	24	the	the	DET
fcis-30506	34	25	models	model	NOUN
fcis-30506	34	26	.	.	PUNCT
fcis-30506	35	1	based	base	VERB
fcis-30506	35	2	on	on	ADP
fcis-30506	35	3	this	this	PRON
fcis-30506	35	4	,	,	PUNCT
fcis-30506	35	5	this	this	DET
fcis-30506	35	6	paper	paper	NOUN
fcis-30506	35	7	proposes	propose	VERB
fcis-30506	35	8	to	to	PART
fcis-30506	35	9	use	use	VERB
fcis-30506	35	10	a	a	DET
fcis-30506	35	11	two	two	NUM
fcis-30506	35	12	-	-	PUNCT
fcis-30506	35	13	layer	layer	NOUN
fcis-30506	35	14	gaussian	gaussian	ADJ
fcis-30506	35	15	process	process	NOUN
fcis-30506	35	16	ensemble	ensemble	ADJ
fcis-30506	35	17	regression	regression	NOUN
fcis-30506	35	18	model	model	NOUN
fcis-30506	35	19	to	to	PART
fcis-30506	35	20	incorporate	incorporate	VERB
fcis-30506	35	21	geographical	geographical	ADJ
fcis-30506	35	22	factors	factor	NOUN
fcis-30506	35	23	.	.	PUNCT
fcis-30506	36	1	on	on	ADP
fcis-30506	36	2	one	one	NUM
fcis-30506	36	3	hand	hand	NOUN
fcis-30506	36	4	,	,	PUNCT
fcis-30506	36	5	it	it	PRON
fcis-30506	36	6	accurately	accurately	ADV
fcis-30506	36	7	predicts	predict	VERB
fcis-30506	36	8	the	the	DET
fcis-30506	36	9	mortality	mortality	NOUN
fcis-30506	36	10	rates	rate	NOUN
fcis-30506	36	11	of	of	ADP
fcis-30506	36	12	31	31	NUM
fcis-30506	36	13	provinces	province	NOUN
fcis-30506	36	14	,	,	PUNCT
fcis-30506	36	15	enabling	enable	VERB
fcis-30506	36	16	insurance	insurance	NOUN
fcis-30506	36	17	companies	company	NOUN
fcis-30506	36	18	to	to	PART
fcis-30506	36	19	more	more	ADV
fcis-30506	36	20	accurately	accurately	ADV
fcis-30506	36	21	estimate	estimate	VERB
fcis-30506	36	22	future	future	ADJ
fcis-30506	36	23	liability	liability	NOUN
fcis-30506	36	24	for	for	ADP
fcis-30506	36	25	compensation	compensation	NOUN
fcis-30506	36	26	and	and	CCONJ
fcis-30506	36	27	avoid	avoid	VERB
fcis-30506	36	28	redundant	redundant	ADJ
fcis-30506	36	29	or	or	CCONJ
fcis-30506	36	30	insufficient	insufficient	ADJ
fcis-30506	36	31	reserves	reserve	NOUN
fcis-30506	36	32	.	.	PUNCT
fcis-30506	37	1	on	on	ADP
fcis-30506	37	2	the	the	DET
fcis-30506	37	3	other	other	ADJ
fcis-30506	37	4	hand	hand	NOUN
fcis-30506	37	5	,	,	PUNCT
fcis-30506	37	6	by	by	ADP
fcis-30506	37	7	leveraging	leverage	VERB
fcis-30506	37	8	the	the	DET
fcis-30506	37	9	bayesian	bayesian	NOUN
fcis-30506	37	10	characteristics	characteristic	NOUN
fcis-30506	37	11	of	of	ADP
fcis-30506	37	12	the	the	DET
fcis-30506	37	13	gaussian	gaussian	ADJ
fcis-30506	37	14	process	process	NOUN
fcis-30506	37	15	regression	regression	NOUN
fcis-30506	37	16	model	model	NOUN
fcis-30506	37	17	(	(	PUNCT
fcis-30506	37	18	gpr	gpr	PROPN
fcis-30506	37	19	)	)	PUNCT
fcis-30506	37	20	,	,	PUNCT
fcis-30506	37	21	it	it	PRON
fcis-30506	37	22	outputs	output	VERB
fcis-30506	37	23	the	the	DET
fcis-30506	37	24	confidence	confidence	NOUN
fcis-30506	37	25	intervals	interval	NOUN
fcis-30506	37	26	of	of	ADP
fcis-30506	37	27	the	the	DET
fcis-30506	37	28	population	population	NOUN
fcis-30506	37	29	mortality	mortality	NOUN
fcis-30506	37	30	rates	rate	NOUN
fcis-30506	37	31	in	in	ADP
fcis-30506	37	32	each	each	DET
fcis-30506	37	33	province	province	NOUN
fcis-30506	37	34	,	,	PUNCT
fcis-30506	37	35	conducting	conduct	VERB
fcis-30506	37	36	uncertainty	uncertainty	NOUN
fcis-30506	37	37	quantification	quantification	NOUN
fcis-30506	37	38	and	and	CCONJ
fcis-30506	37	39	providing	provide	VERB
fcis-30506	37	40	probabilistic	probabilistic	ADJ
fcis-30506	37	41	support	support	NOUN
fcis-30506	37	42	for	for	ADP
fcis-30506	37	43	risk	risk	NOUN
fcis-30506	37	44	management	management	NOUN
fcis-30506	37	45	.	.	PUNCT
fcis-30506	38	1	moreover	moreover	ADV
fcis-30506	38	2	,	,	PUNCT
fcis-30506	38	3	through	through	ADP
fcis-30506	38	4	empirical	empirical	ADJ
fcis-30506	38	5	research	research	NOUN
fcis-30506	38	6	,	,	PUNCT
fcis-30506	38	7	it	it	PRON
fcis-30506	38	8	has	have	AUX
fcis-30506	38	9	been	be	AUX
fcis-30506	38	10	proved	prove	VERB
fcis-30506	38	11	that	that	SCONJ
fcis-30506	38	12	the	the	DET
fcis-30506	38	13	performance	performance	NOUN
fcis-30506	38	14	of	of	ADP
fcis-30506	38	15	this	this	DET
fcis-30506	38	16	model	model	NOUN
fcis-30506	38	17	is	be	AUX
fcis-30506	38	18	excellent	excellent	ADJ
fcis-30506	38	19	and	and	CCONJ
fcis-30506	38	20	the	the	DET
fcis-30506	38	21	results	result	NOUN
fcis-30506	38	22	are	be	AUX
fcis-30506	38	23	effective	effective	ADJ
fcis-30506	38	24	.	.	PUNCT
fcis-30506	39	1	based	base	VERB
fcis-30506	39	2	on	on	ADP
fcis-30506	39	3	this	this	PRON
fcis-30506	39	4	,	,	PUNCT
fcis-30506	39	5	this	this	DET
fcis-30506	39	6	paper	paper	NOUN
fcis-30506	39	7	proposes	propose	VERB
fcis-30506	39	8	to	to	PART
fcis-30506	39	9	use	use	VERB
fcis-30506	39	10	a	a	DET
fcis-30506	39	11	two	two	NUM
fcis-30506	39	12	-	-	PUNCT
fcis-30506	39	13	layer	layer	NOUN
fcis-30506	39	14	gaussian	gaussian	ADJ
fcis-30506	39	15	process	process	NOUN
fcis-30506	39	16	ensemble	ensemble	ADJ
fcis-30506	39	17	regression	regression	NOUN
fcis-30506	39	18	model	model	NOUN
fcis-30506	39	19	to	to	PART
fcis-30506	39	20	incorporate	incorporate	VERB
fcis-30506	39	21	geographical	geographical	ADJ
fcis-30506	39	22	factors	factor	NOUN
fcis-30506	39	23	.	.	PUNCT
fcis-30506	40	1	on	on	ADP
fcis-30506	40	2	one	one	NUM
fcis-30506	40	3	hand	hand	NOUN
fcis-30506	40	4	,	,	PUNCT
fcis-30506	40	5	it	it	PRON
fcis-30506	40	6	accurately	accurately	ADV
fcis-30506	40	7	predicts	predict	VERB
fcis-30506	40	8	the	the	DET
fcis-30506	40	9	mortality	mortality	NOUN
fcis-30506	40	10	rates	rate	NOUN
fcis-30506	40	11	of	of	ADP
fcis-30506	40	12	31	31	NUM
fcis-30506	40	13	provinces	province	NOUN
fcis-30506	40	14	,	,	PUNCT
fcis-30506	40	15	enabling	enable	VERB
fcis-30506	40	16	insurance	insurance	NOUN
fcis-30506	40	17	companies	company	NOUN
fcis-30506	40	18	to	to	PART
fcis-30506	40	19	more	more	ADV
fcis-30506	40	20	accurately	accurately	ADV
fcis-30506	40	21	estimate	estimate	VERB
fcis-30506	40	22	future	future	ADJ
fcis-30506	40	23	liability	liability	NOUN
fcis-30506	40	24	for	for	ADP
fcis-30506	40	25	compensation	compensation	NOUN
fcis-30506	40	26	and	and	CCONJ
fcis-30506	40	27	avoid	avoid	VERB
fcis-30506	40	28	redundant	redundant	ADJ
fcis-30506	40	29	or	or	CCONJ
fcis-30506	40	30	insufficient	insufficient	ADJ
fcis-30506	40	31	reserves	reserve	NOUN
fcis-30506	40	32	.	.	PUNCT
fcis-30506	41	1	on	on	ADP
fcis-30506	41	2	the	the	DET
fcis-30506	41	3	other	other	ADJ
fcis-30506	41	4	hand	hand	NOUN
fcis-30506	41	5	,	,	PUNCT
fcis-30506	41	6	by	by	ADP
fcis-30506	41	7	leveraging	leverage	VERB
fcis-30506	41	8	the	the	DET
fcis-30506	41	9	bayesian	bayesian	NOUN
fcis-30506	41	10	characteristics	characteristic	NOUN
fcis-30506	41	11	of	of	ADP
fcis-30506	41	12	the	the	DET
fcis-30506	41	13	gaussian	gaussian	ADJ
fcis-30506	41	14	process	process	NOUN
fcis-30506	41	15	regression	regression	NOUN
fcis-30506	41	16	model	model	NOUN
fcis-30506	41	17	(	(	PUNCT
fcis-30506	41	18	gpr	gpr	PROPN
fcis-30506	41	19	)	)	PUNCT
fcis-30506	41	20	,	,	PUNCT
fcis-30506	41	21	it	it	PRON
fcis-30506	41	22	outputs	output	VERB
fcis-30506	41	23	the	the	DET
fcis-30506	41	24	confidence	confidence	NOUN
fcis-30506	41	25	intervals	interval	NOUN
fcis-30506	41	26	of	of	ADP
fcis-30506	41	27	the	the	DET
fcis-30506	41	28	population	population	NOUN
fcis-30506	41	29	mortality	mortality	NOUN
fcis-30506	41	30	rates	rate	NOUN
fcis-30506	41	31	in	in	ADP
fcis-30506	41	32	31	31	NUM
fcis-30506	41	33	provinces	province	NOUN
fcis-30506	41	34	,	,	PUNCT
fcis-30506	41	35	conducting	conduct	VERB
fcis-30506	41	36	uncertainty	uncertainty	NOUN
fcis-30506	41	37	quantification	quantification	NOUN
fcis-30506	41	38	estimation	estimation	NOUN
fcis-30506	41	39	and	and	CCONJ
fcis-30506	41	40	providing	provide	VERB
fcis-30506	41	41	probabilistic	probabilistic	ADJ
fcis-30506	41	42	support	support	NOUN
fcis-30506	41	43	for	for	ADP
fcis-30506	41	44	risk	risk	NOUN
fcis-30506	41	45	management	management	NOUN
fcis-30506	41	46	.	.	PUNCT
fcis-30506	42	1	moreover	moreover	ADV
fcis-30506	42	2	,	,	PUNCT
fcis-30506	42	3	through	through	ADP
fcis-30506	42	4	empirical	empirical	ADJ
fcis-30506	42	5	research	research	NOUN
fcis-30506	42	6	,	,	PUNCT
fcis-30506	42	7	it	it	PRON
fcis-30506	42	8	has	have	AUX
fcis-30506	42	9	been	be	AUX
fcis-30506	42	10	proved	prove	VERB
fcis-30506	42	11	that	that	SCONJ
fcis-30506	42	12	the	the	DET
fcis-30506	42	13	prediction	prediction	NOUN
fcis-30506	42	14	results	result	NOUN
fcis-30506	42	15	and	and	CCONJ
fcis-30506	42	16	performance	performance	NOUN
fcis-30506	42	17	of	of	ADP
fcis-30506	42	18	this	this	DET
fcis-30506	42	19	model	model	NOUN
fcis-30506	42	20	are	be	AUX
fcis-30506	42	21	more	more	ADV
fcis-30506	42	22	outstanding	outstanding	ADJ
fcis-30506	42	23	.	.	PUNCT
fcis-30506	43	1	2	2	X
fcis-30506	43	2	.	.	X
fcis-30506	43	3	theory	theory	NOUN
fcis-30506	43	4	and	and	CCONJ
fcis-30506	43	5	methodology	methodology	NOUN
fcis-30506	43	6	2.1	2.1	NUM
fcis-30506	43	7	.	.	PUNCT
fcis-30506	44	1	gaussian	gaussian	ADJ
fcis-30506	44	2	process	process	NOUN
fcis-30506	44	3	regression	regression	NOUN
fcis-30506	44	4	gaussian	gaussian	ADJ
fcis-30506	44	5	process	process	NOUN
fcis-30506	44	6	regression	regression	NOUN
fcis-30506	44	7	(	(	PUNCT
fcis-30506	44	8	gpr	gpr	PROPN
fcis-30506	44	9	)	)	PUNCT
fcis-30506	44	10	is	be	AUX
fcis-30506	44	11	a	a	DET
fcis-30506	44	12	bayesian	bayesian	NOUN
fcis-30506	44	13	method	method	NOUN
fcis-30506	44	14	for	for	ADP
fcis-30506	44	15	nonlinear	nonlinear	ADJ
fcis-30506	44	16	regression	regression	NOUN
fcis-30506	44	17	,	,	PUNCT
fcis-30506	44	18	which	which	PRON
fcis-30506	44	19	can	can	AUX
fcis-30506	44	20	be	be	AUX
fcis-30506	44	21	used	use	VERB
fcis-30506	44	22	to	to	PART
fcis-30506	44	23	achieve	achieve	VERB
fcis-30506	44	24	nonlinear	nonlinear	ADJ
fcis-30506	44	25	compensation	compensation	NOUN
fcis-30506	44	26	.	.	PUNCT
fcis-30506	45	1	gaussian	gaussian	ADJ
fcis-30506	45	2	process	process	NOUN
fcis-30506	45	3	is	be	AUX
fcis-30506	45	4	modeled	model	VERB
fcis-30506	45	5	as	as	ADP
fcis-30506	45	6	f	f	PROPN
fcis-30506	45	7	x	x	VERB
fcis-30506	45	8	with	with	ADP
fcis-30506	45	9	mean	mean	ADJ
fcis-30506	45	10	function	function	NOUN
fcis-30506	45	11	m	m	VERB
fcis-30506	45	12	x	x	NOUN
fcis-30506	45	13	and	and	CCONJ
fcis-30506	45	14	covariance	covariance	NOUN
fcis-30506	45	15	kernel	kernel	PROPN
fcis-30506	45	16	k	k	PROPN
fcis-30506	45	17	x	x	PROPN
fcis-30506	45	18	,	,	PUNCT
fcis-30506	45	19	x′	x′	PROPN
fcis-30506	45	20	:	:	PUNCT
fcis-30506	46	1	f	f	X
fcis-30506	46	2	x	x	PUNCT
fcis-30506	47	1	~gp	~gp	PROPN
fcis-30506	47	2	m	m	NUM
fcis-30506	47	3	x	x	X
fcis-30506	47	4	,	,	PUNCT
fcis-30506	47	5	k	k	PROPN
fcis-30506	47	6	x	x	X
fcis-30506	47	7	,	,	PUNCT
fcis-30506	47	8	x	x	SYM
fcis-30506	47	9	1	1	NUM
fcis-30506	47	10	given	give	VERB
fcis-30506	47	11	n	n	NUM
fcis-30506	47	12	data	datum	NOUN
fcis-30506	47	13	points	point	NOUN
fcis-30506	47	14	:	:	PUNCT
fcis-30506	47	15	x	x	PUNCT
fcis-30506	47	16	x	x	X
fcis-30506	47	17	,	,	PUNCT
fcis-30506	47	18	x	x	X
fcis-30506	47	19	,	,	PUNCT
fcis-30506	47	20	⋯	⋯	PROPN
fcis-30506	47	21	,	,	PUNCT
fcis-30506	47	22	x	x	X
fcis-30506	47	23	and	and	CCONJ
fcis-30506	47	24	the	the	DET
fcis-30506	47	25	corresponding	corresponding	ADJ
fcis-30506	47	26	observed	observe	VERB
fcis-30506	47	27	value	value	NOUN
fcis-30506	47	28	y	y	PROPN
fcis-30506	47	29	y	y	PROPN
fcis-30506	47	30	,	,	PUNCT
fcis-30506	47	31	y	y	PROPN
fcis-30506	47	32	,	,	PUNCT
fcis-30506	47	33	⋯	⋯	PROPN
fcis-30506	47	34	,	,	PUNCT
fcis-30506	47	35	y	y	PROPN
fcis-30506	47	36	,	,	PUNCT
fcis-30506	47	37	it	it	PRON
fcis-30506	47	38	can	can	AUX
fcis-30506	47	39	form	form	VERB
fcis-30506	47	40	a	a	DET
fcis-30506	47	41	multivariate	multivariate	NOUN
fcis-30506	47	42	matrix	matrix	NOUN
fcis-30506	47	43	:	:	PUNCT
fcis-30506	47	44	⋮	⋮	NOUN
fcis-30506	47	45	~	~	PUNCT
fcis-30506	47	46	⋮	⋮	NOUN
fcis-30506	47	47	,	,	PUNCT
fcis-30506	47	48	,	,	PUNCT
fcis-30506	47	49	,	,	PUNCT
fcis-30506	47	50	⋯	⋯	PROPN
fcis-30506	47	51	,	,	PUNCT
fcis-30506	47	52	,	,	PUNCT
fcis-30506	47	53	,	,	PUNCT
fcis-30506	47	54	⋯	⋯	NOUN
fcis-30506	47	55	,	,	PUNCT
fcis-30506	47	56	⋮	⋮	NOUN
fcis-30506	47	57	,	,	PUNCT
fcis-30506	47	58	,	,	PUNCT
fcis-30506	47	59	⋯	⋯	PROPN
fcis-30506	47	60	,	,	PUNCT
fcis-30506	47	61	2	2	NUM
fcis-30506	47	62	where	where	SCONJ
fcis-30506	47	63	is	be	AUX
fcis-30506	47	64	variance	variance	NOUN
fcis-30506	47	65	,	,	PUNCT
fcis-30506	47	66	i	i	PRON
fcis-30506	47	67	is	be	AUX
fcis-30506	47	68	identity	identity	NOUN
fcis-30506	47	69	matrix	matrix	NOUN
fcis-30506	47	70	.	.	PUNCT
fcis-30506	48	1	based	base	VERB
fcis-30506	48	2	on	on	ADP
fcis-30506	48	3	the	the	DET
fcis-30506	48	4	observational	observational	ADJ
fcis-30506	48	5	data	datum	NOUN
fcis-30506	48	6	,	,	PUNCT
fcis-30506	48	7	the	the	DET
fcis-30506	48	8	posterior	posterior	ADJ
fcis-30506	48	9	distribution	distribution	NOUN
fcis-30506	48	10	can	can	AUX
fcis-30506	48	11	be	be	AUX
fcis-30506	48	12	obtained	obtain	VERB
fcis-30506	48	13	.	.	PUNCT
fcis-30506	49	1	given	give	VERB
fcis-30506	49	2	new	new	ADJ
fcis-30506	49	3	data	datum	NOUN
fcis-30506	49	4	points	point	NOUN
fcis-30506	49	5	∗	∗	NOUN
fcis-30506	49	6	,	,	PUNCT
fcis-30506	49	7	the	the	DET
fcis-30506	49	8	posterior	posterior	ADJ
fcis-30506	49	9	distribution	distribution	NOUN
fcis-30506	49	10	of	of	ADP
fcis-30506	49	11	∗	∗	NOUN
fcis-30506	49	12	corresponding	correspond	VERB
fcis-30506	49	13	to	to	ADP
fcis-30506	49	14	this	this	DET
fcis-30506	49	15	output	output	NOUN
fcis-30506	49	16	is	be	AUX
fcis-30506	49	17	:	:	PUNCT
fcis-30506	49	18	∗|	∗|	PROPN
fcis-30506	49	19	,	,	PUNCT
fcis-30506	49	20	,	,	PUNCT
fcis-30506	49	21	∗~	∗~	PROPN
fcis-30506	49	22	∗	∗	NOUN
fcis-30506	49	23	,	,	PUNCT
fcis-30506	49	24	∗	∗	NOUN
fcis-30506	49	25	3	3	NUM
fcis-30506	49	26	∗	∗	NOUN
fcis-30506	49	27	∗	∗	NOUN
fcis-30506	49	28	∗	∗	NOUN
fcis-30506	49	29	4	4	NUM
fcis-30506	49	30	∗	∗	NOUN
fcis-30506	49	31	∗	∗	NOUN
fcis-30506	49	32	,	,	PUNCT
fcis-30506	49	33	∗	∗	NOUN
fcis-30506	49	34	∗	∗	NOUN
fcis-30506	49	35	∗	∗	NOUN
fcis-30506	49	36	5	5	NUM
fcis-30506	49	37	∗	∗	NOUN
fcis-30506	49	38	∗	∗	NOUN
fcis-30506	49	39	,	,	PUNCT
fcis-30506	49	40	,	,	PUNCT
fcis-30506	49	41	∗	∗	NOUN
fcis-30506	49	42	,	,	PUNCT
fcis-30506	49	43	,	,	PUNCT
fcis-30506	49	44	⋯	⋯	PROPN
fcis-30506	49	45	,	,	PUNCT
fcis-30506	49	46	∗	∗	NOUN
fcis-30506	49	47	,	,	PUNCT
fcis-30506	49	48	6	6	NUM
fcis-30506	49	49	where	where	SCONJ
fcis-30506	49	50	k	k	PROPN
fcis-30506	49	51	is	be	AUX
fcis-30506	49	52	covariance	covariance	NOUN
fcis-30506	49	53	matrix	matrix	NOUN
fcis-30506	49	54	,	,	PUNCT
fcis-30506	49	55	,	,	PUNCT
fcis-30506	49	56	;	;	PUNCT
fcis-30506	49	57	m	m	VERB
fcis-30506	49	58	is	be	AUX
fcis-30506	49	59	mean	mean	ADJ
fcis-30506	49	60	vector	vector	NOUN
fcis-30506	49	61	,	,	PUNCT
fcis-30506	49	62	m	m	VERB
fcis-30506	49	63	m	m	VERB
fcis-30506	49	64	x	x	X
fcis-30506	49	65	,	,	PUNCT
fcis-30506	49	66	m	m	VERB
fcis-30506	49	67	x	x	INTJ
fcis-30506	49	68	,	,	PUNCT
fcis-30506	49	69	⋯	⋯	PROPN
fcis-30506	49	70	,	,	PUNCT
fcis-30506	49	71	m	m	PROPN
fcis-30506	49	72	x	x	X
fcis-30506	49	73	.	.	PUNCT
fcis-30506	50	1	the	the	DET
fcis-30506	50	2	gaussian	gaussian	ADJ
fcis-30506	50	3	process	process	NOUN
fcis-30506	50	4	regression	regression	NOUN
fcis-30506	50	5	model	model	NOUN
fcis-30506	50	6	not	not	PART
fcis-30506	50	7	only	only	ADV
fcis-30506	50	8	provides	provide	VERB
fcis-30506	50	9	point	point	NOUN
fcis-30506	50	10	prediction	prediction	NOUN
fcis-30506	50	11	values	value	NOUN
fcis-30506	50	12	,	,	PUNCT
fcis-30506	50	13	but	but	CCONJ
fcis-30506	50	14	also	also	ADV
fcis-30506	50	15	gives	give	VERB
fcis-30506	50	16	the	the	DET
fcis-30506	50	17	confidence	confidence	NOUN
fcis-30506	50	18	interval	interval	NOUN
fcis-30506	50	19	of	of	ADP
fcis-30506	50	20	mortality	mortality	NOUN
fcis-30506	50	21	rate	rate	NOUN
fcis-30506	50	22	with	with	ADP
fcis-30506	50	23	a	a	DET
fcis-30506	50	24	certain	certain	ADJ
fcis-30506	50	25	confidence	confidence	NOUN
fcis-30506	50	26	level	level	NOUN
fcis-30506	50	27	,	,	PUNCT
fcis-30506	50	28	quantifying	quantify	VERB
fcis-30506	50	29	the	the	DET
fcis-30506	50	30	uncertainty	uncertainty	NOUN
fcis-30506	50	31	of	of	ADP
fcis-30506	50	32	the	the	DET
fcis-30506	50	33	prediction	prediction	NOUN
fcis-30506	50	34	:	:	PUNCT
fcis-30506	50	35	∈	∈	NOUN
fcis-30506	50	36	∗	∗	NOUN
fcis-30506	50	37	∙	∙	PROPN
fcis-30506	50	38	∗	∗	NOUN
fcis-30506	50	39	√	√	PROPN
fcis-30506	50	40	∗	∗	NOUN
fcis-30506	50	41	∙	∙	PROPN
fcis-30506	50	42	∗	∗	NOUN
fcis-30506	50	43	√	√	NUM
fcis-30506	50	44	7	7	NUM
fcis-30506	50	45	2.2	2.2	NUM
fcis-30506	50	46	.	.	PUNCT
fcis-30506	51	1	double	double	ADJ
fcis-30506	51	2	-	-	PUNCT
fcis-30506	51	3	layer	layer	NOUN
fcis-30506	51	4	gaussian	gaussian	ADJ
fcis-30506	51	5	process	process	NOUN
fcis-30506	51	6	ensemble	ensemble	ADJ
fcis-30506	51	7	regression	regression	NOUN
fcis-30506	51	8	figure	figure	NOUN
fcis-30506	51	9	1	1	NUM
fcis-30506	51	10	.	.	PUNCT
fcis-30506	51	11	schematic	schematic	ADJ
fcis-30506	51	12	diagram	diagram	NOUN
fcis-30506	51	13	of	of	ADP
fcis-30506	51	14	dgpr	dgpr	NOUN
fcis-30506	51	15	66	66	NUM
fcis-30506	51	16	stacking	stack	VERB
fcis-30506	51	17	ensemble	ensemble	ADJ
fcis-30506	51	18	learning	learning	NOUN
fcis-30506	51	19	model	model	NOUN
fcis-30506	51	20	is	be	AUX
fcis-30506	51	21	a	a	DET
fcis-30506	51	22	hierarchical	hierarchical	ADJ
fcis-30506	51	23	integration	integration	NOUN
fcis-30506	51	24	architecture	architecture	NOUN
fcis-30506	51	25	that	that	PRON
fcis-30506	51	26	integrates	integrate	VERB
fcis-30506	51	27	different	different	ADJ
fcis-30506	51	28	algorithm	algorithm	NOUN
fcis-30506	51	29	models	model	NOUN
fcis-30506	51	30	.	.	PUNCT
fcis-30506	52	1	here	here	ADV
fcis-30506	52	2	,	,	PUNCT
fcis-30506	52	3	multiple	multiple	ADJ
fcis-30506	52	4	single	single	ADJ
fcis-30506	52	5	gaussian	gaussian	ADJ
fcis-30506	52	6	process	process	NOUN
fcis-30506	52	7	regression	regression	NOUN
fcis-30506	52	8	models	model	NOUN
fcis-30506	52	9	are	be	AUX
fcis-30506	52	10	first	first	ADV
fcis-30506	52	11	grouped	group	VERB
fcis-30506	52	12	to	to	PART
fcis-30506	52	13	form	form	VERB
fcis-30506	52	14	sub	sub	NOUN
fcis-30506	52	15	-	-	NOUN
fcis-30506	52	16	models	model	NOUN
fcis-30506	52	17	for	for	ADP
fcis-30506	52	18	the	the	DET
fcis-30506	52	19	first	first	ADJ
fcis-30506	52	20	layer	layer	NOUN
fcis-30506	52	21	of	of	ADP
fcis-30506	52	22	parallel	parallel	ADJ
fcis-30506	52	23	learning	learning	NOUN
fcis-30506	52	24	data	datum	NOUN
fcis-30506	52	25	.	.	PUNCT
fcis-30506	53	1	then	then	ADV
fcis-30506	53	2	,	,	PUNCT
fcis-30506	53	3	the	the	DET
fcis-30506	53	4	results	result	NOUN
fcis-30506	53	5	obtained	obtain	VERB
fcis-30506	53	6	from	from	ADP
fcis-30506	53	7	each	each	DET
fcis-30506	53	8	sub	sub	NOUN
fcis-30506	53	9	-	-	NOUN
fcis-30506	53	10	model	model	NOUN
fcis-30506	53	11	are	be	AUX
fcis-30506	53	12	input	input	VERB
fcis-30506	53	13	into	into	ADP
fcis-30506	53	14	the	the	DET
fcis-30506	53	15	second	second	ADJ
fcis-30506	53	16	layer	layer	NOUN
fcis-30506	53	17	of	of	ADP
fcis-30506	53	18	meta	meta	NOUN
fcis-30506	53	19	-	-	PUNCT
fcis-30506	53	20	model	model	NOUN
fcis-30506	53	21	for	for	ADP
fcis-30506	53	22	training	training	NOUN
fcis-30506	53	23	,	,	PUNCT
fcis-30506	53	24	thereby	thereby	ADV
fcis-30506	53	25	obtaining	obtain	VERB
fcis-30506	53	26	a	a	DET
fcis-30506	53	27	complete	complete	ADJ
fcis-30506	53	28	two	two	NUM
fcis-30506	53	29	-	-	PUNCT
fcis-30506	53	30	layer	layer	NOUN
fcis-30506	53	31	gaussian	gaussian	ADJ
fcis-30506	53	32	process	process	NOUN
fcis-30506	53	33	ensemble	ensemble	ADJ
fcis-30506	53	34	learning	learning	NOUN
fcis-30506	53	35	model	model	NOUN
fcis-30506	53	36	.	.	PUNCT
fcis-30506	54	1	the	the	DET
fcis-30506	54	2	model	model	NOUN
fcis-30506	54	3	uses	use	VERB
fcis-30506	54	4	the	the	DET
fcis-30506	54	5	prediction	prediction	NOUN
fcis-30506	54	6	data	datum	NOUN
fcis-30506	54	7	of	of	ADP
fcis-30506	54	8	the	the	DET
fcis-30506	54	9	sub	sub	NOUN
fcis-30506	54	10	-	-	NOUN
fcis-30506	54	11	models	model	NOUN
fcis-30506	54	12	as	as	ADP
fcis-30506	54	13	the	the	DET
fcis-30506	54	14	training	training	NOUN
fcis-30506	54	15	data	datum	NOUN
fcis-30506	54	16	of	of	ADP
fcis-30506	54	17	the	the	DET
fcis-30506	54	18	meta	meta	NOUN
fcis-30506	54	19	-	-	PUNCT
fcis-30506	54	20	model	model	NOUN
fcis-30506	54	21	,	,	PUNCT
fcis-30506	54	22	reducing	reduce	VERB
fcis-30506	54	23	the	the	DET
fcis-30506	54	24	generalization	generalization	NOUN
fcis-30506	54	25	error	error	NOUN
fcis-30506	54	26	and	and	CCONJ
fcis-30506	54	27	overfitting	overfitte	VERB
fcis-30506	54	28	problem	problem	NOUN
fcis-30506	54	29	of	of	ADP
fcis-30506	54	30	a	a	DET
fcis-30506	54	31	single	single	ADJ
fcis-30506	54	32	model	model	NOUN
fcis-30506	54	33	and	and	CCONJ
fcis-30506	54	34	improving	improve	VERB
fcis-30506	54	35	the	the	DET
fcis-30506	54	36	prediction	prediction	NOUN
fcis-30506	54	37	accuracy	accuracy	NOUN
fcis-30506	54	38	.	.	PUNCT
fcis-30506	55	1	3	3	X
fcis-30506	55	2	.	.	X
fcis-30506	55	3	empirical	empirical	ADJ
fcis-30506	55	4	analysis	analysis	NOUN
fcis-30506	55	5	3.1	3.1	NUM
fcis-30506	55	6	.	.	PUNCT
fcis-30506	56	1	simulation	simulation	NOUN
fcis-30506	56	2	data	datum	NOUN
fcis-30506	56	3	analysis	analysis	NOUN
fcis-30506	56	4	the	the	DET
fcis-30506	56	5	simulated	simulated	ADJ
fcis-30506	56	6	data	data	NOUN
fcis-30506	56	7	consists	consist	VERB
fcis-30506	56	8	of	of	ADP
fcis-30506	56	9	200	200	NUM
fcis-30506	56	10	samples	sample	NOUN
fcis-30506	56	11	.	.	PUNCT
fcis-30506	57	1	the	the	DET
fcis-30506	57	2	independent	independent	ADJ
fcis-30506	57	3	variable	variable	NOUN
fcis-30506	57	4	x	x	PUNCT
fcis-30506	57	5	is	be	AUX
fcis-30506	57	6	a	a	DET
fcis-30506	57	7	random	random	ADJ
fcis-30506	57	8	sample	sample	NOUN
fcis-30506	57	9	drawn	draw	VERB
fcis-30506	57	10	from	from	ADP
fcis-30506	57	11	the	the	DET
fcis-30506	57	12	standard	standard	ADJ
fcis-30506	57	13	normal	normal	ADJ
fcis-30506	57	14	distribution	distribution	NOUN
fcis-30506	57	15	,	,	PUNCT
fcis-30506	57	16	and	and	CCONJ
fcis-30506	57	17	the	the	DET
fcis-30506	57	18	dependent	dependent	ADJ
fcis-30506	57	19	variable	variable	NOUN
fcis-30506	57	20	y	y	PROPN
fcis-30506	57	21	is	be	AUX
fcis-30506	57	22	generated	generate	VERB
fcis-30506	57	23	by	by	ADP
fcis-30506	57	24	the	the	DET
fcis-30506	57	25	following	follow	VERB
fcis-30506	57	26	formula	formula	NOUN
fcis-30506	57	27	:	:	PUNCT
fcis-30506	57	28	y	y	NOUN
fcis-30506	57	29	sin	sin	VERB
fcis-30506	57	30	x	x	PUNCT
fcis-30506	57	31	ϵ	ϵ	X
fcis-30506	57	32	8	8	NUM
fcis-30506	57	33	where	where	SCONJ
fcis-30506	57	34	is	be	AUX
fcis-30506	57	35	random	random	ADJ
fcis-30506	57	36	noise	noise	NOUN
fcis-30506	57	37	,	,	PUNCT
fcis-30506	57	38	following	follow	VERB
fcis-30506	57	39	a	a	DET
fcis-30506	57	40	normal	normal	ADJ
fcis-30506	57	41	distribution	distribution	NOUN
fcis-30506	57	42	n	n	NOUN
fcis-30506	57	43	0	0	NUM
fcis-30506	57	44	,	,	PUNCT
fcis-30506	57	45	0.1	0.1	NUM
fcis-30506	57	46	.	.	PUNCT
fcis-30506	58	1	in	in	ADP
fcis-30506	58	2	the	the	DET
fcis-30506	58	3	simulated	simulate	VERB
fcis-30506	58	4	data	datum	NOUN
fcis-30506	58	5	set	set	VERB
fcis-30506	58	6	,	,	PUNCT
fcis-30506	58	7	80	80	NUM
fcis-30506	58	8	%	%	NOUN
fcis-30506	58	9	of	of	ADP
fcis-30506	58	10	the	the	DET
fcis-30506	58	11	data	datum	NOUN
fcis-30506	58	12	is	be	AUX
fcis-30506	58	13	randomly	randomly	ADV
fcis-30506	58	14	selected	select	VERB
fcis-30506	58	15	as	as	ADP
fcis-30506	58	16	the	the	DET
fcis-30506	58	17	training	training	NOUN
fcis-30506	58	18	set	set	NOUN
fcis-30506	58	19	,	,	PUNCT
fcis-30506	58	20	while	while	SCONJ
fcis-30506	58	21	20	20	NUM
fcis-30506	58	22	%	%	NOUN
fcis-30506	58	23	of	of	ADP
fcis-30506	58	24	the	the	DET
fcis-30506	58	25	data	datum	NOUN
fcis-30506	58	26	is	be	AUX
fcis-30506	58	27	set	set	VERB
fcis-30506	58	28	aside	aside	ADV
fcis-30506	58	29	as	as	ADP
fcis-30506	58	30	the	the	DET
fcis-30506	58	31	test	test	NOUN
fcis-30506	58	32	set	set	VERB
fcis-30506	58	33	.	.	PUNCT
fcis-30506	59	1	in	in	ADP
fcis-30506	59	2	order	order	NOUN
fcis-30506	59	3	to	to	PART
fcis-30506	59	4	evaluate	evaluate	VERB
fcis-30506	59	5	the	the	DET
fcis-30506	59	6	predictive	predictive	ADJ
fcis-30506	59	7	performance	performance	NOUN
fcis-30506	59	8	of	of	ADP
fcis-30506	59	9	the	the	DET
fcis-30506	59	10	proposed	propose	VERB
fcis-30506	59	11	model	model	NOUN
fcis-30506	59	12	,	,	PUNCT
fcis-30506	59	13	three	three	NUM
fcis-30506	59	14	commonly	commonly	ADV
fcis-30506	59	15	used	use	VERB
fcis-30506	59	16	evaluation	evaluation	NOUN
fcis-30506	59	17	metrics	metric	NOUN
fcis-30506	59	18	were	be	AUX
fcis-30506	59	19	adopted	adopt	VERB
fcis-30506	59	20	,	,	PUNCT
fcis-30506	59	21	including	include	VERB
fcis-30506	59	22	:	:	PUNCT
fcis-30506	59	23	mean	mean	VERB
fcis-30506	59	24	squared	square	VERB
fcis-30506	59	25	error(mse	error(mse	NOUN
fcis-30506	59	26	)	)	PUNCT
fcis-30506	59	27	,	,	PUNCT
fcis-30506	59	28	mean	mean	VERB
fcis-30506	59	29	absolute	absolute	ADJ
fcis-30506	59	30	error	error	NOUN
fcis-30506	59	31	(	(	PUNCT
fcis-30506	59	32	mae	mae	PROPN
fcis-30506	59	33	)	)	PUNCT
fcis-30506	59	34	,	,	PUNCT
fcis-30506	59	35	mean	mean	VERB
fcis-30506	59	36	relative	relative	ADJ
fcis-30506	59	37	error	error	NOUN
fcis-30506	59	38	(	(	PUNCT
fcis-30506	59	39	mre	mre	NOUN
fcis-30506	59	40	)	)	PUNCT
fcis-30506	59	41	.	.	PUNCT
fcis-30506	60	1	mse	mse	PROPN
fcis-30506	60	2	9	9	NUM
fcis-30506	60	3	mae	mae	PROPN
fcis-30506	61	1	|	|	ADV
fcis-30506	61	2	|	|	ADV
fcis-30506	61	3	10	10	NUM
fcis-30506	61	4	mre	mre	NOUN
fcis-30506	62	1	|	|	ADV
fcis-30506	62	2	|	|	ADV
fcis-30506	62	3	11	11	NUM
fcis-30506	62	4	where	where	SCONJ
fcis-30506	62	5	n	n	PRON
fcis-30506	62	6	is	be	AUX
fcis-30506	62	7	sample	sample	NOUN
fcis-30506	62	8	size	size	NOUN
fcis-30506	62	9	;	;	PUNCT
fcis-30506	62	10	is	be	AUX
fcis-30506	62	11	the	the	DET
fcis-30506	62	12	true	true	ADJ
fcis-30506	62	13	value	value	NOUN
fcis-30506	62	14	of	of	ADP
fcis-30506	62	15	sample	sample	NOUN
fcis-30506	62	16	i	i	PRON
fcis-30506	62	17	;	;	PUNCT
fcis-30506	62	18	is	be	AUX
fcis-30506	62	19	the	the	DET
fcis-30506	62	20	predicted	predict	VERB
fcis-30506	62	21	value	value	NOUN
fcis-30506	62	22	of	of	ADP
fcis-30506	62	23	sample	sample	NOUN
fcis-30506	62	24	i.	i.	NOUN
fcis-30506	62	25	based	base	VERB
fcis-30506	62	26	on	on	ADP
fcis-30506	62	27	the	the	DET
fcis-30506	62	28	simulated	simulate	VERB
fcis-30506	62	29	data	datum	NOUN
fcis-30506	62	30	,	,	PUNCT
fcis-30506	62	31	a	a	DET
fcis-30506	62	32	monte	monte	PROPN
fcis-30506	62	33	carlo	carlo	PROPN
fcis-30506	62	34	experiment	experiment	NOUN
fcis-30506	62	35	was	be	AUX
fcis-30506	62	36	conducted	conduct	VERB
fcis-30506	62	37	:	:	PUNCT
fcis-30506	62	38	the	the	DET
fcis-30506	62	39	experiment	experiment	NOUN
fcis-30506	62	40	found	find	VERB
fcis-30506	62	41	that	that	SCONJ
fcis-30506	62	42	the	the	DET
fcis-30506	62	43	predicted	predict	VERB
fcis-30506	62	44	values	value	NOUN
fcis-30506	62	45	of	of	ADP
fcis-30506	62	46	the	the	DET
fcis-30506	62	47	model	model	NOUN
fcis-30506	62	48	were	be	AUX
fcis-30506	62	49	basically	basically	ADV
fcis-30506	62	50	consistent	consistent	ADJ
fcis-30506	62	51	with	with	ADP
fcis-30506	62	52	the	the	DET
fcis-30506	62	53	true	true	ADJ
fcis-30506	62	54	values	value	NOUN
fcis-30506	62	55	,	,	PUNCT
fcis-30506	62	56	and	and	CCONJ
fcis-30506	62	57	the	the	DET
fcis-30506	62	58	simulation	simulation	NOUN
fcis-30506	62	59	effect	effect	NOUN
fcis-30506	62	60	was	be	AUX
fcis-30506	62	61	good	good	ADJ
fcis-30506	62	62	;	;	PUNCT
fcis-30506	62	63	as	as	ADV
fcis-30506	62	64	well	well	ADV
fcis-30506	62	65	as	as	ADP
fcis-30506	62	66	that	that	SCONJ
fcis-30506	62	67	the	the	DET
fcis-30506	62	68	double	double	ADJ
fcis-30506	62	69	-	-	PUNCT
fcis-30506	62	70	layer	layer	NOUN
fcis-30506	62	71	gaussian	gaussian	ADJ
fcis-30506	62	72	process	process	NOUN
fcis-30506	62	73	regression	regression	NOUN
fcis-30506	62	74	model	model	NOUN
fcis-30506	62	75	can	can	AUX
fcis-30506	62	76	be	be	AUX
fcis-30506	62	77	used	use	VERB
fcis-30506	62	78	to	to	PART
fcis-30506	62	79	fit	fit	VERB
fcis-30506	62	80	nonlinear	nonlinear	ADJ
fcis-30506	62	81	functions	function	NOUN
fcis-30506	62	82	.	.	PUNCT
fcis-30506	63	1	figure	figure	NOUN
fcis-30506	63	2	2	2	NUM
fcis-30506	63	3	.	.	PUNCT
fcis-30506	64	1	the	the	DET
fcis-30506	64	2	actual	actual	ADJ
fcis-30506	64	3	values	value	NOUN
fcis-30506	64	4	and	and	CCONJ
fcis-30506	64	5	predicted	predict	VERB
fcis-30506	64	6	values	value	NOUN
fcis-30506	64	7	of	of	ADP
fcis-30506	64	8	the	the	DET
fcis-30506	64	9	simulated	simulate	VERB
fcis-30506	64	10	data	datum	NOUN
fcis-30506	64	11	the	the	DET
fcis-30506	64	12	dgpr	dgpr	NOUN
fcis-30506	64	13	model	model	NOUN
fcis-30506	64	14	was	be	AUX
fcis-30506	64	15	compared	compare	VERB
fcis-30506	64	16	with	with	ADP
fcis-30506	64	17	the	the	DET
fcis-30506	64	18	random	random	ADJ
fcis-30506	64	19	forest	forest	NOUN
fcis-30506	64	20	,	,	PUNCT
fcis-30506	64	21	gpr	gpr	PROPN
fcis-30506	64	22	,	,	PUNCT
fcis-30506	64	23	xgboost	xgboost	ADV
fcis-30506	64	24	,	,	PUNCT
fcis-30506	64	25	and	and	CCONJ
fcis-30506	64	26	gbdt	gbdt	NOUN
fcis-30506	64	27	models	model	NOUN
fcis-30506	64	28	in	in	ADP
fcis-30506	64	29	terms	term	NOUN
fcis-30506	64	30	of	of	ADP
fcis-30506	64	31	model	model	NOUN
fcis-30506	64	32	performance	performance	NOUN
fcis-30506	64	33	.	.	PUNCT
fcis-30506	65	1	it	it	PRON
fcis-30506	65	2	was	be	AUX
fcis-30506	65	3	found	find	VERB
fcis-30506	65	4	that	that	SCONJ
fcis-30506	65	5	the	the	DET
fcis-30506	65	6	mean	mean	ADJ
fcis-30506	65	7	square	square	NOUN
fcis-30506	65	8	error	error	NOUN
fcis-30506	65	9	,	,	PUNCT
fcis-30506	65	10	mean	mean	ADJ
fcis-30506	65	11	absolute	absolute	ADJ
fcis-30506	65	12	error	error	NOUN
fcis-30506	65	13	and	and	CCONJ
fcis-30506	65	14	mean	mean	VERB
fcis-30506	65	15	relative	relative	ADJ
fcis-30506	65	16	error	error	NOUN
fcis-30506	65	17	of	of	ADP
fcis-30506	65	18	the	the	DET
fcis-30506	65	19	dgpr	dgpr	NOUN
fcis-30506	65	20	model	model	NOUN
fcis-30506	65	21	were	be	AUX
fcis-30506	65	22	all	all	ADV
fcis-30506	65	23	smaller	small	ADJ
fcis-30506	65	24	than	than	ADP
fcis-30506	65	25	those	those	PRON
fcis-30506	65	26	of	of	ADP
fcis-30506	65	27	other	other	ADJ
fcis-30506	65	28	models	model	NOUN
fcis-30506	65	29	,	,	PUNCT
fcis-30506	65	30	and	and	CCONJ
fcis-30506	65	31	it	it	PRON
fcis-30506	65	32	had	have	VERB
fcis-30506	65	33	excellent	excellent	ADJ
fcis-30506	65	34	performance	performance	NOUN
fcis-30506	65	35	and	and	CCONJ
fcis-30506	65	36	could	could	AUX
fcis-30506	65	37	predict	predict	VERB
fcis-30506	65	38	the	the	DET
fcis-30506	65	39	mortality	mortality	NOUN
fcis-30506	65	40	rate	rate	NOUN
fcis-30506	65	41	more	more	ADV
fcis-30506	65	42	accurately	accurately	ADV
fcis-30506	65	43	.	.	PUNCT
fcis-30506	66	1	3.2	3.2	NUM
fcis-30506	66	2	.	.	PUNCT
fcis-30506	67	1	real	real	ADJ
fcis-30506	67	2	data	datum	NOUN
fcis-30506	67	3	analysis	analysis	NOUN
fcis-30506	67	4	3.2.1	3.2.1	NUM
fcis-30506	67	5	.	.	PUNCT
fcis-30506	68	1	data	datum	NOUN
fcis-30506	68	2	sources	source	NOUN
fcis-30506	68	3	and	and	CCONJ
fcis-30506	68	4	pre	pre	NOUN
fcis-30506	68	5	-	-	NOUN
fcis-30506	68	6	analysis	analysis	VERB
fcis-30506	68	7	the	the	DET
fcis-30506	68	8	data	datum	NOUN
fcis-30506	68	9	selected	select	VERB
fcis-30506	68	10	for	for	ADP
fcis-30506	68	11	this	this	DET
fcis-30506	68	12	article	article	NOUN
fcis-30506	68	13	are	be	AUX
fcis-30506	68	14	from	from	ADP
fcis-30506	68	15	the	the	DET
fcis-30506	68	16	"	"	PUNCT
fcis-30506	68	17	national	national	ADJ
fcis-30506	68	18	statistical	statistical	ADJ
fcis-30506	68	19	yearbook	yearbook	NOUN
fcis-30506	68	20	"	"	PUNCT
fcis-30506	68	21	regarding	regard	VERB
fcis-30506	68	22	the	the	DET
fcis-30506	68	23	mortality	mortality	NOUN
fcis-30506	68	24	rates	rate	NOUN
fcis-30506	68	25	of	of	ADP
fcis-30506	68	26	the	the	DET
fcis-30506	68	27	population	population	NOUN
fcis-30506	68	28	in	in	ADP
fcis-30506	68	29	31	31	NUM
fcis-30506	68	30	provinces	province	NOUN
fcis-30506	68	31	of	of	ADP
fcis-30506	68	32	china	china	PROPN
fcis-30506	68	33	from	from	ADP
fcis-30506	68	34	2005	2005	NUM
fcis-30506	68	35	to	to	ADP
fcis-30506	68	36	2023	2023	NUM
fcis-30506	68	37	.	.	PUNCT
fcis-30506	69	1	during	during	ADP
fcis-30506	69	2	the	the	DET
fcis-30506	69	3	pre	pre	NOUN
fcis-30506	69	4	-	-	NOUN
fcis-30506	69	5	analysis	analysis	NOUN
fcis-30506	69	6	of	of	ADP
fcis-30506	69	7	this	this	DET
fcis-30506	69	8	data	datum	NOUN
fcis-30506	69	9	,	,	PUNCT
fcis-30506	69	10	it	it	PRON
fcis-30506	69	11	was	be	AUX
fcis-30506	69	12	discovered	discover	VERB
fcis-30506	69	13	that	that	SCONJ
fcis-30506	69	14	:	:	PUNCT
fcis-30506	69	15	the	the	DET
fcis-30506	69	16	dispersion	dispersion	NOUN
fcis-30506	69	17	degree	degree	NOUN
fcis-30506	69	18	of	of	ADP
fcis-30506	69	19	mortality	mortality	NOUN
fcis-30506	69	20	rates	rate	NOUN
fcis-30506	69	21	among	among	ADP
fcis-30506	69	22	the	the	DET
fcis-30506	69	23	31	31	NUM
fcis-30506	69	24	provinces	province	NOUN
fcis-30506	69	25	of	of	ADP
fcis-30506	69	26	china	china	PROPN
fcis-30506	69	27	fluctuated	fluctuate	VERB
fcis-30506	69	28	greatly	greatly	ADV
fcis-30506	69	29	from	from	ADP
fcis-30506	69	30	2005	2005	NUM
fcis-30506	69	31	to	to	ADP
fcis-30506	69	32	2023	2023	NUM
fcis-30506	69	33	.	.	PUNCT
fcis-30506	70	1	the	the	DET
fcis-30506	70	2	overall	overall	ADJ
fcis-30506	70	3	dispersion	dispersion	NOUN
fcis-30506	70	4	degree	degree	NOUN
fcis-30506	70	5	showed	show	VERB
fcis-30506	70	6	an	an	DET
fcis-30506	70	7	upward	upward	ADJ
fcis-30506	70	8	trend	trend	NOUN
fcis-30506	70	9	,	,	PUNCT
fcis-30506	70	10	indicating	indicate	VERB
fcis-30506	70	11	that	that	SCONJ
fcis-30506	70	12	the	the	DET
fcis-30506	70	13	mortality	mortality	NOUN
fcis-30506	70	14	rate	rate	NOUN
fcis-30506	70	15	differences	difference	NOUN
fcis-30506	70	16	among	among	ADP
fcis-30506	70	17	regions	region	NOUN
fcis-30506	70	18	were	be	AUX
fcis-30506	70	19	increasing	increase	VERB
fcis-30506	70	20	.	.	PUNCT
fcis-30506	71	1	this	this	PRON
fcis-30506	71	2	might	might	AUX
fcis-30506	71	3	be	be	AUX
fcis-30506	71	4	due	due	ADJ
fcis-30506	71	5	to	to	ADP
fcis-30506	71	6	the	the	DET
fcis-30506	71	7	expansion	expansion	NOUN
fcis-30506	71	8	of	of	ADP
fcis-30506	71	9	population	population	NOUN
fcis-30506	71	10	migration	migration	NOUN
fcis-30506	71	11	scale	scale	NOUN
fcis-30506	71	12	in	in	ADP
fcis-30506	71	13	china	china	PROPN
fcis-30506	71	14	,	,	PUNCT
fcis-30506	71	15	the	the	DET
fcis-30506	71	16	uneven	uneven	ADJ
fcis-30506	71	17	spatial	spatial	ADJ
fcis-30506	71	18	distribution	distribution	NOUN
fcis-30506	71	19	of	of	ADP
fcis-30506	71	20	medical	medical	ADJ
fcis-30506	71	21	resources	resource	NOUN
fcis-30506	71	22	,	,	PUNCT
fcis-30506	71	23	and	and	CCONJ
fcis-30506	71	24	regional	regional	ADJ
fcis-30506	71	25	differences	difference	NOUN
fcis-30506	71	26	such	such	ADJ
fcis-30506	71	27	as	as	ADP
fcis-30506	71	28	environmental	environmental	ADJ
fcis-30506	71	29	differences	difference	NOUN
fcis-30506	71	30	,	,	PUNCT
fcis-30506	71	31	which	which	PRON
fcis-30506	71	32	led	lead	VERB
fcis-30506	71	33	to	to	ADP
fcis-30506	71	34	a	a	DET
fcis-30506	71	35	significant	significant	ADJ
fcis-30506	71	36	enhancement	enhancement	NOUN
fcis-30506	71	37	of	of	ADP
fcis-30506	71	38	spatial	spatial	ADJ
fcis-30506	71	39	heterogeneity	heterogeneity	NOUN
fcis-30506	71	40	in	in	ADP
fcis-30506	71	41	mortality	mortality	NOUN
fcis-30506	71	42	rates	rate	NOUN
fcis-30506	71	43	among	among	ADP
fcis-30506	71	44	provinces	province	NOUN
fcis-30506	71	45	.	.	PUNCT
fcis-30506	72	1	as	as	ADP
fcis-30506	72	2	a	a	DET
fcis-30506	72	3	result	result	NOUN
fcis-30506	72	4	,	,	PUNCT
fcis-30506	72	5	there	there	PRON
fcis-30506	72	6	was	be	VERB
fcis-30506	72	7	a	a	DET
fcis-30506	72	8	systematic	systematic	ADJ
fcis-30506	72	9	deviation	deviation	NOUN
fcis-30506	72	10	between	between	ADP
fcis-30506	72	11	the	the	DET
fcis-30506	72	12	unified	unified	ADJ
fcis-30506	72	13	premium	premium	NOUN
fcis-30506	72	14	rate	rate	NOUN
fcis-30506	72	15	system	system	NOUN
fcis-30506	72	16	and	and	CCONJ
fcis-30506	72	17	the	the	DET
fcis-30506	72	18	actual	actual	ADJ
fcis-30506	72	19	risk	risk	NOUN
fcis-30506	72	20	structure	structure	NOUN
fcis-30506	72	21	.	.	PUNCT
fcis-30506	73	1	when	when	SCONJ
fcis-30506	73	2	designing	design	VERB
fcis-30506	73	3	personal	personal	ADJ
fcis-30506	73	4	insurance	insurance	NOUN
fcis-30506	73	5	products	product	NOUN
fcis-30506	73	6	,	,	PUNCT
fcis-30506	73	7	insurance	insurance	NOUN
fcis-30506	73	8	companies	company	NOUN
fcis-30506	73	9	should	should	AUX
fcis-30506	73	10	not	not	PART
fcis-30506	73	11	only	only	ADV
fcis-30506	73	12	set	set	VERB
fcis-30506	73	13	premiums	premium	NOUN
fcis-30506	73	14	uniformly	uniformly	ADV
fcis-30506	73	15	but	but	CCONJ
fcis-30506	73	16	also	also	ADV
fcis-30506	73	17	take	take	VERB
fcis-30506	73	18	geographical	geographical	ADJ
fcis-30506	73	19	factors	factor	NOUN
fcis-30506	73	20	into	into	ADP
fcis-30506	73	21	account	account	NOUN
fcis-30506	73	22	and	and	CCONJ
fcis-30506	73	23	formulate	formulate	VERB
fcis-30506	73	24	premiums	premium	NOUN
fcis-30506	73	25	separately	separately	ADV
fcis-30506	73	26	for	for	ADP
fcis-30506	73	27	different	different	ADJ
fcis-30506	73	28	regions	region	NOUN
fcis-30506	73	29	.	.	PUNCT
fcis-30506	74	1	by	by	ADP
fcis-30506	74	2	fitting	fit	VERB
fcis-30506	74	3	the	the	DET
fcis-30506	74	4	population	population	NOUN
fcis-30506	74	5	mortality	mortality	NOUN
fcis-30506	74	6	rates	rate	NOUN
fcis-30506	74	7	of	of	ADP
fcis-30506	74	8	31	31	NUM
fcis-30506	74	9	provinces	province	NOUN
fcis-30506	74	10	in	in	ADP
fcis-30506	74	11	china	china	PROPN
fcis-30506	74	12	,	,	PUNCT
fcis-30506	74	13	it	it	PRON
fcis-30506	74	14	is	be	AUX
fcis-30506	74	15	found	find	VERB
fcis-30506	74	16	that	that	SCONJ
fcis-30506	74	17	the	the	DET
fcis-30506	74	18	trends	trend	NOUN
fcis-30506	74	19	of	of	ADP
fcis-30506	74	20	population	population	NOUN
fcis-30506	74	21	mortality	mortality	NOUN
fcis-30506	74	22	rates	rate	NOUN
fcis-30506	74	23	vary	vary	VERB
fcis-30506	74	24	among	among	ADP
fcis-30506	74	25	different	different	ADJ
fcis-30506	74	26	provinces	province	NOUN
fcis-30506	74	27	.	.	PUNCT
fcis-30506	75	1	however	however	ADV
fcis-30506	75	2	,	,	PUNCT
fcis-30506	75	3	they	they	PRON
fcis-30506	75	4	have	have	AUX
fcis-30506	75	5	all	all	PRON
fcis-30506	75	6	shown	show	VERB
fcis-30506	75	7	an	an	DET
fcis-30506	75	8	upward	upward	ADJ
fcis-30506	75	9	trend	trend	NOUN
fcis-30506	75	10	in	in	ADP
fcis-30506	75	11	recent	recent	ADJ
fcis-30506	75	12	years	year	NOUN
fcis-30506	75	13	.	.	PUNCT
fcis-30506	76	1	for	for	ADP
fcis-30506	76	2	each	each	DET
fcis-30506	76	3	province	province	NOUN
fcis-30506	76	4	,	,	PUNCT
fcis-30506	76	5	the	the	DET
fcis-30506	76	6	prediction	prediction	NOUN
fcis-30506	76	7	model	model	NOUN
fcis-30506	76	8	should	should	AUX
fcis-30506	76	9	be	be	AUX
fcis-30506	76	10	independent	independent	ADJ
fcis-30506	76	11	rather	rather	ADV
fcis-30506	76	12	than	than	ADP
fcis-30506	76	13	uniform	uniform	ADJ
fcis-30506	76	14	.	.	PUNCT
fcis-30506	77	1	but	but	CCONJ
fcis-30506	77	2	based	base	VERB
fcis-30506	77	3	solely	solely	ADV
fcis-30506	77	4	on	on	ADP
fcis-30506	77	5	the	the	DET
fcis-30506	77	6	historical	historical	ADJ
fcis-30506	77	7	mortality	mortality	NOUN
fcis-30506	77	8	rate	rate	NOUN
fcis-30506	77	9	data	datum	NOUN
fcis-30506	77	10	of	of	ADP
fcis-30506	77	11	each	each	DET
fcis-30506	77	12	province	province	NOUN
fcis-30506	77	13	over	over	ADP
fcis-30506	77	14	the	the	DET
fcis-30506	77	15	past	past	ADJ
fcis-30506	77	16	19	19	NUM
fcis-30506	77	17	years	year	NOUN
fcis-30506	77	18	,	,	PUNCT
fcis-30506	77	19	with	with	ADP
fcis-30506	77	20	insufficient	insufficient	ADJ
fcis-30506	77	21	data	datum	NOUN
fcis-30506	77	22	volume	volume	NOUN
fcis-30506	77	23	and	and	CCONJ
fcis-30506	77	24	insufficient	insufficient	ADJ
fcis-30506	77	25	training	training	NOUN
fcis-30506	77	26	for	for	ADP
fcis-30506	77	27	the	the	DET
fcis-30506	77	28	model	model	NOUN
fcis-30506	77	29	,	,	PUNCT
fcis-30506	77	30	it	it	PRON
fcis-30506	77	31	is	be	AUX
fcis-30506	77	32	impossible	impossible	ADJ
fcis-30506	77	33	to	to	PART
fcis-30506	77	34	accurately	accurately	ADV
fcis-30506	77	35	predict	predict	VERB
fcis-30506	77	36	future	future	ADJ
fcis-30506	77	37	results	result	NOUN
fcis-30506	77	38	.	.	PUNCT
fcis-30506	78	1	according	accord	VERB
fcis-30506	78	2	to	to	PART
fcis-30506	78	3	figure	figure	NOUN
fcis-30506	78	4	5	5	NUM
fcis-30506	78	5	,	,	PUNCT
fcis-30506	78	6	it	it	PRON
fcis-30506	78	7	can	can	AUX
fcis-30506	78	8	be	be	AUX
fcis-30506	78	9	observed	observe	VERB
fcis-30506	78	10	that	that	SCONJ
fcis-30506	78	11	the	the	DET
fcis-30506	78	12	curves	curve	NOUN
fcis-30506	78	13	of	of	ADP
fcis-30506	78	14	some	some	DET
fcis-30506	78	15	provinces	province	NOUN
fcis-30506	78	16	are	be	AUX
fcis-30506	78	17	very	very	ADV
fcis-30506	78	18	similar	similar	ADJ
fcis-30506	78	19	.	.	PUNCT
fcis-30506	79	1	therefore	therefore	ADV
fcis-30506	79	2	,	,	PUNCT
fcis-30506	79	3	we	we	PRON
fcis-30506	79	4	can	can	AUX
fcis-30506	79	5	draw	draw	VERB
fcis-30506	79	6	on	on	ADP
fcis-30506	79	7	their	their	PRON
fcis-30506	79	8	data	datum	NOUN
fcis-30506	79	9	to	to	PART
fcis-30506	79	10	expand	expand	VERB
fcis-30506	79	11	the	the	DET
fcis-30506	79	12	sample	sample	NOUN
fcis-30506	79	13	67	67	NUM
fcis-30506	79	14	size	size	NOUN
fcis-30506	79	15	and	and	CCONJ
fcis-30506	79	16	obtain	obtain	VERB
fcis-30506	79	17	more	more	ADV
fcis-30506	79	18	accurate	accurate	ADJ
fcis-30506	79	19	population	population	NOUN
fcis-30506	79	20	mortality	mortality	NOUN
fcis-30506	79	21	rate	rate	NOUN
fcis-30506	79	22	data	data	PROPN
fcis-30506	79	23	.	.	PUNCT
fcis-30506	80	1	table	table	NOUN
fcis-30506	80	2	1	1	NUM
fcis-30506	80	3	.	.	PUNCT
fcis-30506	81	1	performance	performance	NOUN
fcis-30506	81	2	comparison	comparison	NOUN
fcis-30506	81	3	between	between	ADP
fcis-30506	81	4	dgpr	dgpr	PROPN
fcis-30506	81	5	,	,	PUNCT
fcis-30506	81	6	gpr	gpr	PROPN
fcis-30506	81	7	,	,	PUNCT
fcis-30506	81	8	rf	rf	PROPN
fcis-30506	81	9	,	,	PUNCT
fcis-30506	81	10	xgboost	xgboost	ADV
fcis-30506	81	11	and	and	CCONJ
fcis-30506	81	12	gbdt	gbdt	VERB
fcis-30506	81	13	mse	mse	NOUN
fcis-30506	81	14	-	-	PUNCT
fcis-30506	81	15	mean	mean	NOUN
fcis-30506	81	16	mse	mse	NOUN
fcis-30506	81	17	-	-	PUNCT
fcis-30506	81	18	variance	variance	NOUN
fcis-30506	81	19	mae	mae	PROPN
fcis-30506	81	20	-	-	PUNCT
fcis-30506	81	21	mean	mean	PROPN
fcis-30506	81	22	mae	mae	PROPN
fcis-30506	81	23	-	-	PUNCT
fcis-30506	81	24	variance	variance	NOUN
fcis-30506	81	25	mre	mre	ADJ
fcis-30506	81	26	-	-	ADJ
fcis-30506	81	27	mean	mean	ADJ
fcis-30506	81	28	mre	mre	ADJ
fcis-30506	81	29	-	-	ADJ
fcis-30506	81	30	variance	variance	NOUN
fcis-30506	81	31	dgpr	dgpr	NOUN
fcis-30506	81	32	0.0131	0.0131	NUM
fcis-30506	81	33	0.0075	0.0075	NUM
fcis-30506	81	34	0.0860	0.0860	NUM
fcis-30506	82	1	0.0166	0.0166	NUM
fcis-30506	82	2	8.1144	8.1144	NUM
fcis-30506	82	3	7.3171	7.3171	NUM
fcis-30506	82	4	rf	rf	VERB
fcis-30506	82	5	0.0281	0.0281	NUM
fcis-30506	82	6	0.0198	0.0198	NUM
fcis-30506	82	7	0.1204	0.1204	NUM
fcis-30506	82	8	0.0193	0.0193	NUM
fcis-30506	82	9	8.1208	8.1208	NUM
fcis-30506	82	10	7.4164	7.4164	NUM
fcis-30506	82	11	gpr	gpr	NOUN
fcis-30506	82	12	0.4679	0.4679	NUM
fcis-30506	82	13	0.0578	0.0578	NUM
fcis-30506	82	14	0.6077	0.6077	NUM
fcis-30506	82	15	0.0527	0.0527	NUM
fcis-30506	82	16	1.0000	1.0000	NUM
fcis-30506	82	17	0.0000	0.0000	NUM
fcis-30506	82	18	xgboost	xgboost	ADP
fcis-30506	82	19	0.0572	0.0572	NUM
fcis-30506	82	20	0.0162	0.0162	NUM
fcis-30506	82	21	0.1882	0.1882	NUM
fcis-30506	82	22	0.0255	0.0255	NUM
fcis-30506	82	23	6.3024	6.3024	NUM
fcis-30506	82	24	5.6146	5.6146	NUM
fcis-30506	82	25	gbdt	gbdt	VERB
fcis-30506	82	26	0.1977	0.1977	NUM
fcis-30506	82	27	0.0250	0.0250	NUM
fcis-30506	82	28	0.3821	0.3821	NUM
fcis-30506	82	29	0.0335	0.0335	NUM
fcis-30506	82	30	3.7801	3.7801	NUM
fcis-30506	82	31	3.1188	3.1188	NUM
fcis-30506	82	32	figure	figure	NOUN
fcis-30506	82	33	3	3	NUM
fcis-30506	82	34	.	.	PUNCT
fcis-30506	82	35	box	box	NOUN
fcis-30506	82	36	plot	plot	NOUN
fcis-30506	82	37	for	for	ADP
fcis-30506	82	38	performance	performance	NOUN
fcis-30506	82	39	comparison	comparison	NOUN
fcis-30506	82	40	figure	figure	NOUN
fcis-30506	82	41	4	4	NUM
fcis-30506	82	42	.	.	PUNCT
fcis-30506	83	1	the	the	DET
fcis-30506	83	2	theil	theil	PROPN
fcis-30506	83	3	coefficient	coefficient	NOUN
fcis-30506	83	4	and	and	CCONJ
fcis-30506	83	5	coefficient	coefficient	NOUN
fcis-30506	83	6	of	of	ADP
fcis-30506	83	7	variation	variation	NOUN
fcis-30506	83	8	of	of	ADP
fcis-30506	83	9	mortality	mortality	NOUN
fcis-30506	83	10	rates	rate	NOUN
fcis-30506	83	11	in	in	ADP
fcis-30506	83	12	31	31	NUM
fcis-30506	83	13	provinces	province	NOUN
fcis-30506	83	14	of	of	ADP
fcis-30506	83	15	china	china	PROPN
fcis-30506	83	16	figure	figure	NOUN
fcis-30506	83	17	5	5	NUM
fcis-30506	83	18	.	.	PUNCT
fcis-30506	84	1	the	the	DET
fcis-30506	84	2	mortality	mortality	NOUN
fcis-30506	84	3	trend	trend	NOUN
fcis-30506	84	4	in	in	ADP
fcis-30506	84	5	31	31	NUM
fcis-30506	84	6	provinces	province	NOUN
fcis-30506	84	7	of	of	ADP
fcis-30506	84	8	china	china	PROPN
fcis-30506	84	9	68	68	NUM
fcis-30506	84	10	3.2.2	3.2.2	NUM
fcis-30506	84	11	.	.	PUNCT
fcis-30506	85	1	analysis	analysis	NOUN
fcis-30506	85	2	of	of	ADP
fcis-30506	85	3	experimental	experimental	ADJ
fcis-30506	85	4	results	result	NOUN
fcis-30506	85	5	the	the	DET
fcis-30506	85	6	real	real	ADJ
fcis-30506	85	7	data	datum	NOUN
fcis-30506	85	8	experiment	experiment	NOUN
fcis-30506	85	9	uses	use	VERB
fcis-30506	85	10	the	the	DET
fcis-30506	85	11	provincial	provincial	ADJ
fcis-30506	85	12	-	-	PUNCT
fcis-30506	85	13	level	level	NOUN
fcis-30506	85	14	population	population	NOUN
fcis-30506	85	15	mortality	mortality	NOUN
fcis-30506	85	16	rates	rate	NOUN
fcis-30506	85	17	from	from	ADP
fcis-30506	85	18	2005	2005	NUM
fcis-30506	85	19	to	to	ADP
fcis-30506	85	20	2002	2002	NUM
fcis-30506	85	21	as	as	ADP
fcis-30506	85	22	the	the	DET
fcis-30506	85	23	training	training	NOUN
fcis-30506	85	24	set	set	NOUN
fcis-30506	85	25	,	,	PUNCT
fcis-30506	85	26	and	and	CCONJ
fcis-30506	85	27	the	the	DET
fcis-30506	85	28	provincial	provincial	ADJ
fcis-30506	85	29	-	-	PUNCT
fcis-30506	85	30	level	level	NOUN
fcis-30506	85	31	population	population	NOUN
fcis-30506	85	32	mortality	mortality	NOUN
fcis-30506	85	33	rates	rate	NOUN
fcis-30506	85	34	in	in	ADP
fcis-30506	85	35	2023	2023	NUM
fcis-30506	85	36	as	as	ADP
fcis-30506	85	37	the	the	DET
fcis-30506	85	38	test	test	NOUN
fcis-30506	85	39	set	set	VERB
fcis-30506	85	40	to	to	PART
fcis-30506	85	41	predict	predict	VERB
fcis-30506	85	42	the	the	DET
fcis-30506	85	43	provincial	provincial	ADJ
fcis-30506	85	44	-	-	PUNCT
fcis-30506	85	45	level	level	NOUN
fcis-30506	85	46	population	population	NOUN
fcis-30506	85	47	mortality	mortality	NOUN
fcis-30506	85	48	rates	rate	NOUN
fcis-30506	85	49	in	in	ADP
fcis-30506	85	50	2023	2023	NUM
fcis-30506	85	51	:	:	PUNCT
fcis-30506	85	52	figure	figure	NOUN
fcis-30506	85	53	6	6	NUM
fcis-30506	85	54	.	.	PUNCT
fcis-30506	86	1	the	the	DET
fcis-30506	86	2	actual	actual	ADJ
fcis-30506	86	3	and	and	CCONJ
fcis-30506	86	4	predicted	predict	VERB
fcis-30506	86	5	values	value	NOUN
fcis-30506	86	6	of	of	ADP
fcis-30506	86	7	mortality	mortality	NOUN
fcis-30506	86	8	rate	rate	NOUN
fcis-30506	86	9	in	in	ADP
fcis-30506	86	10	2023	2023	NUM
fcis-30506	86	11	the	the	DET
fcis-30506	86	12	experimental	experimental	ADJ
fcis-30506	86	13	results	result	NOUN
fcis-30506	86	14	not	not	PART
fcis-30506	86	15	only	only	ADV
fcis-30506	86	16	yielded	yield	VERB
fcis-30506	86	17	the	the	DET
fcis-30506	86	18	predicted	predict	VERB
fcis-30506	86	19	values	value	NOUN
fcis-30506	86	20	for	for	ADP
fcis-30506	86	21	2023	2023	NUM
fcis-30506	86	22	,	,	PUNCT
fcis-30506	86	23	but	but	CCONJ
fcis-30506	86	24	also	also	ADV
fcis-30506	86	25	quantitatively	quantitatively	ADV
fcis-30506	86	26	estimated	estimate	VERB
fcis-30506	86	27	the	the	DET
fcis-30506	86	28	uncertainty	uncertainty	NOUN
fcis-30506	86	29	of	of	ADP
fcis-30506	86	30	the	the	DET
fcis-30506	86	31	mortality	mortality	NOUN
fcis-30506	86	32	rates	rate	NOUN
fcis-30506	86	33	in	in	ADP
fcis-30506	86	34	each	each	DET
fcis-30506	86	35	province	province	NOUN
fcis-30506	86	36	:	:	PUNCT
fcis-30506	86	37	figure	figure	VERB
fcis-30506	86	38	7	7	NUM
fcis-30506	86	39	.	.	PUNCT
fcis-30506	87	1	the	the	DET
fcis-30506	87	2	predicted	predict	VERB
fcis-30506	87	3	range	range	NOUN
fcis-30506	87	4	of	of	ADP
fcis-30506	87	5	mortality	mortality	NOUN
fcis-30506	87	6	rate	rate	NOUN
fcis-30506	87	7	for	for	ADP
fcis-30506	87	8	2023	2023	NUM
fcis-30506	87	9	as	as	SCONJ
fcis-30506	87	10	shown	show	VERB
fcis-30506	87	11	in	in	ADP
fcis-30506	87	12	figure	figure	NOUN
fcis-30506	87	13	7	7	NUM
fcis-30506	87	14	,	,	PUNCT
fcis-30506	87	15	it	it	PRON
fcis-30506	87	16	can	can	AUX
fcis-30506	87	17	be	be	AUX
fcis-30506	87	18	observed	observe	VERB
fcis-30506	87	19	that	that	SCONJ
fcis-30506	87	20	the	the	DET
fcis-30506	87	21	actual	actual	ADJ
fcis-30506	87	22	mortality	mortality	NOUN
fcis-30506	87	23	rates	rate	NOUN
fcis-30506	87	24	of	of	ADP
fcis-30506	87	25	most	most	ADJ
fcis-30506	87	26	provinces	province	NOUN
fcis-30506	87	27	fall	fall	VERB
fcis-30506	87	28	within	within	ADP
fcis-30506	87	29	the	the	DET
fcis-30506	87	30	confidence	confidence	NOUN
fcis-30506	87	31	intervals	interval	NOUN
fcis-30506	87	32	,	,	PUNCT
fcis-30506	87	33	indicating	indicate	VERB
fcis-30506	87	34	that	that	SCONJ
fcis-30506	87	35	the	the	DET
fcis-30506	87	36	predicted	predict	VERB
fcis-30506	87	37	confidence	confidence	NOUN
fcis-30506	87	38	intervals	interval	NOUN
fcis-30506	87	39	are	be	AUX
fcis-30506	87	40	reasonable	reasonable	ADJ
fcis-30506	87	41	and	and	CCONJ
fcis-30506	87	42	can	can	AUX
fcis-30506	87	43	encompass	encompass	VERB
fcis-30506	87	44	the	the	DET
fcis-30506	87	45	actual	actual	ADJ
fcis-30506	87	46	data	datum	NOUN
fcis-30506	87	47	.	.	PUNCT
fcis-30506	88	1	insurance	insurance	NOUN
fcis-30506	88	2	companies	company	NOUN
fcis-30506	88	3	set	set	VERB
fcis-30506	88	4	life	life	NOUN
fcis-30506	88	5	insurance	insurance	NOUN
fcis-30506	88	6	premiums	premium	NOUN
fcis-30506	88	7	based	base	VERB
fcis-30506	88	8	on	on	ADP
fcis-30506	88	9	mortality	mortality	NOUN
fcis-30506	88	10	rates	rate	NOUN
fcis-30506	88	11	.	.	PUNCT
fcis-30506	89	1	if	if	SCONJ
fcis-30506	89	2	they	they	PRON
fcis-30506	89	3	can	can	AUX
fcis-30506	89	4	predict	predict	VERB
fcis-30506	89	5	the	the	DET
fcis-30506	89	6	possible	possible	ADJ
fcis-30506	89	7	range	range	NOUN
fcis-30506	89	8	of	of	ADP
fcis-30506	89	9	future	future	ADJ
fcis-30506	89	10	mortality	mortality	NOUN
fcis-30506	89	11	rates	rate	NOUN
fcis-30506	89	12	rather	rather	ADV
fcis-30506	89	13	than	than	ADP
fcis-30506	89	14	a	a	DET
fcis-30506	89	15	single	single	ADJ
fcis-30506	89	16	value	value	NOUN
fcis-30506	89	17	,	,	PUNCT
fcis-30506	89	18	they	they	PRON
fcis-30506	89	19	can	can	AUX
fcis-30506	89	20	better	well	ADV
fcis-30506	89	21	assess	assess	VERB
fcis-30506	89	22	risks	risk	NOUN
fcis-30506	89	23	.	.	PUNCT
fcis-30506	90	1	if	if	SCONJ
fcis-30506	90	2	the	the	DET
fcis-30506	90	3	predicted	predict	VERB
fcis-30506	90	4	mortality	mortality	NOUN
fcis-30506	90	5	rate	rate	NOUN
fcis-30506	90	6	range	range	NOUN
fcis-30506	90	7	for	for	ADP
fcis-30506	90	8	a	a	DET
fcis-30506	90	9	province	province	NOUN
fcis-30506	90	10	is	be	AUX
fcis-30506	90	11	wide	wide	ADJ
fcis-30506	90	12	,	,	PUNCT
fcis-30506	90	13	it	it	PRON
fcis-30506	90	14	indicates	indicate	VERB
fcis-30506	90	15	higher	high	ADJ
fcis-30506	90	16	uncertainty	uncertainty	NOUN
fcis-30506	90	17	,	,	PUNCT
fcis-30506	90	18	and	and	CCONJ
fcis-30506	90	19	the	the	DET
fcis-30506	90	20	insurance	insurance	NOUN
fcis-30506	90	21	company	company	NOUN
fcis-30506	90	22	may	may	AUX
fcis-30506	90	23	need	need	VERB
fcis-30506	90	24	to	to	PART
fcis-30506	90	25	set	set	VERB
fcis-30506	90	26	higher	high	ADJ
fcis-30506	90	27	premiums	premium	NOUN
fcis-30506	90	28	to	to	PART
fcis-30506	90	29	cover	cover	VERB
fcis-30506	90	30	potential	potential	ADJ
fcis-30506	90	31	risks	risk	NOUN
fcis-30506	90	32	.	.	PUNCT
fcis-30506	91	1	conversely	conversely	ADV
fcis-30506	91	2	,	,	PUNCT
fcis-30506	91	3	if	if	SCONJ
fcis-30506	91	4	the	the	DET
fcis-30506	91	5	range	range	NOUN
fcis-30506	91	6	is	be	AUX
fcis-30506	91	7	narrow	narrow	ADJ
fcis-30506	91	8	,	,	PUNCT
fcis-30506	91	9	it	it	PRON
fcis-30506	91	10	indicates	indicate	VERB
fcis-30506	91	11	more	more	ADV
fcis-30506	91	12	reliable	reliable	ADJ
fcis-30506	91	13	prediction	prediction	NOUN
fcis-30506	91	14	and	and	CCONJ
fcis-30506	91	15	can	can	AUX
fcis-30506	91	16	set	set	VERB
fcis-30506	91	17	premiums	premium	NOUN
fcis-30506	91	18	more	more	ADV
fcis-30506	91	19	precisely	precisely	ADV
fcis-30506	91	20	.	.	PUNCT
fcis-30506	92	1	moreover	moreover	ADV
fcis-30506	92	2	,	,	PUNCT
fcis-30506	92	3	confidence	confidence	NOUN
fcis-30506	92	4	intervals	interval	NOUN
fcis-30506	92	5	can	can	AUX
fcis-30506	92	6	help	help	VERB
fcis-30506	92	7	insurance	insurance	NOUN
fcis-30506	92	8	companies	company	NOUN
fcis-30506	92	9	buffer	buffer	VERB
fcis-30506	92	10	risks	risk	NOUN
fcis-30506	92	11	.	.	PUNCT
fcis-30506	93	1	if	if	SCONJ
fcis-30506	93	2	the	the	DET
fcis-30506	93	3	actual	actual	ADJ
fcis-30506	93	4	mortality	mortality	NOUN
fcis-30506	93	5	rate	rate	NOUN
fcis-30506	93	6	exceeds	exceed	VERB
fcis-30506	93	7	the	the	DET
fcis-30506	93	8	predicted	predict	VERB
fcis-30506	93	9	interval	interval	NOUN
fcis-30506	93	10	,	,	PUNCT
fcis-30506	93	11	the	the	DET
fcis-30506	93	12	insurance	insurance	NOUN
fcis-30506	93	13	company	company	NOUN
fcis-30506	93	14	may	may	AUX
fcis-30506	93	15	need	need	VERB
fcis-30506	93	16	to	to	PART
fcis-30506	93	17	adjust	adjust	VERB
fcis-30506	93	18	capital	capital	NOUN
fcis-30506	93	19	reserves	reserve	NOUN
fcis-30506	93	20	or	or	CCONJ
fcis-30506	93	21	reinsurance	reinsurance	NOUN
fcis-30506	93	22	strategies	strategy	NOUN
fcis-30506	93	23	.	.	PUNCT
fcis-30506	94	1	for	for	ADP
fcis-30506	94	2	provinces	province	NOUN
fcis-30506	94	3	like	like	ADP
fcis-30506	94	4	liaoning	liaoning	PROPN
fcis-30506	94	5	,	,	PUNCT
fcis-30506	94	6	anhui	anhui	PROPN
fcis-30506	94	7	,	,	PUNCT
fcis-30506	94	8	and	and	CCONJ
fcis-30506	94	9	henan	henan	PROPN
fcis-30506	94	10	where	where	SCONJ
fcis-30506	94	11	the	the	DET
fcis-30506	94	12	actual	actual	ADJ
fcis-30506	94	13	data	datum	NOUN
fcis-30506	94	14	do	do	AUX
fcis-30506	94	15	not	not	PART
fcis-30506	94	16	fall	fall	VERB
fcis-30506	94	17	within	within	ADP
fcis-30506	94	18	the	the	DET
fcis-30506	94	19	confidence	confidence	NOUN
fcis-30506	94	20	intervals	interval	NOUN
fcis-30506	94	21	significantly	significantly	ADV
fcis-30506	94	22	,	,	PUNCT
fcis-30506	94	23	further	further	ADJ
fcis-30506	94	24	analysis	analysis	NOUN
fcis-30506	94	25	may	may	AUX
fcis-30506	94	26	be	be	AUX
fcis-30506	94	27	needed	need	VERB
fcis-30506	94	28	to	to	PART
fcis-30506	94	29	identify	identify	VERB
fcis-30506	94	30	reasons	reason	NOUN
fcis-30506	94	31	,	,	PUNCT
fcis-30506	94	32	such	such	ADJ
fcis-30506	94	33	as	as	ADP
fcis-30506	94	34	whether	whether	SCONJ
fcis-30506	94	35	there	there	PRON
fcis-30506	94	36	are	be	VERB
fcis-30506	94	37	special	special	ADJ
fcis-30506	94	38	population	population	NOUN
fcis-30506	94	39	structures	structure	NOUN
fcis-30506	94	40	,	,	PUNCT
fcis-30506	94	41	distribution	distribution	NOUN
fcis-30506	94	42	of	of	ADP
fcis-30506	94	43	medical	medical	ADJ
fcis-30506	94	44	resources	resource	NOUN
fcis-30506	94	45	,	,	PUNCT
fcis-30506	94	46	or	or	CCONJ
fcis-30506	94	47	other	other	ADJ
fcis-30506	94	48	external	external	ADJ
fcis-30506	94	49	factors	factor	NOUN
fcis-30506	94	50	,	,	PUNCT
fcis-30506	94	51	so	so	SCONJ
fcis-30506	94	52	as	as	SCONJ
fcis-30506	94	53	to	to	PART
fcis-30506	94	54	adjust	adjust	VERB
fcis-30506	94	55	the	the	DET
fcis-30506	94	56	model	model	NOUN
fcis-30506	94	57	or	or	CCONJ
fcis-30506	94	58	formulate	formulate	VERB
fcis-30506	94	59	targeted	target	VERB
fcis-30506	94	60	risk	risk	NOUN
fcis-30506	94	61	management	management	NOUN
fcis-30506	94	62	measures	measure	NOUN
fcis-30506	94	63	when	when	SCONJ
fcis-30506	94	64	calculating	calculate	VERB
fcis-30506	94	65	premiums	premium	NOUN
fcis-30506	94	66	.	.	PUNCT
fcis-30506	95	1	in	in	ADP
fcis-30506	95	2	order	order	NOUN
fcis-30506	95	3	to	to	PART
fcis-30506	95	4	evaluate	evaluate	VERB
fcis-30506	95	5	the	the	DET
fcis-30506	95	6	predictive	predictive	ADJ
fcis-30506	95	7	performance	performance	NOUN
fcis-30506	95	8	of	of	ADP
fcis-30506	95	9	the	the	DET
fcis-30506	95	10	model	model	NOUN
fcis-30506	95	11	under	under	ADP
fcis-30506	95	12	real	real	ADJ
fcis-30506	95	13	data	datum	NOUN
fcis-30506	95	14	,	,	PUNCT
fcis-30506	95	15	three	three	NUM
fcis-30506	95	16	commonly	commonly	ADV
fcis-30506	95	17	used	use	VERB
fcis-30506	95	18	evaluation	evaluation	NOUN
fcis-30506	95	19	metrics	metric	NOUN
fcis-30506	95	20	were	be	AUX
fcis-30506	95	21	also	also	ADV
fcis-30506	95	22	adopted	adopt	VERB
fcis-30506	95	23	:	:	PUNCT
fcis-30506	95	24	mean	mean	ADJ
fcis-30506	95	25	squared	square	VERB
fcis-30506	95	26	error	error	NOUN
fcis-30506	95	27	(	(	PUNCT
fcis-30506	95	28	mse	mse	NOUN
fcis-30506	95	29	)	)	PUNCT
fcis-30506	95	30	,	,	PUNCT
fcis-30506	95	31	mean	mean	VERB
fcis-30506	95	32	absolute	absolute	ADJ
fcis-30506	95	33	error	error	NOUN
fcis-30506	95	34	(	(	PUNCT
fcis-30506	95	35	mae	mae	PROPN
fcis-30506	95	36	)	)	PUNCT
fcis-30506	95	37	,	,	PUNCT
fcis-30506	95	38	mean	mean	VERB
fcis-30506	95	39	relative	relative	ADJ
fcis-30506	95	40	error	error	NOUN
fcis-30506	95	41	(	(	PUNCT
fcis-30506	95	42	mre	mre	NOUN
fcis-30506	95	43	):	):	PUNCT
fcis-30506	95	44	69	69	NUM
fcis-30506	95	45	table	table	NOUN
fcis-30506	95	46	2	2	NUM
fcis-30506	95	47	.	.	PUNCT
fcis-30506	95	48	performance	performance	NOUN
fcis-30506	95	49	comparison	comparison	NOUN
fcis-30506	95	50	between	between	ADP
fcis-30506	95	51	dgpr	dgpr	NOUN
fcis-30506	95	52	,	,	PUNCT
fcis-30506	95	53	rf	rf	NOUN
fcis-30506	95	54	,	,	PUNCT
fcis-30506	95	55	arima	arima	NOUN
fcis-30506	95	56	,	,	PUNCT
fcis-30506	95	57	xgboost	xgboost	NOUN
fcis-30506	95	58	and	and	CCONJ
fcis-30506	95	59	gbdt	gbdt	PROPN
fcis-30506	95	60	mse	mse	PROPN
fcis-30506	95	61	mae	mae	PROPN
fcis-30506	95	62	mre	mre	PROPN
fcis-30506	95	63	dgpr	dgpr	PROPN
fcis-30506	95	64	0.5676	0.5676	NUM
fcis-30506	95	65	0.6285	0.6285	NUM
fcis-30506	95	66	0.1593	0.1593	NUM
fcis-30506	95	67	rf	rf	NUM
fcis-30506	95	68	0.9902	0.9902	NUM
fcis-30506	95	69	0.8172	0.8172	NUM
fcis-30506	95	70	0.1674	0.1674	NUM
fcis-30506	95	71	arima	arima	PROPN
fcis-30506	95	72	4.3426	4.3426	NUM
fcis-30506	95	73	1.8061	1.8061	NUM
fcis-30506	95	74	0.2197	0.2197	NUM
fcis-30506	95	75	xgboost	xgboost	NOUN
fcis-30506	96	1	4.1543	4.1543	NUM
fcis-30506	96	2	1.8859	1.8859	NUM
fcis-30506	96	3	0.2365	0.2365	NUM
fcis-30506	96	4	gbdt	gbdt	VERB
fcis-30506	96	5	1.1849	1.1849	NUM
fcis-30506	96	6	0.9097	0.9097	NUM
fcis-30506	96	7	0.1614	0.1614	NUM
fcis-30506	96	8	by	by	ADP
fcis-30506	96	9	comparing	compare	VERB
fcis-30506	96	10	the	the	DET
fcis-30506	96	11	mean	mean	ADJ
fcis-30506	96	12	square	square	NOUN
fcis-30506	96	13	error	error	NOUN
fcis-30506	96	14	,	,	PUNCT
fcis-30506	96	15	absolute	absolute	ADJ
fcis-30506	96	16	error	error	NOUN
fcis-30506	96	17	and	and	CCONJ
fcis-30506	96	18	relative	relative	ADJ
fcis-30506	96	19	error	error	NOUN
fcis-30506	96	20	of	of	ADP
fcis-30506	96	21	each	each	DET
fcis-30506	96	22	model	model	NOUN
fcis-30506	96	23	,	,	PUNCT
fcis-30506	96	24	it	it	PRON
fcis-30506	96	25	is	be	AUX
fcis-30506	96	26	found	find	VERB
fcis-30506	96	27	that	that	SCONJ
fcis-30506	96	28	the	the	DET
fcis-30506	96	29	dgpr	dgpr	NOUN
fcis-30506	96	30	model	model	NOUN
fcis-30506	96	31	has	have	VERB
fcis-30506	96	32	smaller	small	ADJ
fcis-30506	96	33	errors	error	NOUN
fcis-30506	96	34	than	than	ADP
fcis-30506	96	35	those	those	PRON
fcis-30506	96	36	of	of	ADP
fcis-30506	96	37	random	random	ADJ
fcis-30506	96	38	forest	forest	NOUN
fcis-30506	96	39	,	,	PUNCT
fcis-30506	96	40	arima	arima	PROPN
fcis-30506	96	41	,	,	PUNCT
fcis-30506	96	42	xgboost	xgboost	NOUN
fcis-30506	96	43	and	and	CCONJ
fcis-30506	96	44	gbdt	gbdt	NOUN
fcis-30506	96	45	,	,	PUNCT
fcis-30506	96	46	and	and	CCONJ
fcis-30506	96	47	the	the	DET
fcis-30506	96	48	prediction	prediction	NOUN
fcis-30506	96	49	results	result	NOUN
fcis-30506	96	50	are	be	AUX
fcis-30506	96	51	more	more	ADV
fcis-30506	96	52	accurate	accurate	ADJ
fcis-30506	96	53	.	.	PUNCT
fcis-30506	97	1	4	4	X
fcis-30506	97	2	.	.	X
fcis-30506	97	3	conclusion	conclusion	NOUN
fcis-30506	97	4	and	and	CCONJ
fcis-30506	97	5	outlook	outlook	VERB
fcis-30506	97	6	4.1	4.1	NUM
fcis-30506	97	7	.	.	PUNCT
fcis-30506	98	1	conclusion	conclusion	NOUN
fcis-30506	98	2	and	and	CCONJ
fcis-30506	98	3	recommendations	recommendation	NOUN
fcis-30506	98	4	due	due	ADP
fcis-30506	98	5	to	to	ADP
fcis-30506	98	6	the	the	DET
fcis-30506	98	7	significant	significant	ADJ
fcis-30506	98	8	spatial	spatial	ADJ
fcis-30506	98	9	-	-	PUNCT
fcis-30506	98	10	temporal	temporal	ADJ
fcis-30506	98	11	heterogeneity	heterogeneity	NOUN
fcis-30506	98	12	of	of	ADP
fcis-30506	98	13	mortality	mortality	NOUN
fcis-30506	98	14	rates	rate	NOUN
fcis-30506	98	15	in	in	ADP
fcis-30506	98	16	31	31	NUM
fcis-30506	98	17	provinces	province	NOUN
fcis-30506	98	18	of	of	ADP
fcis-30506	98	19	china	china	PROPN
fcis-30506	98	20	caused	cause	VERB
fcis-30506	98	21	by	by	ADP
fcis-30506	98	22	reasons	reason	NOUN
fcis-30506	98	23	such	such	ADJ
fcis-30506	98	24	as	as	ADP
fcis-30506	98	25	population	population	NOUN
fcis-30506	98	26	mobility	mobility	NOUN
fcis-30506	98	27	,	,	PUNCT
fcis-30506	98	28	uneven	uneven	ADJ
fcis-30506	98	29	distribution	distribution	NOUN
fcis-30506	98	30	of	of	ADP
fcis-30506	98	31	medical	medical	ADJ
fcis-30506	98	32	resources	resource	NOUN
fcis-30506	98	33	and	and	CCONJ
fcis-30506	98	34	environmental	environmental	ADJ
fcis-30506	98	35	differences	difference	NOUN
fcis-30506	98	36	,	,	PUNCT
fcis-30506	98	37	the	the	DET
fcis-30506	98	38	traditional	traditional	ADJ
fcis-30506	98	39	unified	unified	ADJ
fcis-30506	98	40	rate	rate	NOUN
fcis-30506	98	41	model	model	NOUN
fcis-30506	98	42	is	be	AUX
fcis-30506	98	43	difficult	difficult	ADJ
fcis-30506	98	44	to	to	PART
fcis-30506	98	45	match	match	VERB
fcis-30506	98	46	the	the	DET
fcis-30506	98	47	actual	actual	ADJ
fcis-30506	98	48	risk	risk	NOUN
fcis-30506	98	49	distribution	distribution	NOUN
fcis-30506	98	50	.	.	PUNCT
fcis-30506	99	1	in	in	ADP
fcis-30506	99	2	this	this	DET
fcis-30506	99	3	paper	paper	NOUN
fcis-30506	99	4	,	,	PUNCT
fcis-30506	99	5	a	a	DET
fcis-30506	99	6	two	two	NUM
fcis-30506	99	7	-	-	PUNCT
fcis-30506	99	8	layer	layer	NOUN
fcis-30506	99	9	gaussian	gaussian	ADJ
fcis-30506	99	10	process	process	NOUN
fcis-30506	99	11	ensemble	ensemble	ADJ
fcis-30506	99	12	regression	regression	NOUN
fcis-30506	99	13	model	model	NOUN
fcis-30506	99	14	(	(	PUNCT
fcis-30506	99	15	dgpr	dgpr	PROPN
fcis-30506	99	16	)	)	PUNCT
fcis-30506	99	17	is	be	AUX
fcis-30506	99	18	proposed	propose	VERB
fcis-30506	99	19	,	,	PUNCT
fcis-30506	99	20	which	which	PRON
fcis-30506	99	21	takes	take	VERB
fcis-30506	99	22	geographical	geographical	ADJ
fcis-30506	99	23	factors	factor	NOUN
fcis-30506	99	24	into	into	ADP
fcis-30506	99	25	account	account	NOUN
fcis-30506	99	26	and	and	CCONJ
fcis-30506	99	27	accurately	accurately	ADV
fcis-30506	99	28	predicts	predict	VERB
fcis-30506	99	29	the	the	DET
fcis-30506	99	30	mortality	mortality	NOUN
fcis-30506	99	31	rates	rate	NOUN
fcis-30506	99	32	of	of	ADP
fcis-30506	99	33	31	31	NUM
fcis-30506	99	34	provinces	province	NOUN
fcis-30506	99	35	.	.	PUNCT
fcis-30506	100	1	insurance	insurance	NOUN
fcis-30506	100	2	companies	company	NOUN
fcis-30506	100	3	can	can	AUX
fcis-30506	100	4	more	more	ADV
fcis-30506	100	5	accurately	accurately	ADV
fcis-30506	100	6	estimate	estimate	VERB
fcis-30506	100	7	future	future	ADJ
fcis-30506	100	8	liability	liability	NOUN
fcis-30506	100	9	for	for	ADP
fcis-30506	100	10	compensation	compensation	NOUN
fcis-30506	100	11	and	and	CCONJ
fcis-30506	100	12	avoid	avoid	VERB
fcis-30506	100	13	redundant	redundant	ADJ
fcis-30506	100	14	or	or	CCONJ
fcis-30506	100	15	insufficient	insufficient	ADJ
fcis-30506	100	16	reserves	reserve	NOUN
fcis-30506	100	17	.	.	PUNCT
fcis-30506	101	1	moreover	moreover	ADV
fcis-30506	101	2	,	,	PUNCT
fcis-30506	101	3	by	by	ADP
fcis-30506	101	4	leveraging	leverage	VERB
fcis-30506	101	5	the	the	DET
fcis-30506	101	6	bayesian	bayesian	NOUN
fcis-30506	101	7	characteristics	characteristic	NOUN
fcis-30506	101	8	of	of	ADP
fcis-30506	101	9	the	the	DET
fcis-30506	101	10	gaussian	gaussian	ADJ
fcis-30506	101	11	process	process	NOUN
fcis-30506	101	12	regression	regression	NOUN
fcis-30506	101	13	model	model	NOUN
fcis-30506	101	14	(	(	PUNCT
fcis-30506	101	15	gpr	gpr	PROPN
fcis-30506	101	16	)	)	PUNCT
fcis-30506	101	17	,	,	PUNCT
fcis-30506	101	18	the	the	DET
fcis-30506	101	19	confidence	confidence	NOUN
fcis-30506	101	20	intervals	interval	NOUN
fcis-30506	101	21	of	of	ADP
fcis-30506	101	22	population	population	NOUN
fcis-30506	101	23	mortality	mortality	NOUN
fcis-30506	101	24	rates	rate	NOUN
fcis-30506	101	25	in	in	ADP
fcis-30506	101	26	each	each	DET
fcis-30506	101	27	province	province	NOUN
fcis-30506	101	28	are	be	AUX
fcis-30506	101	29	output	output	NOUN
fcis-30506	101	30	to	to	PART
fcis-30506	101	31	quantify	quantify	VERB
fcis-30506	101	32	uncertainty	uncertainty	NOUN
fcis-30506	101	33	and	and	CCONJ
fcis-30506	101	34	provide	provide	VERB
fcis-30506	101	35	probabilistic	probabilistic	ADJ
fcis-30506	101	36	support	support	NOUN
fcis-30506	101	37	for	for	ADP
fcis-30506	101	38	risk	risk	NOUN
fcis-30506	101	39	management	management	NOUN
fcis-30506	101	40	.	.	PUNCT
fcis-30506	102	1	for	for	ADP
fcis-30506	102	2	insurance	insurance	NOUN
fcis-30506	102	3	companies	company	NOUN
fcis-30506	102	4	,	,	PUNCT
fcis-30506	102	5	the	the	DET
fcis-30506	102	6	regional	regional	ADJ
fcis-30506	102	7	differences	difference	NOUN
fcis-30506	102	8	in	in	ADP
fcis-30506	102	9	mortality	mortality	NOUN
fcis-30506	102	10	rates	rate	NOUN
fcis-30506	102	11	will	will	AUX
fcis-30506	102	12	directly	directly	ADV
fcis-30506	102	13	affect	affect	VERB
fcis-30506	102	14	the	the	DET
fcis-30506	102	15	pricing	pricing	NOUN
fcis-30506	102	16	of	of	ADP
fcis-30506	102	17	products	product	NOUN
fcis-30506	102	18	and	and	CCONJ
fcis-30506	102	19	the	the	DET
fcis-30506	102	20	assessment	assessment	NOUN
fcis-30506	102	21	of	of	ADP
fcis-30506	102	22	reserves	reserve	NOUN
fcis-30506	102	23	of	of	ADP
fcis-30506	102	24	their	their	PRON
fcis-30506	102	25	branches	branch	NOUN
fcis-30506	102	26	,	,	PUNCT
fcis-30506	102	27	thereby	thereby	ADV
fcis-30506	102	28	influencing	influence	VERB
fcis-30506	102	29	the	the	DET
fcis-30506	102	30	company	company	NOUN
fcis-30506	102	31	's	's	PART
fcis-30506	102	32	solvency	solvency	NOUN
fcis-30506	102	33	.	.	PUNCT
fcis-30506	103	1	therefore	therefore	ADV
fcis-30506	103	2	,	,	PUNCT
fcis-30506	103	3	when	when	SCONJ
fcis-30506	103	4	setting	set	VERB
fcis-30506	103	5	the	the	DET
fcis-30506	103	6	rates	rate	NOUN
fcis-30506	103	7	,	,	PUNCT
fcis-30506	103	8	the	the	DET
fcis-30506	103	9	mortality	mortality	NOUN
fcis-30506	103	10	rate	rate	NOUN
fcis-30506	103	11	differences	difference	NOUN
fcis-30506	103	12	among	among	ADP
fcis-30506	103	13	different	different	ADJ
fcis-30506	103	14	provinces	province	NOUN
fcis-30506	103	15	should	should	AUX
fcis-30506	103	16	be	be	AUX
fcis-30506	103	17	taken	take	VERB
fcis-30506	103	18	into	into	ADP
fcis-30506	103	19	account	account	NOUN
fcis-30506	103	20	,	,	PUNCT
fcis-30506	103	21	and	and	CCONJ
fcis-30506	103	22	different	different	ADJ
fcis-30506	103	23	prices	price	NOUN
fcis-30506	103	24	should	should	AUX
fcis-30506	103	25	be	be	AUX
fcis-30506	103	26	implemented	implement	VERB
fcis-30506	103	27	for	for	ADP
fcis-30506	103	28	different	different	ADJ
fcis-30506	103	29	provinces	province	NOUN
fcis-30506	103	30	.	.	PUNCT
fcis-30506	104	1	based	base	VERB
fcis-30506	104	2	on	on	ADP
fcis-30506	104	3	the	the	DET
fcis-30506	104	4	current	current	ADJ
fcis-30506	104	5	trend	trend	NOUN
fcis-30506	104	6	of	of	ADP
fcis-30506	104	7	population	population	NOUN
fcis-30506	104	8	mortality	mortality	NOUN
fcis-30506	104	9	rates	rate	NOUN
fcis-30506	104	10	,	,	PUNCT
fcis-30506	104	11	insurance	insurance	NOUN
fcis-30506	104	12	companies	company	NOUN
fcis-30506	104	13	should	should	AUX
fcis-30506	104	14	consider	consider	VERB
fcis-30506	104	15	the	the	DET
fcis-30506	104	16	overall	overall	ADJ
fcis-30506	104	17	increase	increase	NOUN
fcis-30506	104	18	in	in	ADP
fcis-30506	104	19	mortality	mortality	NOUN
fcis-30506	104	20	rates	rate	NOUN
fcis-30506	104	21	and	and	CCONJ
fcis-30506	104	22	fully	fully	ADV
fcis-30506	104	23	grasp	grasp	VERB
fcis-30506	104	24	the	the	DET
fcis-30506	104	25	population	population	NOUN
fcis-30506	104	26	mortality	mortality	NOUN
fcis-30506	104	27	patterns	pattern	NOUN
fcis-30506	104	28	in	in	ADP
fcis-30506	104	29	each	each	DET
fcis-30506	104	30	province	province	NOUN
fcis-30506	104	31	of	of	ADP
fcis-30506	104	32	china	china	PROPN
fcis-30506	104	33	.	.	PUNCT
fcis-30506	105	1	by	by	ADP
fcis-30506	105	2	using	use	VERB
fcis-30506	105	3	the	the	DET
fcis-30506	105	4	confidence	confidence	NOUN
fcis-30506	105	5	intervals	interval	NOUN
fcis-30506	105	6	of	of	ADP
fcis-30506	105	7	mortality	mortality	NOUN
fcis-30506	105	8	rates	rate	NOUN
fcis-30506	105	9	in	in	ADP
fcis-30506	105	10	different	different	ADJ
fcis-30506	105	11	provinces	province	NOUN
fcis-30506	105	12	to	to	PART
fcis-30506	105	13	quantify	quantify	VERB
fcis-30506	105	14	uncertainty	uncertainty	NOUN
fcis-30506	105	15	,	,	PUNCT
fcis-30506	105	16	precise	precise	ADJ
fcis-30506	105	17	pricing	pricing	NOUN
fcis-30506	105	18	and	and	CCONJ
fcis-30506	105	19	differentiated	differentiated	ADJ
fcis-30506	105	20	rates	rate	NOUN
fcis-30506	105	21	should	should	AUX
fcis-30506	105	22	be	be	AUX
fcis-30506	105	23	implemented	implement	VERB
fcis-30506	105	24	.	.	PUNCT
fcis-30506	106	1	identify	identify	VERB
fcis-30506	106	2	provinces	province	NOUN
fcis-30506	106	3	with	with	ADP
fcis-30506	106	4	high	high	ADJ
fcis-30506	106	5	uncertainty	uncertainty	NOUN
fcis-30506	106	6	and	and	CCONJ
fcis-30506	106	7	set	set	VERB
fcis-30506	106	8	higher	high	ADJ
fcis-30506	106	9	premiums	premium	NOUN
fcis-30506	106	10	to	to	PART
fcis-30506	106	11	cover	cover	VERB
fcis-30506	106	12	potential	potential	ADJ
fcis-30506	106	13	risks	risk	NOUN
fcis-30506	106	14	,	,	PUNCT
fcis-30506	106	15	while	while	SCONJ
fcis-30506	106	16	appropriately	appropriately	ADV
fcis-30506	106	17	reducing	reduce	VERB
fcis-30506	106	18	premiums	premium	NOUN
fcis-30506	106	19	in	in	ADP
fcis-30506	106	20	regions	region	NOUN
fcis-30506	106	21	with	with	ADP
fcis-30506	106	22	low	low	ADJ
fcis-30506	106	23	uncertainty	uncertainty	NOUN
fcis-30506	106	24	and	and	CCONJ
fcis-30506	106	25	low	low	ADJ
fcis-30506	106	26	mortality	mortality	NOUN
fcis-30506	106	27	rates	rate	NOUN
fcis-30506	106	28	to	to	PART
fcis-30506	106	29	enhance	enhance	VERB
fcis-30506	106	30	market	market	NOUN
fcis-30506	106	31	competitiveness	competitiveness	NOUN
fcis-30506	106	32	.	.	PUNCT
fcis-30506	107	1	conduct	conduct	VERB
fcis-30506	107	2	capital	capital	NOUN
fcis-30506	107	3	reserves	reserve	NOUN
fcis-30506	107	4	and	and	CCONJ
fcis-30506	107	5	solvency	solvency	NOUN
fcis-30506	107	6	management	management	NOUN
fcis-30506	107	7	,	,	PUNCT
fcis-30506	107	8	reserve	reserve	VERB
fcis-30506	107	9	more	more	ADJ
fcis-30506	107	10	reserves	reserve	NOUN
fcis-30506	107	11	for	for	ADP
fcis-30506	107	12	provinces	province	NOUN
fcis-30506	107	13	with	with	ADP
fcis-30506	107	14	high	high	ADJ
fcis-30506	107	15	uncertainty	uncertainty	NOUN
fcis-30506	107	16	to	to	PART
fcis-30506	107	17	ensure	ensure	VERB
fcis-30506	107	18	solvency	solvency	NOUN
fcis-30506	107	19	adequacy	adequacy	NOUN
fcis-30506	107	20	.	.	PUNCT
fcis-30506	108	1	for	for	ADP
fcis-30506	108	2	extreme	extreme	ADJ
fcis-30506	108	3	risks	risk	NOUN
fcis-30506	108	4	outside	outside	ADP
fcis-30506	108	5	the	the	DET
fcis-30506	108	6	prediction	prediction	NOUN
fcis-30506	108	7	range	range	NOUN
fcis-30506	108	8	,	,	PUNCT
fcis-30506	108	9	transfer	transfer	VERB
fcis-30506	108	10	part	part	NOUN
fcis-30506	108	11	of	of	ADP
fcis-30506	108	12	the	the	DET
fcis-30506	108	13	risks	risk	NOUN
fcis-30506	108	14	through	through	ADP
fcis-30506	108	15	reinsurance	reinsurance	NOUN
fcis-30506	108	16	.	.	PUNCT
fcis-30506	109	1	4.2	4.2	NUM
fcis-30506	109	2	.	.	PUNCT
fcis-30506	109	3	outlook	outlook	NOUN
fcis-30506	109	4	it	it	PRON
fcis-30506	109	5	conducts	conduct	VERB
fcis-30506	109	6	research	research	NOUN
fcis-30506	109	7	and	and	CCONJ
fcis-30506	109	8	prediction	prediction	NOUN
fcis-30506	109	9	on	on	ADP
fcis-30506	109	10	the	the	DET
fcis-30506	109	11	regional	regional	ADJ
fcis-30506	109	12	differences	difference	NOUN
fcis-30506	109	13	of	of	ADP
fcis-30506	109	14	population	population	NOUN
fcis-30506	109	15	mortality	mortality	NOUN
fcis-30506	109	16	rates	rate	NOUN
fcis-30506	109	17	,	,	PUNCT
fcis-30506	109	18	but	but	CCONJ
fcis-30506	109	19	there	there	PRON
fcis-30506	109	20	are	be	VERB
fcis-30506	109	21	still	still	ADV
fcis-30506	109	22	many	many	ADJ
fcis-30506	109	23	aspects	aspect	NOUN
fcis-30506	109	24	that	that	PRON
fcis-30506	109	25	need	need	VERB
fcis-30506	109	26	improvement	improvement	NOUN
fcis-30506	109	27	.	.	PUNCT
fcis-30506	110	1	first	first	ADV
fcis-30506	110	2	,	,	PUNCT
fcis-30506	110	3	there	there	PRON
fcis-30506	110	4	is	be	VERB
fcis-30506	110	5	no	no	DET
fcis-30506	110	6	precise	precise	ADJ
fcis-30506	110	7	research	research	NOUN
fcis-30506	110	8	on	on	ADP
fcis-30506	110	9	how	how	SCONJ
fcis-30506	110	10	to	to	PART
fcis-30506	110	11	determine	determine	VERB
fcis-30506	110	12	the	the	DET
fcis-30506	110	13	number	number	NOUN
fcis-30506	110	14	of	of	ADP
fcis-30506	110	15	base	base	NOUN
fcis-30506	110	16	models	model	NOUN
fcis-30506	110	17	when	when	SCONJ
fcis-30506	110	18	multiple	multiple	ADJ
fcis-30506	110	19	gaussian	gaussian	ADJ
fcis-30506	110	20	process	process	NOUN
fcis-30506	110	21	regression	regression	NOUN
fcis-30506	110	22	models	model	NOUN
fcis-30506	110	23	are	be	AUX
fcis-30506	110	24	adopted	adopt	VERB
fcis-30506	110	25	.	.	PUNCT
fcis-30506	111	1	in	in	ADP
fcis-30506	111	2	the	the	DET
fcis-30506	111	3	future	future	NOUN
fcis-30506	111	4	,	,	PUNCT
fcis-30506	111	5	more	more	ADV
fcis-30506	111	6	complex	complex	ADJ
fcis-30506	111	7	combined	combined	ADJ
fcis-30506	111	8	kernel	kernel	NOUN
fcis-30506	111	9	functions	function	NOUN
fcis-30506	111	10	should	should	AUX
fcis-30506	111	11	be	be	AUX
fcis-30506	111	12	selected	select	VERB
fcis-30506	111	13	to	to	PART
fcis-30506	111	14	conduct	conduct	VERB
fcis-30506	111	15	more	more	ADJ
fcis-30506	111	16	complex	complex	ADJ
fcis-30506	111	17	mortality	mortality	NOUN
fcis-30506	111	18	rate	rate	NOUN
fcis-30506	111	19	predictions	prediction	NOUN
fcis-30506	111	20	.	.	PUNCT
fcis-30506	112	1	second	second	ADJ
fcis-30506	112	2	,	,	PUNCT
fcis-30506	112	3	the	the	DET
fcis-30506	112	4	data	datum	NOUN
fcis-30506	112	5	used	use	VERB
fcis-30506	112	6	only	only	ADV
fcis-30506	112	7	includes	include	VERB
fcis-30506	112	8	mortality	mortality	NOUN
fcis-30506	112	9	rates	rate	NOUN
fcis-30506	112	10	by	by	ADP
fcis-30506	112	11	province	province	NOUN
fcis-30506	112	12	,	,	PUNCT
fcis-30506	112	13	without	without	ADP
fcis-30506	112	14	considering	consider	VERB
fcis-30506	112	15	the	the	DET
fcis-30506	112	16	mortality	mortality	NOUN
fcis-30506	112	17	rates	rate	NOUN
fcis-30506	112	18	of	of	ADP
fcis-30506	112	19	different	different	ADJ
fcis-30506	112	20	genders	gender	NOUN
fcis-30506	112	21	and	and	CCONJ
fcis-30506	112	22	age	age	NOUN
fcis-30506	112	23	groups	group	NOUN
fcis-30506	112	24	within	within	ADP
fcis-30506	112	25	each	each	DET
fcis-30506	112	26	province	province	NOUN
fcis-30506	112	27	.	.	PUNCT
fcis-30506	113	1	in	in	ADP
fcis-30506	113	2	the	the	DET
fcis-30506	113	3	future	future	NOUN
fcis-30506	113	4	,	,	PUNCT
fcis-30506	113	5	it	it	PRON
fcis-30506	113	6	is	be	AUX
fcis-30506	113	7	necessary	necessary	ADJ
fcis-30506	113	8	to	to	PART
fcis-30506	113	9	further	far	ADV
fcis-30506	113	10	analyze	analyze	VERB
fcis-30506	113	11	regional	regional	ADJ
fcis-30506	113	12	mortality	mortality	NOUN
fcis-30506	113	13	rates	rate	NOUN
fcis-30506	113	14	by	by	ADP
fcis-30506	113	15	gender	gender	NOUN
fcis-30506	113	16	and	and	CCONJ
fcis-30506	113	17	age	age	NOUN
fcis-30506	113	18	group	group	NOUN
fcis-30506	113	19	to	to	PART
fcis-30506	113	20	provide	provide	VERB
fcis-30506	113	21	more	more	ADV
fcis-30506	113	22	specific	specific	ADJ
fcis-30506	113	23	data	datum	NOUN
fcis-30506	113	24	for	for	ADP
fcis-30506	113	25	insurance	insurance	NOUN
fcis-30506	113	26	companies	company	NOUN
fcis-30506	113	27	.	.	PUNCT
fcis-30506	114	1	third	third	ADJ
fcis-30506	114	2	,	,	PUNCT
fcis-30506	114	3	due	due	ADP
fcis-30506	114	4	to	to	ADP
fcis-30506	114	5	the	the	DET
fcis-30506	114	6	limitations	limitation	NOUN
fcis-30506	114	7	of	of	ADP
fcis-30506	114	8	research	research	NOUN
fcis-30506	114	9	data	datum	NOUN
fcis-30506	114	10	,	,	PUNCT
fcis-30506	114	11	this	this	DET
fcis-30506	114	12	paper	paper	NOUN
fcis-30506	114	13	only	only	ADV
fcis-30506	114	14	analyzed	analyze	VERB
fcis-30506	114	15	the	the	DET
fcis-30506	114	16	predicted	predict	VERB
fcis-30506	114	17	mortality	mortality	NOUN
fcis-30506	114	18	rates	rate	NOUN
fcis-30506	114	19	and	and	CCONJ
fcis-30506	114	20	did	do	AUX
fcis-30506	114	21	not	not	PART
fcis-30506	114	22	deeply	deeply	ADV
fcis-30506	114	23	explore	explore	VERB
fcis-30506	114	24	the	the	DET
fcis-30506	114	25	specific	specific	ADJ
fcis-30506	114	26	reasons	reason	NOUN
fcis-30506	114	27	for	for	ADP
fcis-30506	114	28	the	the	DET
fcis-30506	114	29	differences	difference	NOUN
fcis-30506	114	30	in	in	ADP
fcis-30506	114	31	population	population	NOUN
fcis-30506	114	32	mortality	mortality	NOUN
fcis-30506	114	33	rates	rate	NOUN
fcis-30506	114	34	among	among	ADP
fcis-30506	114	35	different	different	ADJ
fcis-30506	114	36	regions	region	NOUN
fcis-30506	114	37	.	.	PUNCT
fcis-30506	115	1	in	in	ADP
fcis-30506	115	2	future	future	ADJ
fcis-30506	115	3	research	research	NOUN
fcis-30506	115	4	,	,	PUNCT
fcis-30506	115	5	it	it	PRON
fcis-30506	115	6	is	be	AUX
fcis-30506	115	7	hoped	hope	VERB
fcis-30506	115	8	to	to	PART
fcis-30506	115	9	obtain	obtain	VERB
fcis-30506	115	10	more	more	ADJ
fcis-30506	115	11	data	datum	NOUN
fcis-30506	115	12	and	and	CCONJ
fcis-30506	115	13	be	be	AUX
fcis-30506	115	14	able	able	ADJ
fcis-30506	115	15	to	to	PART
fcis-30506	115	16	analyze	analyze	VERB
fcis-30506	115	17	the	the	DET
fcis-30506	115	18	dynamic	dynamic	ADJ
fcis-30506	115	19	evolution	evolution	NOUN
fcis-30506	115	20	of	of	ADP
fcis-30506	115	21	population	population	NOUN
fcis-30506	115	22	mortality	mortality	NOUN
fcis-30506	115	23	rates	rate	NOUN
fcis-30506	115	24	in	in	ADP
fcis-30506	115	25	different	different	ADJ
fcis-30506	115	26	provinces	province	NOUN
fcis-30506	115	27	more	more	ADV
fcis-30506	115	28	comprehensively	comprehensively	ADV
fcis-30506	115	29	and	and	CCONJ
fcis-30506	115	30	specifically	specifically	ADV
fcis-30506	115	31	.	.	PUNCT
fcis-30506	116	1	references	reference	NOUN
fcis-30506	116	2	[	[	X
fcis-30506	116	3	1	1	NUM
fcis-30506	116	4	]	]	X
fcis-30506	116	5	cheng	cheng	PROPN
fcis-30506	116	6	gongpin	gongpin	PROPN
fcis-30506	116	7	,	,	PUNCT
fcis-30506	116	8	shen	shen	PROPN
fcis-30506	116	9	shijie	shijie	PROPN
fcis-30506	116	10	,	,	PUNCT
fcis-30506	116	11	xu	xu	PROPN
fcis-30506	116	12	dongni	dongni	PROPN
fcis-30506	116	13	.	.	PUNCT
fcis-30506	117	1	pricing	pricing	NOUN
fcis-30506	117	2	strategies	strategy	NOUN
fcis-30506	117	3	for	for	ADP
fcis-30506	117	4	long	long	ADJ
fcis-30506	117	5	-	-	PUNCT
fcis-30506	117	6	term	term	NOUN
fcis-30506	117	7	care	care	NOUN
fcis-30506	117	8	insurance	insurance	NOUN
fcis-30506	117	9	products	product	NOUN
fcis-30506	117	10	in	in	ADP
fcis-30506	117	11	china	china	PROPN
fcis-30506	117	12	based	base	VERB
fcis-30506	117	13	on	on	ADP
fcis-30506	117	14	combined	combined	ADJ
fcis-30506	117	15	machine	machine	NOUN
fcis-30506	117	16	learning	learning	NOUN
fcis-30506	117	17	models	model	NOUN
fcis-30506	118	1	[	[	X
fcis-30506	118	2	j	j	X
fcis-30506	118	3	]	]	X
fcis-30506	118	4	.	.	PUNCT
fcis-30506	119	1	insurance	insurance	NOUN
fcis-30506	119	2	research	research	NOUN
fcis-30506	119	3	,	,	PUNCT
fcis-30506	119	4	2024	2024	NUM
fcis-30506	119	5	,	,	PUNCT
fcis-30506	119	6	(	(	PUNCT
fcis-30506	119	7	12	12	NUM
fcis-30506	119	8	):	):	PUNCT
fcis-30506	119	9	57	57	NUM
fcis-30506	119	10	-	-	SYM
fcis-30506	119	11	71	71	NUM
fcis-30506	119	12	.	.	PUNCT
fcis-30506	120	1	[	[	X
fcis-30506	120	2	2	2	NUM
fcis-30506	120	3	]	]	PUNCT
fcis-30506	120	4	wang	wang	PROPN
fcis-30506	120	5	tingting	tingting	PROPN
fcis-30506	120	6	.	.	PUNCT
fcis-30506	121	1	extension	extension	NOUN
fcis-30506	121	2	research	research	NOUN
fcis-30506	121	3	on	on	ADP
fcis-30506	121	4	mortality	mortality	NOUN
fcis-30506	121	5	models	model	NOUN
fcis-30506	121	6	and	and	CCONJ
fcis-30506	121	7	prediction	prediction	NOUN
fcis-30506	121	8	of	of	ADP
fcis-30506	121	9	china	china	PROPN
fcis-30506	121	10	's	's	PART
fcis-30506	121	11	population	population	NOUN
fcis-30506	121	12	mortality	mortality	NOUN
fcis-30506	121	13	rate	rate	NOUN
fcis-30506	122	1	[	[	X
fcis-30506	122	2	d	d	X
fcis-30506	122	3	]	]	X
fcis-30506	122	4	.	.	PUNCT
fcis-30506	123	1	zhejiang	zhejiang	PROPN
fcis-30506	123	2	university	university	PROPN
fcis-30506	123	3	,	,	PUNCT
fcis-30506	123	4	2014	2014	NUM
fcis-30506	123	5	.	.	PUNCT
fcis-30506	124	1	[	[	X
fcis-30506	124	2	3	3	X
fcis-30506	124	3	]	]	X
fcis-30506	124	4	wang	wang	PROPN
fcis-30506	124	5	zhigang	zhigang	PROPN
fcis-30506	124	6	,	,	PUNCT
fcis-30506	124	7	wang	wang	PROPN
fcis-30506	124	8	xiaojun	xiaojun	PROPN
fcis-30506	124	9	,	,	PUNCT
fcis-30506	124	10	zhang	zhang	PROPN
fcis-30506	124	11	xuebin	xuebin	PROPN
fcis-30506	124	12	.	.	PUNCT
fcis-30506	125	1	theoretical	theoretical	ADJ
fcis-30506	125	2	distribution	distribution	NOUN
fcis-30506	125	3	and	and	CCONJ
fcis-30506	125	4	interval	interval	NOUN
fcis-30506	125	5	prediction	prediction	NOUN
fcis-30506	125	6	of	of	ADP
fcis-30506	125	7	the	the	DET
fcis-30506	125	8	lee	lee	PROPN
fcis-30506	125	9	-	-	PUNCT
fcis-30506	125	10	carter	carter	PROPN
fcis-30506	125	11	model	model	NOUN
fcis-30506	126	1	[	[	X
fcis-30506	126	2	j	j	X
fcis-30506	126	3	]	]	X
fcis-30506	126	4	.	.	PUNCT
fcis-30506	127	1	mathematical	mathematical	ADJ
fcis-30506	127	2	and	and	CCONJ
fcis-30506	127	3	statistical	statistical	ADJ
fcis-30506	127	4	methods	method	NOUN
fcis-30506	127	5	in	in	ADP
fcis-30506	127	6	management	management	NOUN
fcis-30506	127	7	,	,	PUNCT
fcis-30506	127	8	2016	2016	NUM
fcis-30506	127	9	,	,	PUNCT
fcis-30506	127	10	35	35	NUM
fcis-30506	127	11	(	(	PUNCT
fcis-30506	127	12	03	03	NUM
fcis-30506	127	13	):	):	PUNCT
fcis-30506	127	14	484	484	NUM
fcis-30506	127	15	-	-	SYM
fcis-30506	127	16	493	493	NUM
fcis-30506	127	17	.	.	PUNCT
fcis-30506	128	1	[	[	X
fcis-30506	128	2	4	4	X
fcis-30506	128	3	]	]	X
fcis-30506	128	4	wang	wang	PROPN
fcis-30506	128	5	xiaojun	xiaojun	PROPN
fcis-30506	128	6	,	,	PUNCT
fcis-30506	128	7	zhao	zhao	PROPN
fcis-30506	128	8	xiaoyue	xiaoyue	PROPN
fcis-30506	128	9	,	,	PUNCT
fcis-30506	128	10	chen	chen	PROPN
fcis-30506	128	11	huimin	huimin	PROPN
fcis-30506	128	12	.	.	PUNCT
fcis-30506	129	1	joint	joint	ADJ
fcis-30506	129	2	modeling	modeling	NOUN
fcis-30506	129	3	and	and	CCONJ
fcis-30506	129	4	consistent	consistent	ADJ
fcis-30506	129	5	forecasting	forecasting	NOUN
fcis-30506	129	6	of	of	ADP
fcis-30506	129	7	mortality	mortality	NOUN
fcis-30506	129	8	rates	rate	NOUN
fcis-30506	129	9	for	for	ADP
fcis-30506	129	10	elderly	elderly	ADJ
fcis-30506	129	11	populations	population	NOUN
fcis-30506	129	12	with	with	ADP
fcis-30506	129	13	multiple	multiple	ADJ
fcis-30506	129	14	demographic	demographic	ADJ
fcis-30506	129	15	characteristics	characteristic	NOUN
fcis-30506	130	1	[	[	X
fcis-30506	130	2	j	j	X
fcis-30506	130	3	]	]	X
fcis-30506	130	4	.	.	PUNCT
fcis-30506	131	1	population	population	NOUN
fcis-30506	131	2	and	and	CCONJ
fcis-30506	131	3	economy	economy	NOUN
fcis-30506	131	4	,	,	PUNCT
fcis-30506	131	5	2021	2021	NUM
fcis-30506	131	6	,	,	PUNCT
fcis-30506	131	7	(	(	PUNCT
fcis-30506	131	8	02	02	NUM
fcis-30506	131	9	):	):	PUNCT
fcis-30506	131	10	45	45	NUM
fcis-30506	131	11	-	-	SYM
fcis-30506	131	12	56	56	NUM
fcis-30506	131	13	.	.	PUNCT
fcis-30506	132	1	[	[	X
fcis-30506	132	2	5	5	X
fcis-30506	132	3	]	]	PUNCT
fcis-30506	132	4	wang	wang	PROPN
fcis-30506	132	5	xiaojun	xiaojun	PROPN
fcis-30506	132	6	,	,	PUNCT
fcis-30506	132	7	lu	lu	PROPN
fcis-30506	132	8	qian	qian	PROPN
fcis-30506	132	9	.	.	PUNCT
fcis-30506	133	1	research	research	NOUN
fcis-30506	133	2	progress	progress	NOUN
fcis-30506	133	3	on	on	ADP
fcis-30506	133	4	dynamic	dynamic	ADJ
fcis-30506	133	5	mortality	mortality	NOUN
fcis-30506	133	6	models	model	NOUN
fcis-30506	134	1	[	[	X
fcis-30506	134	2	j	j	X
fcis-30506	134	3	]	]	X
fcis-30506	134	4	.	.	PUNCT
fcis-30506	135	1	journal	journal	PROPN
fcis-30506	135	2	of	of	ADP
fcis-30506	135	3	applied	apply	VERB
fcis-30506	135	4	probability	probability	NOUN
fcis-30506	135	5	and	and	CCONJ
fcis-30506	135	6	statistics	statistic	NOUN
fcis-30506	135	7	,	,	PUNCT
fcis-30506	135	8	2020	2020	NUM
fcis-30506	135	9	,	,	PUNCT
fcis-30506	135	10	36(04	36(04	NUM
fcis-30506	135	11	):	):	PUNCT
fcis-30506	135	12	415	415	NUM
fcis-30506	135	13	-	-	NUM
fcis-30506	135	14	440	440	NUM
fcis-30506	135	15	.	.	PUNCT
fcis-30506	136	1	[	[	X
fcis-30506	136	2	6	6	NUM
fcis-30506	136	3	]	]	PUNCT
fcis-30506	136	4	wu	wu	PROPN
fcis-30506	136	5	xiaokun	xiaokun	PROPN
fcis-30506	136	6	,	,	PUNCT
fcis-30506	136	7	li	li	PROPN
fcis-30506	136	8	yaojie	yaojie	PROPN
fcis-30506	136	9	.	.	PUNCT
fcis-30506	137	1	exponential	exponential	ADJ
fcis-30506	137	2	extrapolation	extrapolation	NOUN
fcis-30506	137	3	prediction	prediction	NOUN
fcis-30506	137	4	of	of	ADP
fcis-30506	137	5	mortality	mortality	NOUN
fcis-30506	137	6	rate	rate	NOUN
fcis-30506	137	7	and	and	CCONJ
fcis-30506	137	8	deviation	deviation	NOUN
fcis-30506	137	9	correction	correction	NOUN
fcis-30506	137	10	based	base	VERB
fcis-30506	137	11	on	on	ADP
fcis-30506	137	12	leecarter	leecarter	ADJ
fcis-30506	137	13	model	model	NOUN
fcis-30506	138	1	[	[	X
fcis-30506	138	2	j	j	X
fcis-30506	138	3	]	]	X
fcis-30506	138	4	.	.	PUNCT
fcis-30506	139	1	statistics	statistic	NOUN
fcis-30506	139	2	and	and	CCONJ
fcis-30506	139	3	decision	decision	NOUN
fcis-30506	139	4	,	,	PUNCT
fcis-30506	139	5	2016	2016	NUM
fcis-30506	139	6	,	,	PUNCT
fcis-30506	139	7	(	(	PUNCT
fcis-30506	139	8	20	20	NUM
fcis-30506	139	9	):	):	PUNCT
fcis-30506	139	10	19	19	NUM
fcis-30506	139	11	-	-	SYM
fcis-30506	139	12	21	21	NUM
fcis-30506	139	13	.	.	PUNCT
fcis-30506	140	1	[	[	X
fcis-30506	140	2	7	7	X
fcis-30506	140	3	]	]	X
fcis-30506	140	4	li	li	PROPN
fcis-30506	140	5	yangzheng	yangzheng	PROPN
fcis-30506	140	6	.	.	PUNCT
fcis-30506	141	1	estimation	estimation	NOUN
fcis-30506	141	2	and	and	CCONJ
fcis-30506	141	3	dynamic	dynamic	ADJ
fcis-30506	141	4	research	research	NOUN
fcis-30506	141	5	on	on	ADP
fcis-30506	141	6	population	population	NOUN
fcis-30506	141	7	mortality	mortality	NOUN
fcis-30506	141	8	rates	rate	NOUN
fcis-30506	141	9	in	in	ADP
fcis-30506	141	10	chinese	chinese	ADJ
fcis-30506	141	11	provinces	province	NOUN
fcis-30506	142	1	[	[	X
fcis-30506	142	2	d	d	X
fcis-30506	142	3	]	]	X
fcis-30506	142	4	.	.	PUNCT
fcis-30506	143	1	southwest	southwest	PROPN
fcis-30506	143	2	university	university	PROPN
fcis-30506	143	3	of	of	ADP
fcis-30506	143	4	finance	finance	NOUN
fcis-30506	143	5	and	and	CCONJ
fcis-30506	143	6	economics	economic	NOUN
fcis-30506	143	7	,	,	PUNCT
fcis-30506	143	8	2023	2023	NUM
fcis-30506	143	9	.	.	PUNCT
fcis-30506	144	1	[	[	X
fcis-30506	144	2	8	8	NUM
fcis-30506	144	3	]	]	X
fcis-30506	144	4	qiu	qiu	PROPN
fcis-30506	144	5	chunjuan	chunjuan	PROPN
fcis-30506	144	6	,	,	PUNCT
fcis-30506	144	7	guan	guan	PROPN
fcis-30506	144	8	huilin	huilin	PROPN
fcis-30506	144	9	,	,	PUNCT
fcis-30506	144	10	qian	qian	PROPN
fcis-30506	144	11	linyi	linyi	PROPN
fcis-30506	144	12	,	,	PUNCT
fcis-30506	144	13	wang	wang	PROPN
fcis-30506	144	14	wei	wei	PROPN
fcis-30506	144	15	.	.	PUNCT
fcis-30506	145	1	pricing	price	VERB
fcis-30506	145	2	research	research	NOUN
fcis-30506	145	3	on	on	ADP
fcis-30506	145	4	long	long	ADJ
fcis-30506	145	5	-	-	PUNCT
fcis-30506	145	6	term	term	NOUN
fcis-30506	145	7	care	care	NOUN
fcis-30506	145	8	insurance	insurance	NOUN
fcis-30506	145	9	:	:	PUNCT
fcis-30506	145	10	based	base	VERB
fcis-30506	145	11	on	on	ADP
fcis-30506	145	12	xgboost	xgboost	X
fcis-30506	145	13	algorithm	algorithm	PROPN
fcis-30506	145	14	and	and	CCONJ
fcis-30506	145	15	bp	bp	PROPN
fcis-30506	145	16	combined	combine	VERB
fcis-30506	145	17	neural	neural	ADJ
fcis-30506	145	18	network	network	NOUN
fcis-30506	145	19	model	model	NOUN
fcis-30506	146	1	[	[	X
fcis-30506	146	2	j	j	X
fcis-30506	146	3	]	]	X
fcis-30506	146	4	.	.	PUNCT
fcis-30506	147	1	insurance	insurance	NOUN
fcis-30506	147	2	research	research	NOUN
fcis-30506	147	3	,	,	PUNCT
fcis-30506	147	4	2020	2020	NUM
fcis-30506	147	5	,	,	PUNCT
fcis-30506	147	6	(	(	PUNCT
fcis-30506	147	7	12	12	NUM
fcis-30506	147	8	):	):	PUNCT
fcis-30506	147	9	38	38	NUM
fcis-30506	147	10	-	-	SYM
fcis-30506	147	11	53	53	NUM
fcis-30506	147	12	.	.	PUNCT
fcis-30506	148	1	[	[	X
fcis-30506	148	2	9	9	NUM
fcis-30506	148	3	]	]	X
fcis-30506	148	4	qiu	qiu	PROPN
fcis-30506	148	5	chunjuan	chunjuan	PROPN
fcis-30506	148	6	,	,	PUNCT
fcis-30506	148	7	liu	liu	PROPN
fcis-30506	148	8	shoushan	shoushan	PROPN
fcis-30506	148	9	,	,	PUNCT
fcis-30506	148	10	zhang	zhang	PROPN
fcis-30506	148	11	nan	nan	PROPN
fcis-30506	148	12	.	.	PUNCT
fcis-30506	149	1	research	research	NOUN
fcis-30506	149	2	on	on	ADP
fcis-30506	149	3	end	end	NOUN
fcis-30506	149	4	-	-	PUNCT
fcis-30506	149	5	toend	toend	ADJ
fcis-30506	149	6	long	long	ADJ
fcis-30506	149	7	-	-	PUNCT
fcis-30506	149	8	term	term	NOUN
fcis-30506	149	9	care	care	NOUN
fcis-30506	149	10	insurance	insurance	NOUN
fcis-30506	149	11	pricing	pricing	NOUN
fcis-30506	149	12	model	model	NOUN
fcis-30506	149	13	based	base	VERB
fcis-30506	149	14	on	on	ADP
fcis-30506	149	15	deep	deep	ADJ
fcis-30506	149	16	neural	neural	ADJ
fcis-30506	149	17	network	network	NOUN
fcis-30506	150	1	[	[	X
fcis-30506	150	2	j	j	X
fcis-30506	150	3	]	]	X
fcis-30506	150	4	.	.	PUNCT
fcis-30506	151	1	insurance	insurance	NOUN
fcis-30506	151	2	research	research	NOUN
fcis-30506	151	3	,	,	PUNCT
fcis-30506	151	4	2023	2023	NUM
fcis-30506	151	5	,	,	PUNCT
fcis-30506	151	6	(	(	PUNCT
fcis-30506	151	7	12	12	NUM
fcis-30506	151	8	):	):	PUNCT
fcis-30506	151	9	71	71	NUM
fcis-30506	151	10	-	-	SYM
fcis-30506	151	11	81	81	NUM
fcis-30506	151	12	.	.	PUNCT
fcis-30506	152	1	[	[	X
fcis-30506	152	2	10	10	NUM
fcis-30506	152	3	]	]	PUNCT
fcis-30506	152	4	a.	a.	NOUN
fcis-30506	152	5	milidonis	milidonis	PROPN
fcis-30506	152	6	,	,	PUNCT
fcis-30506	152	7	y.	y.	PROPN
fcis-30506	152	8	lin	lin	PROPN
fcis-30506	152	9	,	,	PUNCT
fcis-30506	152	10	s.	s.	PROPN
fcis-30506	152	11	h.	h.	PROPN
fcis-30506	152	12	cox	cox	PROPN
fcis-30506	152	13	.	.	PUNCT
fcis-30506	153	1	mortality	mortality	NOUN
fcis-30506	153	2	regimes	regime	NOUN
fcis-30506	153	3	and	and	CCONJ
fcis-30506	153	4	pricing[j	pricing[j	VERB
fcis-30506	153	5	]	]	PUNCT
fcis-30506	153	6	.	.	PUNCT
fcis-30506	154	1	north	north	PROPN
fcis-30506	154	2	american	american	ADJ
fcis-30506	154	3	actuarial	actuarial	PROPN
fcis-30506	154	4	journal	journal	PROPN
fcis-30506	154	5	,	,	PUNCT
fcis-30506	154	6	2011	2011	NUM
fcis-30506	154	7	,	,	PUNCT
fcis-30506	154	8	15(2	15(2	NUM
fcis-30506	154	9	)	)	PUNCT
fcis-30506	154	10	:	:	PUNCT
fcis-30506	154	11	266	266	NUM
fcis-30506	154	12	–	–	SYM
fcis-30506	154	13	289	289	NUM
fcis-30506	154	14	.	.	PUNCT