id	sid	tid	token	lemma	pos
ajst-3251	1	1	academic	academic	ADJ
ajst-3251	1	2	journal	journal	NOUN
ajst-3251	1	3	of	of	ADP
ajst-3251	1	4	science	science	NOUN
ajst-3251	1	5	and	and	CCONJ
ajst-3251	1	6	technology	technology	NOUN
ajst-3251	1	7	issn	issn	NOUN
ajst-3251	1	8	:	:	PUNCT
ajst-3251	1	9	2771	2771	NUM
ajst-3251	1	10	-	-	SYM
ajst-3251	1	11	3032	3032	NUM
ajst-3251	1	12	|	|	NOUN
ajst-3251	1	13	vol	vol	NOUN
ajst-3251	1	14	.	.	PROPN
ajst-3251	2	1	4	4	NUM
ajst-3251	2	2	,	,	PUNCT
ajst-3251	2	3	no	no	INTJ
ajst-3251	2	4	.	.	NOUN
ajst-3251	2	5	1	1	NUM
ajst-3251	2	6	,	,	PUNCT
ajst-3251	2	7	2022	2022	NUM
ajst-3251	2	8	49	49	NUM
ajst-3251	2	9	study	study	NOUN
ajst-3251	2	10	on	on	ADP
ajst-3251	2	11	rate	rate	NOUN
ajst-3251	2	12	of	of	ADP
ajst-3251	2	13	penetration	penetration	NOUN
ajst-3251	2	14	prediction	prediction	NOUN
ajst-3251	2	15	model	model	NOUN
ajst-3251	2	16	of	of	ADP
ajst-3251	2	17	offshore	offshore	ADJ
ajst-3251	2	18	drilling	drilling	NOUN
ajst-3251	2	19	machinery	machinery	NOUN
ajst-3251	2	20	and	and	CCONJ
ajst-3251	2	21	structure	structure	NOUN
ajst-3251	2	22	optimization	optimization	NOUN
ajst-3251	2	23	base	base	NOUN
ajst-3251	2	24	on	on	ADP
ajst-3251	2	25	deep	deep	ADJ
ajst-3251	2	26	learning	learn	VERB
ajst-3251	2	27	tong	tong	PROPN
ajst-3251	2	28	jiao	jiao	PROPN
ajst-3251	2	29	college	college	PROPN
ajst-3251	2	30	of	of	ADP
ajst-3251	2	31	petroleum	petroleum	NOUN
ajst-3251	2	32	engineering	engineering	NOUN
ajst-3251	2	33	,	,	PUNCT
ajst-3251	2	34	xi'an	xi'an	PROPN
ajst-3251	2	35	shiyou	shiyou	PROPN
ajst-3251	2	36	university	university	PROPN
ajst-3251	2	37	,	,	PUNCT
ajst-3251	2	38	xi'an	xi'an	PROPN
ajst-3251	2	39	710065	710065	NUM
ajst-3251	2	40	,	,	PUNCT
ajst-3251	2	41	china	china	PROPN
ajst-3251	2	42	abstract	abstract	PROPN
ajst-3251	2	43	:	:	PUNCT
ajst-3251	2	44	with	with	ADP
ajst-3251	2	45	the	the	DET
ajst-3251	2	46	development	development	NOUN
ajst-3251	2	47	of	of	ADP
ajst-3251	2	48	modern	modern	ADJ
ajst-3251	2	49	downhole	downhole	NOUN
ajst-3251	2	50	monitoring	monitoring	NOUN
ajst-3251	2	51	technologies	technology	NOUN
ajst-3251	2	52	such	such	ADJ
ajst-3251	2	53	as	as	ADP
ajst-3251	2	54	measurement	measurement	NOUN
ajst-3251	2	55	while	while	SCONJ
ajst-3251	2	56	drilling	drilling	NOUN
ajst-3251	2	57	,	,	PUNCT
ajst-3251	2	58	the	the	DET
ajst-3251	2	59	data	datum	NOUN
ajst-3251	2	60	obtained	obtain	VERB
ajst-3251	2	61	during	during	ADP
ajst-3251	2	62	drilling	drilling	NOUN
ajst-3251	2	63	has	have	VERB
ajst-3251	2	64	the	the	DET
ajst-3251	2	65	characteristics	characteristic	NOUN
ajst-3251	2	66	of	of	ADP
ajst-3251	2	67	huge	huge	ADJ
ajst-3251	2	68	volume	volume	NOUN
ajst-3251	2	69	,	,	PUNCT
ajst-3251	2	70	rich	rich	ADJ
ajst-3251	2	71	variety	variety	NOUN
ajst-3251	2	72	,	,	PUNCT
ajst-3251	2	73	and	and	CCONJ
ajst-3251	2	74	strong	strong	ADJ
ajst-3251	2	75	timeliness	timeliness	NOUN
ajst-3251	2	76	,	,	PUNCT
ajst-3251	2	77	which	which	PRON
ajst-3251	2	78	greatly	greatly	ADV
ajst-3251	2	79	promotes	promote	VERB
ajst-3251	2	80	big	big	ADJ
ajst-3251	2	81	data	datum	NOUN
ajst-3251	2	82	and	and	CCONJ
ajst-3251	2	83	artificial	artificial	ADJ
ajst-3251	2	84	intelligence	intelligence	NOUN
ajst-3251	2	85	in	in	ADP
ajst-3251	2	86	drilling	drilling	NOUN
ajst-3251	2	87	rate	rate	NOUN
ajst-3251	2	88	prediction	prediction	NOUN
ajst-3251	2	89	from	from	ADP
ajst-3251	2	90	the	the	DET
ajst-3251	2	91	data	data	NOUN
ajst-3251	2	92	level	level	NOUN
ajst-3251	2	93	.	.	PUNCT
ajst-3251	3	1	progress	progress	NOUN
ajst-3251	3	2	.	.	PUNCT
ajst-3251	4	1	how	how	SCONJ
ajst-3251	4	2	to	to	PART
ajst-3251	4	3	further	far	ADV
ajst-3251	4	4	effectively	effectively	ADV
ajst-3251	4	5	excavate	excavate	VERB
ajst-3251	4	6	and	and	CCONJ
ajst-3251	4	7	use	use	VERB
ajst-3251	4	8	drilling	drill	VERB
ajst-3251	4	9	big	big	ADJ
ajst-3251	4	10	data	datum	NOUN
ajst-3251	4	11	,	,	PUNCT
ajst-3251	4	12	improve	improve	VERB
ajst-3251	4	13	drilling	drilling	NOUN
ajst-3251	4	14	efficiency	efficiency	NOUN
ajst-3251	4	15	,	,	PUNCT
ajst-3251	4	16	and	and	CCONJ
ajst-3251	4	17	reduce	reduce	VERB
ajst-3251	4	18	drilling	drilling	NOUN
ajst-3251	4	19	risks	risk	NOUN
ajst-3251	4	20	is	be	AUX
ajst-3251	4	21	still	still	ADV
ajst-3251	4	22	a	a	DET
ajst-3251	4	23	hot	hot	ADJ
ajst-3251	4	24	research	research	NOUN
ajst-3251	4	25	topic	topic	NOUN
ajst-3251	4	26	.	.	PUNCT
ajst-3251	5	1	this	this	DET
ajst-3251	5	2	article	article	NOUN
ajst-3251	5	3	explores	explore	VERB
ajst-3251	5	4	the	the	DET
ajst-3251	5	5	real	real	ADJ
ajst-3251	5	6	-	-	PUNCT
ajst-3251	5	7	time	time	NOUN
ajst-3251	5	8	drilling	drilling	NOUN
ajst-3251	5	9	data	datum	NOUN
ajst-3251	5	10	of	of	ADP
ajst-3251	5	11	an	an	DET
ajst-3251	5	12	exploratory	exploratory	ADJ
ajst-3251	5	13	well	well	NOUN
ajst-3251	5	14	in	in	ADP
ajst-3251	5	15	the	the	DET
ajst-3251	5	16	south	south	PROPN
ajst-3251	5	17	china	china	PROPN
ajst-3251	5	18	sea	sea	PROPN
ajst-3251	5	19	,	,	PUNCT
ajst-3251	5	20	and	and	CCONJ
ajst-3251	5	21	establishes	establish	VERB
ajst-3251	5	22	a	a	DET
ajst-3251	5	23	reasonable	reasonable	ADJ
ajst-3251	5	24	machine	machine	NOUN
ajst-3251	5	25	learning	learn	VERB
ajst-3251	5	26	model	model	NOUN
ajst-3251	5	27	to	to	PART
ajst-3251	5	28	accurately	accurately	ADV
ajst-3251	5	29	predict	predict	VERB
ajst-3251	5	30	the	the	DET
ajst-3251	5	31	rop	rop	NOUN
ajst-3251	5	32	and	and	CCONJ
ajst-3251	5	33	its	its	PRON
ajst-3251	5	34	changing	change	VERB
ajst-3251	5	35	trend	trend	NOUN
ajst-3251	5	36	.	.	PUNCT
ajst-3251	6	1	the	the	DET
ajst-3251	6	2	research	research	NOUN
ajst-3251	6	3	first	first	ADV
ajst-3251	6	4	cleans	clean	VERB
ajst-3251	6	5	the	the	DET
ajst-3251	6	6	real	real	ADJ
ajst-3251	6	7	-	-	PUNCT
ajst-3251	6	8	time	time	NOUN
ajst-3251	6	9	drilling	drilling	NOUN
ajst-3251	6	10	data	datum	NOUN
ajst-3251	6	11	,	,	PUNCT
ajst-3251	6	12	and	and	CCONJ
ajst-3251	6	13	improves	improve	VERB
ajst-3251	6	14	the	the	DET
ajst-3251	6	15	data	datum	NOUN
ajst-3251	6	16	quality	quality	NOUN
ajst-3251	6	17	used	use	VERB
ajst-3251	6	18	for	for	ADP
ajst-3251	6	19	modeling	modeling	NOUN
ajst-3251	6	20	through	through	ADP
ajst-3251	6	21	standardized	standardized	ADJ
ajst-3251	6	22	data	datum	NOUN
ajst-3251	6	23	processing	processing	NOUN
ajst-3251	6	24	;	;	PUNCT
ajst-3251	6	25	then	then	ADV
ajst-3251	6	26	establishes	establish	VERB
ajst-3251	6	27	a	a	DET
ajst-3251	6	28	deep	deep	ADJ
ajst-3251	6	29	neural	neural	ADJ
ajst-3251	6	30	network	network	NOUN
ajst-3251	6	31	,	,	PUNCT
ajst-3251	6	32	optimizes	optimize	VERB
ajst-3251	6	33	input	input	NOUN
ajst-3251	6	34	and	and	CCONJ
ajst-3251	6	35	output	output	NOUN
ajst-3251	6	36	parameters	parameter	NOUN
ajst-3251	6	37	,	,	PUNCT
ajst-3251	6	38	trains	train	VERB
ajst-3251	6	39	the	the	DET
ajst-3251	6	40	model	model	NOUN
ajst-3251	6	41	,	,	PUNCT
ajst-3251	6	42	and	and	CCONJ
ajst-3251	6	43	validates	validate	VERB
ajst-3251	6	44	the	the	DET
ajst-3251	6	45	prediction	prediction	NOUN
ajst-3251	6	46	results	result	NOUN
ajst-3251	6	47	;	;	PUNCT
ajst-3251	6	48	finally	finally	ADV
ajst-3251	6	49	,	,	PUNCT
ajst-3251	6	50	optimizes	optimize	VERB
ajst-3251	6	51	the	the	DET
ajst-3251	6	52	model	model	NOUN
ajst-3251	6	53	structure	structure	NOUN
ajst-3251	6	54	to	to	PART
ajst-3251	6	55	improve	improve	VERB
ajst-3251	6	56	prediction	prediction	NOUN
ajst-3251	6	57	the	the	DET
ajst-3251	6	58	accuracy	accuracy	NOUN
ajst-3251	6	59	and	and	CCONJ
ajst-3251	6	60	efficiency	efficiency	NOUN
ajst-3251	6	61	of	of	ADP
ajst-3251	6	62	the	the	DET
ajst-3251	6	63	model	model	NOUN
ajst-3251	6	64	.	.	PUNCT
ajst-3251	7	1	the	the	DET
ajst-3251	7	2	research	research	NOUN
ajst-3251	7	3	results	result	NOUN
ajst-3251	7	4	show	show	VERB
ajst-3251	7	5	that	that	SCONJ
ajst-3251	7	6	when	when	SCONJ
ajst-3251	7	7	the	the	DET
ajst-3251	7	8	amount	amount	NOUN
ajst-3251	7	9	of	of	ADP
ajst-3251	7	10	data	datum	NOUN
ajst-3251	7	11	is	be	AUX
ajst-3251	7	12	sufficient	sufficient	ADJ
ajst-3251	7	13	and	and	CCONJ
ajst-3251	7	14	the	the	DET
ajst-3251	7	15	missing	miss	VERB
ajst-3251	7	16	values	value	NOUN
ajst-3251	7	17	are	be	AUX
ajst-3251	7	18	few	few	ADJ
ajst-3251	7	19	,	,	PUNCT
ajst-3251	7	20	the	the	DET
ajst-3251	7	21	neural	neural	ADJ
ajst-3251	7	22	network	network	NOUN
ajst-3251	7	23	can	can	AUX
ajst-3251	7	24	more	more	ADV
ajst-3251	7	25	accurately	accurately	ADV
ajst-3251	7	26	predict	predict	VERB
ajst-3251	7	27	the	the	DET
ajst-3251	7	28	rop	rop	NOUN
ajst-3251	7	29	and	and	CCONJ
ajst-3251	7	30	its	its	PRON
ajst-3251	7	31	change	change	NOUN
ajst-3251	7	32	trend	trend	NOUN
ajst-3251	7	33	,	,	PUNCT
ajst-3251	7	34	and	and	CCONJ
ajst-3251	7	35	at	at	ADP
ajst-3251	7	36	the	the	DET
ajst-3251	7	37	same	same	ADJ
ajst-3251	7	38	time	time	NOUN
ajst-3251	7	39	,	,	PUNCT
ajst-3251	7	40	reasonable	reasonable	ADJ
ajst-3251	7	41	optimization	optimization	NOUN
ajst-3251	7	42	of	of	ADP
ajst-3251	7	43	the	the	DET
ajst-3251	7	44	model	model	NOUN
ajst-3251	7	45	can	can	AUX
ajst-3251	7	46	also	also	ADV
ajst-3251	7	47	improve	improve	VERB
ajst-3251	7	48	the	the	DET
ajst-3251	7	49	accuracy	accuracy	NOUN
ajst-3251	7	50	and	and	CCONJ
ajst-3251	7	51	calculation	calculation	NOUN
ajst-3251	7	52	efficiency	efficiency	NOUN
ajst-3251	7	53	of	of	ADP
ajst-3251	7	54	the	the	DET
ajst-3251	7	55	drilling	drilling	NOUN
ajst-3251	7	56	rate	rate	NOUN
ajst-3251	7	57	prediction	prediction	NOUN
ajst-3251	7	58	.	.	PUNCT
ajst-3251	8	1	keywords	keyword	NOUN
ajst-3251	8	2	:	:	PUNCT
ajst-3251	8	3	drilling	drill	VERB
ajst-3251	8	4	real	real	ADJ
ajst-3251	8	5	-	-	PUNCT
ajst-3251	8	6	time	time	NOUN
ajst-3251	8	7	data	datum	NOUN
ajst-3251	8	8	,	,	PUNCT
ajst-3251	8	9	deep	deep	ADJ
ajst-3251	8	10	neural	neural	ADJ
ajst-3251	8	11	network	network	NOUN
ajst-3251	8	12	,	,	PUNCT
ajst-3251	8	13	rop	rop	PROPN
ajst-3251	8	14	prediction	prediction	NOUN
ajst-3251	8	15	,	,	PUNCT
ajst-3251	8	16	model	model	NOUN
ajst-3251	8	17	structure	structure	NOUN
ajst-3251	8	18	optimization	optimization	NOUN
ajst-3251	8	19	.	.	PUNCT
ajst-3251	9	1	1	1	X
ajst-3251	9	2	.	.	X
ajst-3251	9	3	introduction	introduction	NOUN
ajst-3251	9	4	raising	raise	VERB
ajst-3251	9	5	drilling	drilling	NOUN
ajst-3251	9	6	speed	speed	NOUN
ajst-3251	9	7	is	be	AUX
ajst-3251	9	8	one	one	NUM
ajst-3251	9	9	of	of	ADP
ajst-3251	9	10	the	the	DET
ajst-3251	9	11	goals	goal	NOUN
ajst-3251	9	12	pursued	pursue	VERB
ajst-3251	9	13	continuously	continuously	ADV
ajst-3251	9	14	in	in	ADP
ajst-3251	9	15	drilling	drill	VERB
ajst-3251	9	16	engineering	engineering	NOUN
ajst-3251	9	17	and	and	CCONJ
ajst-3251	9	18	operations	operation	NOUN
ajst-3251	9	19	.	.	PUNCT
ajst-3251	10	1	however	however	ADV
ajst-3251	10	2	,	,	PUNCT
ajst-3251	10	3	china	china	PROPN
ajst-3251	10	4	's	's	PART
ajst-3251	10	5	current	current	ADJ
ajst-3251	10	6	drilling	drilling	NOUN
ajst-3251	10	7	efficiency	efficiency	NOUN
ajst-3251	10	8	still	still	ADV
ajst-3251	10	9	lags	lag	VERB
ajst-3251	10	10	behind	behind	ADP
ajst-3251	10	11	the	the	DET
ajst-3251	10	12	advanced	advanced	ADJ
ajst-3251	10	13	level	level	NOUN
ajst-3251	10	14	abroad	abroad	ADV
ajst-3251	10	15	.	.	PUNCT
ajst-3251	11	1	according	accord	VERB
ajst-3251	11	2	to	to	ADP
ajst-3251	11	3	statistics	statistic	NOUN
ajst-3251	11	4	,	,	PUNCT
ajst-3251	11	5	at	at	ADP
ajst-3251	11	6	present	present	ADJ
ajst-3251	11	7	,	,	PUNCT
ajst-3251	11	8	a	a	DET
ajst-3251	11	9	domestic	domestic	ADJ
ajst-3251	11	10	drilling	drilling	NOUN
ajst-3251	11	11	rig	rig	NOUN
ajst-3251	11	12	can	can	AUX
ajst-3251	11	13	only	only	ADV
ajst-3251	11	14	drill	drill	VERB
ajst-3251	11	15	2	2	NUM
ajst-3251	11	16	-	-	SYM
ajst-3251	11	17	3	3	NUM
ajst-3251	11	18	horizontal	horizontal	ADJ
ajst-3251	11	19	wells	well	NOUN
ajst-3251	11	20	a	a	DET
ajst-3251	11	21	year	year	NOUN
ajst-3251	11	22	,	,	PUNCT
ajst-3251	11	23	while	while	SCONJ
ajst-3251	11	24	a	a	DET
ajst-3251	11	25	drilling	drilling	NOUN
ajst-3251	11	26	rig	rig	NOUN
ajst-3251	11	27	in	in	ADP
ajst-3251	11	28	north	north	PROPN
ajst-3251	11	29	america	america	PROPN
ajst-3251	11	30	can	can	AUX
ajst-3251	11	31	complete	complete	VERB
ajst-3251	11	32	15	15	NUM
ajst-3251	11	33	-	-	SYM
ajst-3251	11	34	20	20	NUM
ajst-3251	11	35	horizontal	horizontal	ADJ
ajst-3251	11	36	wells	well	NOUN
ajst-3251	11	37	a	a	DET
ajst-3251	11	38	year	year	NOUN
ajst-3251	11	39	.	.	PUNCT
ajst-3251	12	1	with	with	ADP
ajst-3251	12	2	the	the	DET
ajst-3251	12	3	large	large	ADJ
ajst-3251	12	4	-	-	PUNCT
ajst-3251	12	5	scale	scale	NOUN
ajst-3251	12	6	development	development	NOUN
ajst-3251	12	7	of	of	ADP
ajst-3251	12	8	domestic	domestic	ADJ
ajst-3251	12	9	offshore	offshore	ADJ
ajst-3251	12	10	oil	oil	NOUN
ajst-3251	12	11	and	and	CCONJ
ajst-3251	12	12	gas	gas	NOUN
ajst-3251	12	13	and	and	CCONJ
ajst-3251	12	14	unconventional	unconventional	ADJ
ajst-3251	12	15	oil	oil	NOUN
ajst-3251	12	16	and	and	CCONJ
ajst-3251	12	17	gas	gas	NOUN
ajst-3251	12	18	resources	resource	NOUN
ajst-3251	12	19	,	,	PUNCT
ajst-3251	12	20	the	the	DET
ajst-3251	12	21	contradiction	contradiction	NOUN
ajst-3251	12	22	of	of	ADP
ajst-3251	12	23	drilling	drill	VERB
ajst-3251	12	24	rig	rig	NOUN
ajst-3251	12	25	shortage	shortage	NOUN
ajst-3251	12	26	has	have	AUX
ajst-3251	12	27	become	become	VERB
ajst-3251	12	28	increasingly	increasingly	ADV
ajst-3251	12	29	prominent	prominent	ADJ
ajst-3251	12	30	.	.	PUNCT
ajst-3251	13	1	drilling	drilling	NOUN
ajst-3251	13	2	efficiency	efficiency	NOUN
ajst-3251	13	3	has	have	AUX
ajst-3251	13	4	become	become	VERB
ajst-3251	13	5	a	a	DET
ajst-3251	13	6	bottleneck	bottleneck	NOUN
ajst-3251	13	7	problem	problem	NOUN
ajst-3251	13	8	that	that	PRON
ajst-3251	13	9	seriously	seriously	ADV
ajst-3251	13	10	restricts	restrict	VERB
ajst-3251	13	11	the	the	DET
ajst-3251	13	12	rapid	rapid	ADJ
ajst-3251	13	13	scale	scale	NOUN
ajst-3251	13	14	production	production	NOUN
ajst-3251	13	15	of	of	ADP
ajst-3251	13	16	domestic	domestic	ADJ
ajst-3251	13	17	oil	oil	NOUN
ajst-3251	13	18	and	and	CCONJ
ajst-3251	13	19	gas	gas	NOUN
ajst-3251	13	20	resources	resource	NOUN
ajst-3251	13	21	.	.	PUNCT
ajst-3251	14	1	therefore	therefore	ADV
ajst-3251	14	2	,	,	PUNCT
ajst-3251	14	3	during	during	ADP
ajst-3251	14	4	the	the	DET
ajst-3251	14	5	drilling	drilling	NOUN
ajst-3251	14	6	process	process	NOUN
ajst-3251	14	7	,	,	PUNCT
ajst-3251	14	8	how	how	SCONJ
ajst-3251	14	9	to	to	PART
ajst-3251	14	10	effectively	effectively	ADV
ajst-3251	14	11	reduce	reduce	VERB
ajst-3251	14	12	the	the	DET
ajst-3251	14	13	drilling	drilling	NOUN
ajst-3251	14	14	cycle	cycle	NOUN
ajst-3251	14	15	,	,	PUNCT
ajst-3251	14	16	improve	improve	VERB
ajst-3251	14	17	the	the	DET
ajst-3251	14	18	drilling	drilling	NOUN
ajst-3251	14	19	efficiency	efficiency	NOUN
ajst-3251	14	20	and	and	CCONJ
ajst-3251	14	21	reduce	reduce	VERB
ajst-3251	14	22	the	the	DET
ajst-3251	14	23	drilling	drilling	NOUN
ajst-3251	14	24	cost	cost	NOUN
ajst-3251	14	25	has	have	AUX
ajst-3251	14	26	become	become	VERB
ajst-3251	14	27	a	a	DET
ajst-3251	14	28	key	key	ADJ
ajst-3251	14	29	hot	hot	ADJ
ajst-3251	14	30	issue	issue	NOUN
ajst-3251	14	31	in	in	ADP
ajst-3251	14	32	oil	oil	NOUN
ajst-3251	14	33	and	and	CCONJ
ajst-3251	14	34	gas	gas	NOUN
ajst-3251	14	35	exploration	exploration	NOUN
ajst-3251	14	36	and	and	CCONJ
ajst-3251	14	37	development	development	NOUN
ajst-3251	15	1	[	[	X
ajst-3251	15	2	1	1	NUM
ajst-3251	15	3	]	]	PUNCT
ajst-3251	15	4	.	.	PUNCT
ajst-3251	16	1	the	the	DET
ajst-3251	16	2	penetration	penetration	NOUN
ajst-3251	16	3	rate	rate	NOUN
ajst-3251	16	4	is	be	AUX
ajst-3251	16	5	the	the	DET
ajst-3251	16	6	key	key	ADJ
ajst-3251	16	7	factor	factor	NOUN
ajst-3251	16	8	to	to	PART
ajst-3251	16	9	measure	measure	VERB
ajst-3251	16	10	the	the	DET
ajst-3251	16	11	drilling	drilling	NOUN
ajst-3251	16	12	efficiency	efficiency	NOUN
ajst-3251	16	13	[	[	X
ajst-3251	16	14	2	2	NUM
ajst-3251	16	15	]	]	PUNCT
ajst-3251	16	16	,	,	PUNCT
ajst-3251	16	17	so	so	CCONJ
ajst-3251	16	18	it	it	PRON
ajst-3251	16	19	is	be	AUX
ajst-3251	16	20	one	one	NUM
ajst-3251	16	21	of	of	ADP
ajst-3251	16	22	the	the	DET
ajst-3251	16	23	hot	hot	ADJ
ajst-3251	16	24	spots	spot	NOUN
ajst-3251	16	25	in	in	ADP
ajst-3251	16	26	the	the	DET
ajst-3251	16	27	drilling	drilling	NOUN
ajst-3251	16	28	field	field	NOUN
ajst-3251	16	29	to	to	PART
ajst-3251	16	30	determine	determine	VERB
ajst-3251	16	31	the	the	DET
ajst-3251	16	32	penetration	penetration	NOUN
ajst-3251	16	33	rate	rate	NOUN
ajst-3251	16	34	by	by	ADP
ajst-3251	16	35	modeling	model	VERB
ajst-3251	16	36	the	the	DET
ajst-3251	16	37	influence	influence	NOUN
ajst-3251	16	38	of	of	ADP
ajst-3251	16	39	various	various	ADJ
ajst-3251	16	40	variables	variable	NOUN
ajst-3251	16	41	.	.	PUNCT
ajst-3251	17	1	with	with	ADP
ajst-3251	17	2	the	the	DET
ajst-3251	17	3	continuous	continuous	ADJ
ajst-3251	17	4	emergence	emergence	NOUN
ajst-3251	17	5	of	of	ADP
ajst-3251	17	6	new	new	ADJ
ajst-3251	17	7	drilling	drilling	NOUN
ajst-3251	17	8	tools	tool	NOUN
ajst-3251	17	9	,	,	PUNCT
ajst-3251	17	10	sensors	sensor	NOUN
ajst-3251	17	11	and	and	CCONJ
ajst-3251	17	12	downhole	downhole	NOUN
ajst-3251	17	13	acquisition	acquisition	NOUN
ajst-3251	17	14	tools	tool	NOUN
ajst-3251	17	15	in	in	ADP
ajst-3251	17	16	recent	recent	ADJ
ajst-3251	17	17	years	year	NOUN
ajst-3251	17	18	,	,	PUNCT
ajst-3251	17	19	downhole	downhole	ADJ
ajst-3251	17	20	intelligent	intelligent	ADJ
ajst-3251	17	21	measurement	measurement	NOUN
ajst-3251	17	22	systems	system	NOUN
ajst-3251	17	23	are	be	AUX
ajst-3251	17	24	becoming	become	VERB
ajst-3251	17	25	more	more	ADV
ajst-3251	17	26	and	and	CCONJ
ajst-3251	17	27	more	more	ADV
ajst-3251	17	28	complex	complex	ADJ
ajst-3251	17	29	,	,	PUNCT
ajst-3251	17	30	and	and	CCONJ
ajst-3251	17	31	the	the	DET
ajst-3251	17	32	type	type	NOUN
ajst-3251	17	33	and	and	CCONJ
ajst-3251	17	34	scale	scale	NOUN
ajst-3251	17	35	of	of	ADP
ajst-3251	17	36	data	datum	NOUN
ajst-3251	17	37	obtained	obtain	VERB
ajst-3251	17	38	are	be	AUX
ajst-3251	17	39	growing	grow	VERB
ajst-3251	17	40	exponentially	exponentially	ADV
ajst-3251	17	41	;	;	PUNCT
ajst-3251	17	42	however	however	ADV
ajst-3251	17	43	,	,	PUNCT
ajst-3251	17	44	these	these	DET
ajst-3251	17	45	data	datum	NOUN
ajst-3251	17	46	are	be	AUX
ajst-3251	17	47	not	not	PART
ajst-3251	17	48	completely	completely	ADV
ajst-3251	17	49	independent	independent	ADJ
ajst-3251	17	50	,	,	PUNCT
ajst-3251	17	51	and	and	CCONJ
ajst-3251	17	52	there	there	PRON
ajst-3251	17	53	are	be	VERB
ajst-3251	17	54	very	very	ADV
ajst-3251	17	55	complex	complex	ADJ
ajst-3251	17	56	internal	internal	ADJ
ajst-3251	17	57	links	link	NOUN
ajst-3251	17	58	between	between	ADP
ajst-3251	17	59	them	they	PRON
ajst-3251	17	60	.	.	PUNCT
ajst-3251	18	1	in	in	ADP
ajst-3251	18	2	addition	addition	NOUN
ajst-3251	18	3	,	,	PUNCT
ajst-3251	18	4	the	the	DET
ajst-3251	18	5	complexity	complexity	NOUN
ajst-3251	18	6	,	,	PUNCT
ajst-3251	18	7	diversity	diversity	NOUN
ajst-3251	18	8	and	and	CCONJ
ajst-3251	18	9	uncertainty	uncertainty	NOUN
ajst-3251	18	10	of	of	ADP
ajst-3251	18	11	underground	underground	ADJ
ajst-3251	18	12	environment	environment	NOUN
ajst-3251	18	13	and	and	CCONJ
ajst-3251	18	14	geological	geological	ADJ
ajst-3251	18	15	parameters	parameter	NOUN
ajst-3251	18	16	.	.	PUNCT
ajst-3251	19	1	among	among	ADP
ajst-3251	19	2	these	these	DET
ajst-3251	19	3	huge	huge	ADJ
ajst-3251	19	4	data	datum	NOUN
ajst-3251	19	5	,	,	PUNCT
ajst-3251	19	6	the	the	DET
ajst-3251	19	7	factors	factor	NOUN
ajst-3251	19	8	that	that	PRON
ajst-3251	19	9	affect	affect	VERB
ajst-3251	19	10	the	the	DET
ajst-3251	19	11	penetration	penetration	NOUN
ajst-3251	19	12	rate	rate	NOUN
ajst-3251	19	13	are	be	AUX
ajst-3251	19	14	also	also	ADV
ajst-3251	19	15	very	very	ADV
ajst-3251	19	16	complex	complex	ADJ
ajst-3251	19	17	and	and	CCONJ
ajst-3251	19	18	difficult	difficult	ADJ
ajst-3251	19	19	to	to	PART
ajst-3251	19	20	analyze	analyze	VERB
ajst-3251	19	21	,	,	PUNCT
ajst-3251	19	22	making	make	VERB
ajst-3251	19	23	the	the	DET
ajst-3251	19	24	traditional	traditional	ADJ
ajst-3251	19	25	data	datum	NOUN
ajst-3251	19	26	processing	processing	NOUN
ajst-3251	19	27	model	model	NOUN
ajst-3251	19	28	unable	unable	ADJ
ajst-3251	19	29	to	to	PART
ajst-3251	19	30	effectively	effectively	ADV
ajst-3251	19	31	process	process	VERB
ajst-3251	19	32	and	and	CCONJ
ajst-3251	19	33	analyze	analyze	VERB
ajst-3251	19	34	some	some	DET
ajst-3251	19	35	data	datum	NOUN
ajst-3251	19	36	.	.	PUNCT
ajst-3251	20	1	the	the	DET
ajst-3251	20	2	traditional	traditional	ADJ
ajst-3251	20	3	physical	physical	ADJ
ajst-3251	20	4	model	model	NOUN
ajst-3251	20	5	determines	determine	VERB
ajst-3251	20	6	the	the	DET
ajst-3251	20	7	importance	importance	NOUN
ajst-3251	20	8	of	of	ADP
ajst-3251	20	9	drilling	drilling	NOUN
ajst-3251	20	10	parameters	parameter	NOUN
ajst-3251	20	11	through	through	ADP
ajst-3251	20	12	analytical	analytical	ADJ
ajst-3251	20	13	method	method	NOUN
ajst-3251	20	14	,	,	PUNCT
ajst-3251	20	15	but	but	CCONJ
ajst-3251	20	16	it	it	PRON
ajst-3251	20	17	can	can	AUX
ajst-3251	20	18	not	not	PART
ajst-3251	20	19	cover	cover	VERB
ajst-3251	20	20	the	the	DET
ajst-3251	20	21	influence	influence	NOUN
ajst-3251	20	22	between	between	ADP
ajst-3251	20	23	parameters	parameter	NOUN
ajst-3251	20	24	[	[	X
ajst-3251	20	25	3	3	NUM
ajst-3251	20	26	]	]	PUNCT
ajst-3251	20	27	.	.	PUNCT
ajst-3251	21	1	soares	soar	VERB
ajst-3251	21	2	[	[	X
ajst-3251	21	3	4	4	NUM
ajst-3251	21	4	]	]	PUNCT
ajst-3251	21	5	et	et	PROPN
ajst-3251	21	6	al	al	PROPN
ajst-3251	21	7	.	.	PROPN
ajst-3251	21	8	revealed	reveal	VERB
ajst-3251	21	9	several	several	ADJ
ajst-3251	21	10	limitations	limitation	NOUN
ajst-3251	21	11	of	of	ADP
ajst-3251	21	12	using	use	VERB
ajst-3251	21	13	analytical	analytical	ADJ
ajst-3251	21	14	equations	equation	NOUN
ajst-3251	21	15	to	to	PART
ajst-3251	21	16	model	model	VERB
ajst-3251	21	17	the	the	DET
ajst-3251	21	18	penetration	penetration	NOUN
ajst-3251	21	19	rate	rate	NOUN
ajst-3251	21	20	of	of	ADP
ajst-3251	21	21	machinery	machinery	NOUN
ajst-3251	21	22	.	.	PUNCT
ajst-3251	22	1	therefore	therefore	ADV
ajst-3251	22	2	,	,	PUNCT
ajst-3251	22	3	people	people	NOUN
ajst-3251	22	4	begin	begin	VERB
ajst-3251	22	5	to	to	PART
ajst-3251	22	6	use	use	VERB
ajst-3251	22	7	data	data	NOUN
ajst-3251	22	8	-	-	PUNCT
ajst-3251	22	9	driven	drive	VERB
ajst-3251	22	10	models	model	NOUN
ajst-3251	22	11	in	in	ADP
ajst-3251	22	12	the	the	DET
ajst-3251	22	13	field	field	NOUN
ajst-3251	22	14	of	of	ADP
ajst-3251	22	15	artificial	artificial	ADJ
ajst-3251	22	16	intelligence	intelligence	NOUN
ajst-3251	22	17	.	.	PUNCT
ajst-3251	23	1	taking	take	VERB
ajst-3251	23	2	offshore	offshore	ADV
ajst-3251	23	3	drilling	drilling	NOUN
ajst-3251	23	4	as	as	ADP
ajst-3251	23	5	an	an	DET
ajst-3251	23	6	example	example	NOUN
ajst-3251	23	7	,	,	PUNCT
ajst-3251	23	8	due	due	ADP
ajst-3251	23	9	to	to	ADP
ajst-3251	23	10	the	the	DET
ajst-3251	23	11	complexity	complexity	NOUN
ajst-3251	23	12	of	of	ADP
ajst-3251	23	13	conditions	condition	NOUN
ajst-3251	23	14	and	and	CCONJ
ajst-3251	23	15	the	the	DET
ajst-3251	23	16	huge	huge	ADJ
ajst-3251	23	17	amount	amount	NOUN
ajst-3251	23	18	of	of	ADP
ajst-3251	23	19	drilling	drilling	NOUN
ajst-3251	23	20	data	datum	NOUN
ajst-3251	23	21	,	,	PUNCT
ajst-3251	23	22	the	the	DET
ajst-3251	23	23	penetration	penetration	NOUN
ajst-3251	23	24	rate	rate	NOUN
ajst-3251	23	25	equation	equation	NOUN
ajst-3251	23	26	constructed	construct	VERB
ajst-3251	23	27	by	by	ADP
ajst-3251	23	28	a	a	DET
ajst-3251	23	29	single	single	ADJ
ajst-3251	23	30	factor	factor	NOUN
ajst-3251	23	31	has	have	AUX
ajst-3251	23	32	been	be	AUX
ajst-3251	23	33	difficult	difficult	ADJ
ajst-3251	23	34	to	to	PART
ajst-3251	23	35	meet	meet	VERB
ajst-3251	23	36	the	the	DET
ajst-3251	23	37	needs	need	NOUN
ajst-3251	23	38	of	of	ADP
ajst-3251	23	39	modern	modern	ADJ
ajst-3251	23	40	offshore	offshore	ADJ
ajst-3251	23	41	drilling	drilling	NOUN
ajst-3251	23	42	,	,	PUNCT
ajst-3251	23	43	and	and	CCONJ
ajst-3251	23	44	the	the	DET
ajst-3251	23	45	development	development	NOUN
ajst-3251	23	46	of	of	ADP
ajst-3251	23	47	artificial	artificial	ADJ
ajst-3251	23	48	intelligence	intelligence	NOUN
ajst-3251	23	49	has	have	AUX
ajst-3251	23	50	made	make	VERB
ajst-3251	23	51	long	long	ADJ
ajst-3251	23	52	-	-	PUNCT
ajst-3251	23	53	term	term	NOUN
ajst-3251	23	54	progress	progress	NOUN
ajst-3251	23	55	in	in	ADP
ajst-3251	23	56	exploring	explore	VERB
ajst-3251	23	57	drilling	drilling	NOUN
ajst-3251	23	58	intelligence	intelligence	NOUN
ajst-3251	23	59	.	.	PUNCT
ajst-3251	24	1	scholars	scholar	NOUN
ajst-3251	24	2	at	at	ADP
ajst-3251	24	3	home	home	ADV
ajst-3251	24	4	and	and	CCONJ
ajst-3251	24	5	abroad	abroad	ADV
ajst-3251	24	6	are	be	AUX
ajst-3251	24	7	exploring	explore	VERB
ajst-3251	24	8	the	the	DET
ajst-3251	24	9	application	application	NOUN
ajst-3251	24	10	of	of	ADP
ajst-3251	24	11	big	big	ADJ
ajst-3251	24	12	data	datum	NOUN
ajst-3251	24	13	and	and	CCONJ
ajst-3251	24	14	deep	deep	ADJ
ajst-3251	24	15	learning	learning	NOUN
ajst-3251	24	16	in	in	ADP
ajst-3251	24	17	petroleum	petroleum	NOUN
ajst-3251	24	18	engineering	engineering	NOUN
ajst-3251	24	19	,	,	PUNCT
ajst-3251	24	20	and	and	CCONJ
ajst-3251	24	21	building	build	VERB
ajst-3251	24	22	real	real	ADJ
ajst-3251	24	23	-	-	PUNCT
ajst-3251	24	24	time	time	NOUN
ajst-3251	24	25	multi	multi	ADJ
ajst-3251	24	26	factor	factor	NOUN
ajst-3251	24	27	intelligent	intelligent	ADJ
ajst-3251	24	28	drilling	drilling	NOUN
ajst-3251	24	29	equations	equation	NOUN
ajst-3251	24	30	based	base	VERB
ajst-3251	24	31	on	on	ADP
ajst-3251	24	32	data	datum	NOUN
ajst-3251	24	33	drive	drive	NOUN
ajst-3251	24	34	.	.	PUNCT
ajst-3251	25	1	abdulmalek	abdulmalek	ADJ
ajst-3251	26	1	[	[	X
ajst-3251	26	2	5	5	NUM
ajst-3251	26	3	]	]	PUNCT
ajst-3251	26	4	and	and	CCONJ
ajst-3251	26	5	others	other	NOUN
ajst-3251	26	6	demonstrated	demonstrate	VERB
ajst-3251	26	7	the	the	DET
ajst-3251	26	8	feasibility	feasibility	NOUN
ajst-3251	26	9	of	of	ADP
ajst-3251	26	10	artificial	artificial	ADJ
ajst-3251	26	11	neural	neural	ADJ
ajst-3251	26	12	network	network	NOUN
ajst-3251	26	13	for	for	ADP
ajst-3251	26	14	drilling	drilling	NOUN
ajst-3251	26	15	data	datum	NOUN
ajst-3251	26	16	prediction	prediction	NOUN
ajst-3251	26	17	and	and	CCONJ
ajst-3251	26	18	analyzed	analyze	VERB
ajst-3251	26	19	the	the	DET
ajst-3251	26	20	impact	impact	NOUN
ajst-3251	26	21	of	of	ADP
ajst-3251	26	22	multiple	multiple	ADJ
ajst-3251	26	23	factors	factor	NOUN
ajst-3251	26	24	on	on	ADP
ajst-3251	26	25	drilling	drilling	NOUN
ajst-3251	26	26	speed	speed	NOUN
ajst-3251	26	27	.	.	PUNCT
ajst-3251	27	1	li	li	PROPN
ajst-3251	27	2	qi	qi	PROPN
ajst-3251	27	3	and	and	CCONJ
ajst-3251	27	4	others	other	NOUN
ajst-3251	27	5	optimized	optimize	VERB
ajst-3251	27	6	the	the	DET
ajst-3251	27	7	backpropagation	backpropagation	NOUN
ajst-3251	27	8	network	network	NOUN
ajst-3251	27	9	with	with	ADP
ajst-3251	27	10	the	the	DET
ajst-3251	27	11	tenebrus	tenebrus	NOUN
ajst-3251	27	12	algorithm	algorithm	NOUN
ajst-3251	27	13	,	,	PUNCT
ajst-3251	27	14	particle	particle	NOUN
ajst-3251	27	15	swarm	swarm	NOUN
ajst-3251	27	16	optimization	optimization	NOUN
ajst-3251	27	17	algorithm	algorithm	NOUN
ajst-3251	27	18	and	and	CCONJ
ajst-3251	27	19	genetic	genetic	ADJ
ajst-3251	27	20	algorithm	algorithm	NOUN
ajst-3251	27	21	,	,	PUNCT
ajst-3251	27	22	which	which	PRON
ajst-3251	27	23	greatly	greatly	ADV
ajst-3251	27	24	improved	improve	VERB
ajst-3251	27	25	the	the	DET
ajst-3251	27	26	accuracy	accuracy	NOUN
ajst-3251	27	27	of	of	ADP
ajst-3251	27	28	the	the	DET
ajst-3251	27	29	mechanical	mechanical	ADJ
ajst-3251	27	30	penetration	penetration	NOUN
ajst-3251	27	31	rate	rate	NOUN
ajst-3251	27	32	prediction	prediction	NOUN
ajst-3251	27	33	model	model	NOUN
ajst-3251	27	34	.	.	PUNCT
ajst-3251	28	1	a	a	DET
ajst-3251	28	2	comparative	comparative	ADJ
ajst-3251	28	3	study	study	NOUN
ajst-3251	28	4	of	of	ADP
ajst-3251	28	5	the	the	DET
ajst-3251	28	6	differences	difference	NOUN
ajst-3251	28	7	between	between	ADP
ajst-3251	28	8	machine	machine	NOUN
ajst-3251	28	9	learning	learn	VERB
ajst-3251	28	10	methods	method	NOUN
ajst-3251	28	11	and	and	CCONJ
ajst-3251	28	12	physics	physics	NOUN
ajst-3251	28	13	based	base	VERB
ajst-3251	28	14	models	model	NOUN
ajst-3251	28	15	proves	prove	VERB
ajst-3251	28	16	that	that	SCONJ
ajst-3251	28	17	machine	machine	NOUN
ajst-3251	28	18	learning	learning	NOUN
ajst-3251	28	19	methods	method	NOUN
ajst-3251	28	20	have	have	VERB
ajst-3251	28	21	higher	high	ADJ
ajst-3251	28	22	accuracy	accuracy	NOUN
ajst-3251	29	1	[	[	X
ajst-3251	29	2	6	6	NUM
ajst-3251	29	3	-	-	SYM
ajst-3251	29	4	8	8	NUM
ajst-3251	29	5	]	]	PUNCT
ajst-3251	29	6	.	.	PUNCT
ajst-3251	30	1	hadi	hadi	PROPN
ajst-3251	30	2	et	et	PROPN
ajst-3251	30	3	al	al	PROPN
ajst-3251	30	4	.	.	PROPN
ajst-3251	30	5	evaluated	evaluate	VERB
ajst-3251	30	6	the	the	DET
ajst-3251	30	7	role	role	NOUN
ajst-3251	30	8	of	of	ADP
ajst-3251	30	9	multiple	multiple	ADJ
ajst-3251	30	10	regression	regression	NOUN
ajst-3251	30	11	analysis	analysis	NOUN
ajst-3251	30	12	and	and	CCONJ
ajst-3251	30	13	artificial	artificial	ADJ
ajst-3251	30	14	neural	neural	ADJ
ajst-3251	30	15	network	network	NOUN
ajst-3251	30	16	in	in	ADP
ajst-3251	30	17	rop	rop	PROPN
ajst-3251	30	18	prediction	prediction	NOUN
ajst-3251	30	19	[	[	X
ajst-3251	30	20	9	9	NUM
ajst-3251	30	21	]	]	PUNCT
ajst-3251	30	22	.	.	PUNCT
ajst-3251	31	1	aliyev	aliyev	PROPN
ajst-3251	31	2	and	and	CCONJ
ajst-3251	31	3	paul	paul	PROPN
ajst-3251	31	4	also	also	ADV
ajst-3251	31	5	used	use	VERB
ajst-3251	31	6	the	the	DET
ajst-3251	31	7	artificial	artificial	ADJ
ajst-3251	31	8	neural	neural	ADJ
ajst-3251	31	9	network	network	NOUN
ajst-3251	31	10	model	model	NOUN
ajst-3251	31	11	,	,	PUNCT
ajst-3251	31	12	but	but	CCONJ
ajst-3251	31	13	they	they	PRON
ajst-3251	31	14	integrated	integrate	VERB
ajst-3251	31	15	both	both	CCONJ
ajst-3251	31	16	downhole	downhole	NOUN
ajst-3251	31	17	and	and	CCONJ
ajst-3251	31	18	surface	surface	NOUN
ajst-3251	31	19	parameters	parameter	NOUN
ajst-3251	31	20	in	in	ADP
ajst-3251	31	21	their	their	PRON
ajst-3251	31	22	research	research	NOUN
ajst-3251	31	23	[	[	X
ajst-3251	31	24	10	10	NUM
ajst-3251	31	25	]	]	PUNCT
ajst-3251	31	26	.	.	PUNCT
ajst-3251	32	1	at	at	ADP
ajst-3251	32	2	present	present	ADJ
ajst-3251	32	3	,	,	PUNCT
ajst-3251	32	4	scholars	scholar	NOUN
ajst-3251	32	5	at	at	ADP
ajst-3251	32	6	home	home	ADV
ajst-3251	32	7	and	and	CCONJ
ajst-3251	32	8	abroad	abroad	ADV
ajst-3251	32	9	have	have	AUX
ajst-3251	32	10	established	establish	VERB
ajst-3251	32	11	a	a	DET
ajst-3251	32	12	variety	variety	NOUN
ajst-3251	32	13	of	of	ADP
ajst-3251	32	14	models	model	NOUN
ajst-3251	32	15	for	for	ADP
ajst-3251	32	16	rop	rop	NOUN
ajst-3251	32	17	prediction	prediction	NOUN
ajst-3251	32	18	,	,	PUNCT
ajst-3251	32	19	but	but	CCONJ
ajst-3251	32	20	most	most	ADJ
ajst-3251	32	21	of	of	ADP
ajst-3251	32	22	the	the	DET
ajst-3251	32	23	models	model	NOUN
ajst-3251	32	24	remain	remain	VERB
ajst-3251	32	25	in	in	ADP
ajst-3251	32	26	a	a	DET
ajst-3251	32	27	single	single	ADJ
ajst-3251	32	28	middle	middle	ADJ
ajst-3251	32	29	layer	layer	NOUN
ajst-3251	32	30	network	network	NOUN
ajst-3251	32	31	structure	structure	NOUN
ajst-3251	32	32	,	,	PUNCT
ajst-3251	32	33	which	which	PRON
ajst-3251	32	34	can	can	AUX
ajst-3251	32	35	not	not	PART
ajst-3251	32	36	effectively	effectively	ADV
ajst-3251	32	37	represent	represent	VERB
ajst-3251	32	38	the	the	DET
ajst-3251	32	39	nonlinear	nonlinear	ADJ
ajst-3251	32	40	law	law	NOUN
ajst-3251	32	41	of	of	ADP
ajst-3251	32	42	the	the	DET
ajst-3251	32	43	drilling	drilling	NOUN
ajst-3251	32	44	process	process	NOUN
ajst-3251	32	45	;	;	PUNCT
ajst-3251	32	46	at	at	ADP
ajst-3251	32	47	the	the	DET
ajst-3251	32	48	same	same	ADJ
ajst-3251	32	49	time	time	NOUN
ajst-3251	32	50	,	,	PUNCT
ajst-3251	32	51	the	the	DET
ajst-3251	32	52	existing	exist	VERB
ajst-3251	32	53	research	research	NOUN
ajst-3251	32	54	lacks	lack	NOUN
ajst-3251	32	55	of	of	ADP
ajst-3251	32	56	research	research	NOUN
ajst-3251	32	57	on	on	ADP
ajst-3251	32	58	model	model	NOUN
ajst-3251	32	59	structure	structure	NOUN
ajst-3251	32	60	and	and	CCONJ
ajst-3251	32	61	model	model	NOUN
ajst-3251	32	62	parameter	parameter	PROPN
ajst-3251	32	63	optimization	optimization	PROPN
ajst-3251	32	64	,	,	PUNCT
ajst-3251	32	65	which	which	PRON
ajst-3251	32	66	makes	make	VERB
ajst-3251	32	67	most	most	ADJ
ajst-3251	32	68	of	of	ADP
ajst-3251	32	69	the	the	DET
ajst-3251	32	70	drilling	drilling	NOUN
ajst-3251	32	71	speed	speed	NOUN
ajst-3251	32	72	prediction	prediction	NOUN
ajst-3251	32	73	models	model	NOUN
ajst-3251	32	74	have	have	VERB
ajst-3251	32	75	a	a	DET
ajst-3251	32	76	single	single	ADJ
ajst-3251	32	77	structure	structure	NOUN
ajst-3251	32	78	and	and	CCONJ
ajst-3251	32	79	low	low	ADJ
ajst-3251	32	80	efficiency	efficiency	NOUN
ajst-3251	32	81	.	.	PUNCT
ajst-3251	33	1	50	50	NUM
ajst-3251	33	2	therefore	therefore	ADV
ajst-3251	33	3	,	,	PUNCT
ajst-3251	33	4	the	the	DET
ajst-3251	33	5	depth	depth	NOUN
ajst-3251	33	6	neural	neural	ADJ
ajst-3251	33	7	network	network	NOUN
ajst-3251	33	8	model	model	NOUN
ajst-3251	33	9	is	be	AUX
ajst-3251	33	10	selected	select	VERB
ajst-3251	33	11	for	for	ADP
ajst-3251	33	12	full	full	ADJ
ajst-3251	33	13	demonstration	demonstration	NOUN
ajst-3251	33	14	in	in	ADP
ajst-3251	33	15	this	this	DET
ajst-3251	33	16	paper	paper	NOUN
ajst-3251	33	17	,	,	PUNCT
ajst-3251	33	18	and	and	CCONJ
ajst-3251	33	19	the	the	DET
ajst-3251	33	20	model	model	NOUN
ajst-3251	33	21	structure	structure	NOUN
ajst-3251	33	22	is	be	AUX
ajst-3251	33	23	analyzed	analyze	VERB
ajst-3251	33	24	at	at	ADP
ajst-3251	33	25	multiple	multiple	ADJ
ajst-3251	33	26	levels	level	NOUN
ajst-3251	33	27	and	and	CCONJ
ajst-3251	33	28	nodes	node	NOUN
ajst-3251	33	29	.	.	PUNCT
ajst-3251	34	1	by	by	ADP
ajst-3251	34	2	comparing	compare	VERB
ajst-3251	34	3	the	the	DET
ajst-3251	34	4	operation	operation	NOUN
ajst-3251	34	5	effect	effect	NOUN
ajst-3251	34	6	and	and	CCONJ
ajst-3251	34	7	operation	operation	NOUN
ajst-3251	34	8	efficiency	efficiency	NOUN
ajst-3251	34	9	of	of	ADP
ajst-3251	34	10	different	different	ADJ
ajst-3251	34	11	structural	structural	ADJ
ajst-3251	34	12	models	model	NOUN
ajst-3251	34	13	,	,	PUNCT
ajst-3251	34	14	the	the	DET
ajst-3251	34	15	highest	high	ADJ
ajst-3251	34	16	efficiency	efficiency	NOUN
ajst-3251	34	17	penetration	penetration	NOUN
ajst-3251	34	18	rate	rate	NOUN
ajst-3251	34	19	prediction	prediction	NOUN
ajst-3251	34	20	model	model	NOUN
ajst-3251	34	21	is	be	AUX
ajst-3251	34	22	finally	finally	ADV
ajst-3251	34	23	selected	select	VERB
ajst-3251	34	24	to	to	PART
ajst-3251	34	25	provide	provide	VERB
ajst-3251	34	26	support	support	NOUN
ajst-3251	34	27	for	for	ADP
ajst-3251	34	28	onsite	onsite	ADJ
ajst-3251	34	29	testing	testing	NOUN
ajst-3251	34	30	and	and	CCONJ
ajst-3251	34	31	application	application	NOUN
ajst-3251	34	32	.	.	PUNCT
ajst-3251	35	1	2	2	X
ajst-3251	35	2	.	.	X
ajst-3251	35	3	introduction	introduction	NOUN
ajst-3251	35	4	to	to	ADP
ajst-3251	35	5	deep	deep	ADJ
ajst-3251	35	6	neural	neural	ADJ
ajst-3251	35	7	network	network	NOUN
ajst-3251	35	8	deep	deep	ADJ
ajst-3251	35	9	neural	neural	ADJ
ajst-3251	35	10	network	network	NOUN
ajst-3251	35	11	(	(	PUNCT
ajst-3251	35	12	dnn	dnn	PROPN
ajst-3251	35	13	)	)	PUNCT
ajst-3251	35	14	is	be	AUX
ajst-3251	35	15	one	one	NUM
ajst-3251	35	16	of	of	ADP
ajst-3251	35	17	the	the	DET
ajst-3251	35	18	most	most	ADV
ajst-3251	35	19	commonly	commonly	ADV
ajst-3251	35	20	used	use	VERB
ajst-3251	35	21	methods	method	NOUN
ajst-3251	35	22	for	for	ADP
ajst-3251	35	23	deep	deep	ADJ
ajst-3251	35	24	learning	learning	NOUN
ajst-3251	35	25	.	.	PUNCT
ajst-3251	36	1	its	its	PRON
ajst-3251	36	2	most	most	ADV
ajst-3251	36	3	typical	typical	ADJ
ajst-3251	36	4	implementation	implementation	NOUN
ajst-3251	36	5	is	be	AUX
ajst-3251	36	6	a	a	DET
ajst-3251	36	7	multi	multi	ADJ
ajst-3251	36	8	-	-	ADJ
ajst-3251	36	9	layer	layer	ADJ
ajst-3251	36	10	perceptron	perceptron	PROPN
ajst-3251	36	11	,	,	PUNCT
ajst-3251	36	12	which	which	PRON
ajst-3251	36	13	is	be	AUX
ajst-3251	36	14	generally	generally	ADV
ajst-3251	36	15	composed	compose	VERB
ajst-3251	36	16	of	of	ADP
ajst-3251	36	17	an	an	DET
ajst-3251	36	18	input	input	NOUN
ajst-3251	36	19	layer	layer	NOUN
ajst-3251	36	20	,	,	PUNCT
ajst-3251	36	21	multiple	multiple	ADJ
ajst-3251	36	22	hidden	hide	VERB
ajst-3251	36	23	layers	layer	NOUN
ajst-3251	36	24	and	and	CCONJ
ajst-3251	36	25	an	an	DET
ajst-3251	36	26	output	output	NOUN
ajst-3251	36	27	layer	layer	NOUN
ajst-3251	36	28	through	through	ADP
ajst-3251	36	29	full	full	ADJ
ajst-3251	36	30	connection	connection	NOUN
ajst-3251	36	31	.	.	PUNCT
ajst-3251	37	1	a	a	DET
ajst-3251	37	2	typical	typical	ADJ
ajst-3251	37	3	four	four	NUM
ajst-3251	37	4	layer	layer	NOUN
ajst-3251	37	5	neural	neural	ADJ
ajst-3251	37	6	network	network	NOUN
ajst-3251	37	7	structure	structure	NOUN
ajst-3251	37	8	is	be	AUX
ajst-3251	37	9	shown	show	VERB
ajst-3251	37	10	in	in	ADP
ajst-3251	37	11	figure	figure	NOUN
ajst-3251	37	12	1	1	NUM
ajst-3251	37	13	.	.	PUNCT
ajst-3251	37	14	figure	figure	NOUN
ajst-3251	37	15	1	1	NUM
ajst-3251	37	16	.	.	PUNCT
ajst-3251	37	17	schematic	schematic	ADJ
ajst-3251	37	18	diagram	diagram	NOUN
ajst-3251	37	19	of	of	ADP
ajst-3251	37	20	deep	deep	ADJ
ajst-3251	37	21	neural	neural	ADJ
ajst-3251	37	22	network	network	NOUN
ajst-3251	37	23	structure	structure	NOUN
ajst-3251	37	24	after	after	SCONJ
ajst-3251	37	25	the	the	DET
ajst-3251	37	26	input	input	NOUN
ajst-3251	37	27	layer	layer	NOUN
ajst-3251	37	28	obtains	obtain	VERB
ajst-3251	37	29	the	the	DET
ajst-3251	37	30	data	datum	NOUN
ajst-3251	37	31	,	,	PUNCT
ajst-3251	37	32	there	there	PRON
ajst-3251	37	33	are	be	VERB
ajst-3251	37	34	weights	weight	NOUN
ajst-3251	37	35	w	w	NOUN
ajst-3251	37	36	and	and	CCONJ
ajst-3251	37	37	offsets	offset	VERB
ajst-3251	37	38	b	b	PROPN
ajst-3251	37	39	in	in	ADP
ajst-3251	37	40	each	each	DET
ajst-3251	37	41	layer	layer	NOUN
ajst-3251	37	42	.	.	PUNCT
ajst-3251	38	1	f	f	X
ajst-3251	38	2	(	(	PUNCT
ajst-3251	38	3	wx+b	wx+b	X
ajst-3251	38	4	)	)	PUNCT
ajst-3251	38	5	is	be	AUX
ajst-3251	38	6	calculated	calculate	VERB
ajst-3251	38	7	through	through	ADP
ajst-3251	38	8	the	the	DET
ajst-3251	38	9	activation	activation	NOUN
ajst-3251	38	10	function	function	NOUN
ajst-3251	38	11	and	and	CCONJ
ajst-3251	38	12	input	input	NOUN
ajst-3251	38	13	to	to	ADP
ajst-3251	38	14	the	the	DET
ajst-3251	38	15	next	next	ADJ
ajst-3251	38	16	layer	layer	NOUN
ajst-3251	38	17	.	.	PUNCT
ajst-3251	39	1	during	during	ADP
ajst-3251	39	2	each	each	DET
ajst-3251	39	3	training	training	NOUN
ajst-3251	39	4	,	,	PUNCT
ajst-3251	39	5	mlp	mlp	PROPN
ajst-3251	39	6	chooses	choose	VERB
ajst-3251	39	7	to	to	PART
ajst-3251	39	8	use	use	VERB
ajst-3251	39	9	adam	adam	PROPN
ajst-3251	39	10	algorithm	algorithm	PROPN
ajst-3251	39	11	to	to	PART
ajst-3251	39	12	optimize	optimize	VERB
ajst-3251	39	13	w	w	PROPN
ajst-3251	39	14	and	and	CCONJ
ajst-3251	39	15	b	b	NOUN
ajst-3251	39	16	to	to	PART
ajst-3251	39	17	meet	meet	VERB
ajst-3251	39	18	all	all	DET
ajst-3251	39	19	training	training	NOUN
ajst-3251	39	20	data	datum	NOUN
ajst-3251	39	21	as	as	ADV
ajst-3251	39	22	much	much	ADV
ajst-3251	39	23	as	as	ADP
ajst-3251	39	24	possible	possible	ADJ
ajst-3251	39	25	.	.	PUNCT
ajst-3251	40	1	the	the	DET
ajst-3251	40	2	initial	initial	ADJ
ajst-3251	40	3	w	w	NOUN
ajst-3251	40	4	and	and	CCONJ
ajst-3251	40	5	b	b	NOUN
ajst-3251	40	6	are	be	AUX
ajst-3251	40	7	randomly	randomly	ADV
ajst-3251	40	8	generated	generate	VERB
ajst-3251	40	9	groups	group	NOUN
ajst-3251	40	10	.	.	PUNCT
ajst-3251	41	1	as	as	SCONJ
ajst-3251	41	2	shown	show	VERB
ajst-3251	41	3	in	in	ADP
ajst-3251	41	4	figure	figure	NOUN
ajst-3251	41	5	1	1	NUM
ajst-3251	41	6	,	,	PUNCT
ajst-3251	41	7	the	the	DET
ajst-3251	41	8	output	output	NOUN
ajst-3251	41	9	result	result	NOUN
ajst-3251	41	10	of	of	ADP
ajst-3251	41	11	the	the	DET
ajst-3251	41	12	four	four	NUM
ajst-3251	41	13	layer	layer	NOUN
ajst-3251	41	14	perceptron	perceptron	NOUN
ajst-3251	41	15	is	be	AUX
ajst-3251	41	16	the	the	DET
ajst-3251	41	17	application	application	NOUN
ajst-3251	41	18	paradigm	paradigm	NOUN
ajst-3251	41	19	of	of	ADP
ajst-3251	41	20	deep	deep	ADJ
ajst-3251	41	21	learning	learning	NOUN
ajst-3251	41	22	usually	usually	ADV
ajst-3251	41	23	includes	include	VERB
ajst-3251	41	24	three	three	NUM
ajst-3251	41	25	parts	part	NOUN
ajst-3251	41	26	:	:	PUNCT
ajst-3251	41	27	data	datum	NOUN
ajst-3251	41	28	collection	collection	NOUN
ajst-3251	41	29	and	and	CCONJ
ajst-3251	41	30	preprocessing	preprocessing	NOUN
ajst-3251	41	31	,	,	PUNCT
ajst-3251	41	32	model	model	NOUN
ajst-3251	41	33	training	training	NOUN
ajst-3251	41	34	,	,	PUNCT
ajst-3251	41	35	testing	testing	NOUN
ajst-3251	41	36	and	and	CCONJ
ajst-3251	41	37	verification	verification	NOUN
ajst-3251	41	38	,	,	PUNCT
ajst-3251	41	39	and	and	CCONJ
ajst-3251	41	40	model	model	NOUN
ajst-3251	41	41	deployment	deployment	NOUN
ajst-3251	41	42	and	and	CCONJ
ajst-3251	41	43	application	application	NOUN
ajst-3251	41	44	.	.	PUNCT
ajst-3251	42	1	although	although	SCONJ
ajst-3251	42	2	bp	bp	PROPN
ajst-3251	42	3	neural	neural	ADJ
ajst-3251	42	4	network	network	NOUN
ajst-3251	42	5	with	with	ADP
ajst-3251	42	6	single	single	ADJ
ajst-3251	42	7	hidden	hide	VERB
ajst-3251	42	8	layer	layer	NOUN
ajst-3251	42	9	is	be	AUX
ajst-3251	42	10	widely	widely	ADV
ajst-3251	42	11	used	use	VERB
ajst-3251	42	12	,	,	PUNCT
ajst-3251	42	13	it	it	PRON
ajst-3251	42	14	is	be	AUX
ajst-3251	42	15	difficult	difficult	ADJ
ajst-3251	42	16	to	to	PART
ajst-3251	42	17	ensure	ensure	VERB
ajst-3251	42	18	the	the	DET
ajst-3251	42	19	effect	effect	NOUN
ajst-3251	42	20	of	of	ADP
ajst-3251	42	21	feature	feature	NOUN
ajst-3251	42	22	extraction	extraction	NOUN
ajst-3251	42	23	when	when	SCONJ
ajst-3251	42	24	there	there	PRON
ajst-3251	42	25	are	be	VERB
ajst-3251	42	26	many	many	ADJ
ajst-3251	42	27	input	input	NOUN
ajst-3251	42	28	parameters	parameter	NOUN
ajst-3251	42	29	.	.	PUNCT
ajst-3251	43	1	therefore	therefore	ADV
ajst-3251	43	2	,	,	PUNCT
ajst-3251	43	3	for	for	ADP
ajst-3251	43	4	complex	complex	ADJ
ajst-3251	43	5	drilling	drilling	NOUN
ajst-3251	43	6	real	real	ADJ
ajst-3251	43	7	-	-	PUNCT
ajst-3251	43	8	time	time	NOUN
ajst-3251	43	9	data	datum	NOUN
ajst-3251	43	10	,	,	PUNCT
ajst-3251	43	11	this	this	DET
ajst-3251	43	12	paper	paper	NOUN
ajst-3251	43	13	selects	select	VERB
ajst-3251	43	14	the	the	DET
ajst-3251	43	15	depth	depth	NOUN
ajst-3251	43	16	neural	neural	ADJ
ajst-3251	43	17	network	network	NOUN
ajst-3251	43	18	model	model	NOUN
ajst-3251	43	19	(	(	PUNCT
ajst-3251	43	20	dnn	dnn	PROPN
ajst-3251	43	21	)	)	PUNCT
ajst-3251	43	22	to	to	PART
ajst-3251	43	23	study	study	VERB
ajst-3251	43	24	data	datum	NOUN
ajst-3251	43	25	set	set	VERB
ajst-3251	43	26	automatic	automatic	ADJ
ajst-3251	43	27	processing	processing	NOUN
ajst-3251	43	28	and	and	CCONJ
ajst-3251	43	29	mechanical	mechanical	ADJ
ajst-3251	43	30	rate	rate	NOUN
ajst-3251	43	31	of	of	ADP
ajst-3251	43	32	penetration	penetration	NOUN
ajst-3251	43	33	(	(	PUNCT
ajst-3251	43	34	rop	rop	NOUN
ajst-3251	43	35	)	)	PUNCT
ajst-3251	43	36	depth	depth	NOUN
ajst-3251	43	37	learning	learning	NOUN
ajst-3251	43	38	model	model	NOUN
ajst-3251	43	39	training	training	NOUN
ajst-3251	43	40	,	,	PUNCT
ajst-3251	43	41	and	and	CCONJ
ajst-3251	43	42	optimize	optimize	VERB
ajst-3251	43	43	the	the	DET
ajst-3251	43	44	model	model	NOUN
ajst-3251	43	45	structure	structure	NOUN
ajst-3251	43	46	.	.	PUNCT
ajst-3251	44	1	3	3	X
ajst-3251	44	2	.	.	X
ajst-3251	44	3	modeling	modeling	NOUN
ajst-3251	44	4	method	method	NOUN
ajst-3251	44	5	3.1	3.1	NUM
ajst-3251	44	6	.	.	PUNCT
ajst-3251	45	1	real	real	ADJ
ajst-3251	45	2	time	time	NOUN
ajst-3251	45	3	drilling	drill	VERB
ajst-3251	45	4	data	datum	NOUN
ajst-3251	45	5	processing	process	VERB
ajst-3251	45	6	the	the	DET
ajst-3251	45	7	training	training	NOUN
ajst-3251	45	8	of	of	ADP
ajst-3251	45	9	neural	neural	ADJ
ajst-3251	45	10	network	network	NOUN
ajst-3251	45	11	needs	need	VERB
ajst-3251	45	12	regular	regular	ADJ
ajst-3251	45	13	and	and	CCONJ
ajst-3251	45	14	highquality	highquality	NOUN
ajst-3251	45	15	data	datum	NOUN
ajst-3251	45	16	,	,	PUNCT
ajst-3251	45	17	so	so	SCONJ
ajst-3251	45	18	data	data	NOUN
ajst-3251	45	19	preprocessing	preprocessing	NOUN
ajst-3251	45	20	is	be	AUX
ajst-3251	45	21	required	require	VERB
ajst-3251	45	22	before	before	ADP
ajst-3251	45	23	model	model	NOUN
ajst-3251	45	24	training	training	NOUN
ajst-3251	45	25	,	,	PUNCT
ajst-3251	45	26	including	include	VERB
ajst-3251	45	27	data	datum	NOUN
ajst-3251	45	28	cleaning	cleaning	NOUN
ajst-3251	45	29	and	and	CCONJ
ajst-3251	45	30	data	data	NOUN
ajst-3251	45	31	format	format	NOUN
ajst-3251	45	32	processing	processing	NOUN
ajst-3251	45	33	.	.	PUNCT
ajst-3251	46	1	the	the	DET
ajst-3251	46	2	data	datum	NOUN
ajst-3251	46	3	in	in	ADP
ajst-3251	46	4	this	this	DET
ajst-3251	46	5	paper	paper	NOUN
ajst-3251	46	6	is	be	AUX
ajst-3251	46	7	directly	directly	ADV
ajst-3251	46	8	read	read	VERB
ajst-3251	46	9	from	from	ADP
ajst-3251	46	10	the	the	DET
ajst-3251	46	11	real	real	ADJ
ajst-3251	46	12	-	-	PUNCT
ajst-3251	46	13	time	time	NOUN
ajst-3251	46	14	drilling	drilling	NOUN
ajst-3251	46	15	data	datum	NOUN
ajst-3251	46	16	of	of	ADP
ajst-3251	46	17	an	an	DET
ajst-3251	46	18	offshore	offshore	ADJ
ajst-3251	46	19	oilfield	oilfield	NOUN
ajst-3251	46	20	in	in	ADP
ajst-3251	46	21	a	a	DET
ajst-3251	46	22	certain	certain	ADJ
ajst-3251	46	23	area	area	NOUN
ajst-3251	46	24	,	,	PUNCT
ajst-3251	46	25	that	that	ADV
ajst-3251	46	26	is	is	ADV
ajst-3251	46	27	,	,	PUNCT
ajst-3251	46	28	the	the	DET
ajst-3251	46	29	las	las	NOUN
ajst-3251	46	30	file	file	NOUN
ajst-3251	46	31	of	of	ADP
ajst-3251	46	32	its	its	PRON
ajst-3251	46	33	well	well	NOUN
ajst-3251	46	34	.	.	PUNCT
ajst-3251	47	1	the	the	DET
ajst-3251	47	2	las	las	PROPN
ajst-3251	47	3	file	file	NOUN
ajst-3251	47	4	is	be	AUX
ajst-3251	47	5	a	a	DET
ajst-3251	47	6	data	data	NOUN
ajst-3251	47	7	file	file	NOUN
ajst-3251	47	8	that	that	PRON
ajst-3251	47	9	is	be	AUX
ajst-3251	47	10	recorded	record	VERB
ajst-3251	47	11	and	and	CCONJ
ajst-3251	47	12	processed	process	VERB
ajst-3251	47	13	by	by	ADP
ajst-3251	47	14	the	the	DET
ajst-3251	47	15	data	data	NOUN
ajst-3251	47	16	feedback	feedback	NOUN
ajst-3251	47	17	of	of	ADP
ajst-3251	47	18	real	real	ADJ
ajst-3251	47	19	-	-	PUNCT
ajst-3251	47	20	time	time	NOUN
ajst-3251	47	21	measurement	measurement	NOUN
ajst-3251	47	22	of	of	ADP
ajst-3251	47	23	downhole	downhole	NOUN
ajst-3251	47	24	sensors	sensor	NOUN
ajst-3251	47	25	and	and	CCONJ
ajst-3251	47	26	the	the	DET
ajst-3251	47	27	ground	ground	NOUN
ajst-3251	47	28	computer	computer	NOUN
ajst-3251	47	29	during	during	ADP
ajst-3251	47	30	the	the	DET
ajst-3251	47	31	drilling	drilling	NOUN
ajst-3251	47	32	process	process	NOUN
ajst-3251	47	33	.	.	PUNCT
ajst-3251	48	1	it	it	PRON
ajst-3251	48	2	records	record	VERB
ajst-3251	48	3	144	144	NUM
ajst-3251	48	4	parameters	parameter	NOUN
ajst-3251	48	5	of	of	ADP
ajst-3251	48	6	real	real	ADJ
ajst-3251	48	7	-	-	PUNCT
ajst-3251	48	8	time	time	NOUN
ajst-3251	48	9	drilling	drilling	NOUN
ajst-3251	48	10	at	at	ADP
ajst-3251	48	11	different	different	ADJ
ajst-3251	48	12	sampling	sample	VERB
ajst-3251	48	13	depths	depth	NOUN
ajst-3251	48	14	,	,	PUNCT
ajst-3251	48	15	including	include	VERB
ajst-3251	48	16	zero	zero	NUM
ajst-3251	48	17	data	datum	NOUN
ajst-3251	48	18	and	and	CCONJ
ajst-3251	48	19	unused	unused	ADJ
ajst-3251	48	20	parameters	parameter	NOUN
ajst-3251	48	21	.	.	PUNCT
ajst-3251	49	1	there	there	PRON
ajst-3251	49	2	are	be	VERB
ajst-3251	49	3	29610	29610	NUM
ajst-3251	49	4	pieces	piece	NOUN
ajst-3251	49	5	of	of	ADP
ajst-3251	49	6	data	datum	NOUN
ajst-3251	49	7	in	in	ADP
ajst-3251	49	8	this	this	DET
ajst-3251	49	9	las	las	NOUN
ajst-3251	49	10	file	file	NOUN
ajst-3251	49	11	.	.	PUNCT
ajst-3251	50	1	after	after	SCONJ
ajst-3251	50	2	the	the	DET
ajst-3251	50	3	data	data	NOUN
ajst-3251	50	4	is	be	AUX
ajst-3251	50	5	accessed	access	VERB
ajst-3251	50	6	,	,	PUNCT
ajst-3251	50	7	only	only	ADV
ajst-3251	50	8	15	15	NUM
ajst-3251	50	9	key	key	ADJ
ajst-3251	50	10	parameters	parameter	NOUN
ajst-3251	50	11	are	be	AUX
ajst-3251	50	12	retained	retain	VERB
ajst-3251	50	13	according	accord	VERB
ajst-3251	50	14	to	to	ADP
ajst-3251	50	15	the	the	DET
ajst-3251	50	16	traditional	traditional	ADJ
ajst-3251	50	17	rop	rop	NOUN
ajst-3251	50	18	calculation	calculation	NOUN
ajst-3251	50	19	formula	formula	NOUN
ajst-3251	50	20	,	,	PUNCT
ajst-3251	50	21	as	as	SCONJ
ajst-3251	50	22	shown	show	VERB
ajst-3251	50	23	in	in	ADP
ajst-3251	50	24	the	the	DET
ajst-3251	50	25	table	table	NOUN
ajst-3251	50	26	below	below	ADV
ajst-3251	50	27	:	:	PUNCT
ajst-3251	50	28	table	table	NOUN
ajst-3251	50	29	1	1	NUM
ajst-3251	50	30	.	.	PUNCT
ajst-3251	50	31	key	key	ADJ
ajst-3251	50	32	parameters	parameter	NOUN
ajst-3251	50	33	affecting	affect	VERB
ajst-3251	50	34	rop	rop	PROPN
ajst-3251	50	35	parameters	parameter	NOUN
ajst-3251	50	36	full	full	ADJ
ajst-3251	50	37	name	name	NOUN
ajst-3251	50	38	unit	unit	NOUN
ajst-3251	50	39	depth	depth	NOUN
ajst-3251	50	40	depth	depth	NOUN
ajst-3251	50	41	m	m	AUX
ajst-3251	50	42	woba	woba	VERB
ajst-3251	50	43	weight	weight	NOUN
ajst-3251	50	44	on	on	ADP
ajst-3251	50	45	bit	bit	NOUN
ajst-3251	50	46	tonne	tonne	NOUN
ajst-3251	50	47	hkla	hkla	NOUN
ajst-3251	50	48	weight	weight	NOUN
ajst-3251	50	49	on	on	ADP
ajst-3251	50	50	hook	hook	NOUN
ajst-3251	50	51	tonne	tonne	NOUN
ajst-3251	50	52	rpma	rpma	NOUN
ajst-3251	50	53	rotation	rotation	NOUN
ajst-3251	50	54	per	per	ADP
ajst-3251	50	55	minute	minute	NOUN
ajst-3251	50	56	1	1	NUM
ajst-3251	50	57	/	/	SYM
ajst-3251	50	58	min	min	NOUN
ajst-3251	50	59	tqa	tqa	NOUN
ajst-3251	50	60	torque	torque	NOUN
ajst-3251	50	61	kn.m	kn.m	PROPN
ajst-3251	50	62	sppa	sppa	PROPN
ajst-3251	50	63	standpipe	standpipe	PROPN
ajst-3251	50	64	pressure	pressure	NOUN
ajst-3251	50	65	psi	psi	NOUN
ajst-3251	50	66	mdia	mdia	NOUN
ajst-3251	50	67	mud	mud	NOUN
ajst-3251	50	68	density	density	NOUN
ajst-3251	50	69	in	in	ADP
ajst-3251	50	70	g	g	PROPN
ajst-3251	50	71	/	/	SYM
ajst-3251	50	72	cm3	cm3	NOUN
ajst-3251	50	73	mfoa	mfoa	NOUN
ajst-3251	50	74	mud	mud	NOUN
ajst-3251	50	75	flow	flow	VERB
ajst-3251	50	76	out	out	ADP
ajst-3251	50	77	l	l	NOUN
ajst-3251	50	78	/	/	SYM
ajst-3251	50	79	min	min	NOUN
ajst-3251	50	80	bdti	bdti	VERB
ajst-3251	50	81	bit	bit	NOUN
ajst-3251	50	82	drilled	drill	VERB
ajst-3251	50	83	time	time	NOUN
ajst-3251	50	84	h	h	PROPN
ajst-3251	50	85	bddi	bddi	NOUN
ajst-3251	50	86	bit	bit	NOUN
ajst-3251	50	87	drilled	drill	VERB
ajst-3251	50	88	distance	distance	NOUN
ajst-3251	50	89	m	m	NOUN
ajst-3251	50	90	mfia	mfia	NOUN
ajst-3251	50	91	mud	mud	NOUN
ajst-3251	50	92	flow	flow	NOUN
ajst-3251	50	93	in	in	ADP
ajst-3251	50	94	l	l	PROPN
ajst-3251	50	95	/	/	SYM
ajst-3251	50	96	min	min	PROPN
ajst-3251	50	97	tva	tva	PROPN
ajst-3251	50	98	tank	tank	NOUN
ajst-3251	50	99	volume	volume	NOUN
ajst-3251	50	100	m3	m3	PROPN
ajst-3251	50	101	ecdt	ecdt	PROPN
ajst-3251	50	102	ecd	ecd	PROPN
ajst-3251	50	103	at	at	ADP
ajst-3251	50	104	total	total	ADJ
ajst-3251	50	105	depth	depth	NOUN
ajst-3251	50	106	g	g	NOUN
ajst-3251	50	107	/	/	SYM
ajst-3251	50	108	cm3	cm3	NOUN
ajst-3251	50	109	rop	rop	NOUN
ajst-3251	50	110	rate	rate	NOUN
ajst-3251	50	111	of	of	ADP
ajst-3251	50	112	penetration	penetration	NOUN
ajst-3251	50	113	m	m	NOUN
ajst-3251	50	114	/	/	SYM
ajst-3251	50	115	h	h	NOUN
ajst-3251	50	116	after	after	SCONJ
ajst-3251	50	117	all	all	DET
ajst-3251	50	118	the	the	DET
ajst-3251	50	119	data	datum	NOUN
ajst-3251	50	120	are	be	AUX
ajst-3251	50	121	read	read	VERB
ajst-3251	50	122	in	in	ADP
ajst-3251	50	123	,	,	PUNCT
ajst-3251	50	124	the	the	DET
ajst-3251	50	125	data	datum	NOUN
ajst-3251	50	126	is	be	AUX
ajst-3251	50	127	cleaned	clean	VERB
ajst-3251	50	128	according	accord	VERB
ajst-3251	50	129	to	to	ADP
ajst-3251	50	130	the	the	DET
ajst-3251	50	131	following	follow	VERB
ajst-3251	50	132	three	three	NUM
ajst-3251	50	133	rules	rule	NOUN
ajst-3251	50	134	:	:	PUNCT
ajst-3251	50	135	1	1	X
ajst-3251	50	136	.	.	X
ajst-3251	50	137	if	if	SCONJ
ajst-3251	50	138	the	the	DET
ajst-3251	50	139	drilling	drilling	NOUN
ajst-3251	50	140	speed	speed	NOUN
ajst-3251	50	141	in	in	ADP
ajst-3251	50	142	the	the	DET
ajst-3251	50	143	sampling	sample	VERB
ajst-3251	50	144	data	datum	NOUN
ajst-3251	50	145	of	of	ADP
ajst-3251	50	146	a	a	DET
ajst-3251	50	147	certain	certain	ADJ
ajst-3251	50	148	depth	depth	NOUN
ajst-3251	50	149	is	be	AUX
ajst-3251	50	150	less	less	ADJ
ajst-3251	50	151	than	than	ADP
ajst-3251	50	152	or	or	CCONJ
ajst-3251	50	153	equal	equal	ADJ
ajst-3251	50	154	to	to	ADP
ajst-3251	50	155	0	0	NUM
ajst-3251	50	156	,	,	PUNCT
ajst-3251	50	157	the	the	DET
ajst-3251	50	158	data	datum	NOUN
ajst-3251	50	159	of	of	ADP
ajst-3251	50	160	that	that	DET
ajst-3251	50	161	depth	depth	NOUN
ajst-3251	50	162	shall	shall	AUX
ajst-3251	50	163	be	be	AUX
ajst-3251	50	164	eliminated	eliminate	VERB
ajst-3251	50	165	.	.	PUNCT
ajst-3251	51	1	2	2	X
ajst-3251	51	2	.	.	X
ajst-3251	51	3	if	if	SCONJ
ajst-3251	51	4	the	the	DET
ajst-3251	51	5	number	number	NOUN
ajst-3251	51	6	of	of	ADP
ajst-3251	51	7	0	0	NUM
ajst-3251	51	8	data	datum	NOUN
ajst-3251	51	9	under	under	ADP
ajst-3251	51	10	a	a	DET
ajst-3251	51	11	parameter	parameter	NOUN
ajst-3251	51	12	accounts	account	NOUN
ajst-3251	51	13	for	for	ADP
ajst-3251	51	14	80	80	NUM
ajst-3251	51	15	%	%	NOUN
ajst-3251	51	16	of	of	ADP
ajst-3251	51	17	the	the	DET
ajst-3251	51	18	total	total	ADJ
ajst-3251	51	19	data	datum	NOUN
ajst-3251	51	20	of	of	ADP
ajst-3251	51	21	the	the	DET
ajst-3251	51	22	parameter	parameter	NOUN
ajst-3251	51	23	,	,	PUNCT
ajst-3251	51	24	remove	remove	VERB
ajst-3251	51	25	this	this	DET
ajst-3251	51	26	parameter	parameter	NOUN
ajst-3251	51	27	.	.	PUNCT
ajst-3251	52	1	3	3	X
ajst-3251	52	2	.	.	X
ajst-3251	52	3	if	if	SCONJ
ajst-3251	52	4	all	all	DET
ajst-3251	52	5	parameters	parameter	NOUN
ajst-3251	52	6	in	in	ADP
ajst-3251	52	7	the	the	DET
ajst-3251	52	8	data	datum	NOUN
ajst-3251	52	9	sampled	sample	VERB
ajst-3251	52	10	at	at	ADP
ajst-3251	52	11	a	a	DET
ajst-3251	52	12	certain	certain	ADJ
ajst-3251	52	13	depth	depth	NOUN
ajst-3251	52	14	are	be	AUX
ajst-3251	52	15	0	0	NUM
ajst-3251	52	16	,	,	PUNCT
ajst-3251	52	17	the	the	DET
ajst-3251	52	18	data	datum	NOUN
ajst-3251	52	19	at	at	ADP
ajst-3251	52	20	that	that	DET
ajst-3251	52	21	depth	depth	NOUN
ajst-3251	52	22	will	will	AUX
ajst-3251	52	23	be	be	AUX
ajst-3251	52	24	rejected	reject	VERB
ajst-3251	52	25	.	.	PUNCT
ajst-3251	53	1	after	after	ADP
ajst-3251	53	2	data	data	NOUN
ajst-3251	53	3	preprocessing	preprocessing	NOUN
ajst-3251	53	4	,	,	PUNCT
ajst-3251	53	5	there	there	PRON
ajst-3251	53	6	are	be	VERB
ajst-3251	53	7	25224	25224	NUM
ajst-3251	53	8	pieces	piece	NOUN
ajst-3251	53	9	of	of	ADP
ajst-3251	53	10	data	datum	NOUN
ajst-3251	53	11	,	,	PUNCT
ajst-3251	53	12	with	with	ADP
ajst-3251	53	13	a	a	DET
ajst-3251	53	14	data	data	NOUN
ajst-3251	53	15	size	size	NOUN
ajst-3251	53	16	of	of	ADP
ajst-3251	53	17	25224	25224	NUM
ajst-3251	53	18	*	*	SYM
ajst-3251	53	19	12	12	NUM
ajst-3251	53	20	.	.	PUNCT
ajst-3251	54	1	data	datum	NOUN
ajst-3251	54	2	normalization	normalization	NOUN
ajst-3251	54	3	was	be	AUX
ajst-3251	54	4	not	not	PART
ajst-3251	54	5	used	use	VERB
ajst-3251	54	6	in	in	ADP
ajst-3251	54	7	this	this	DET
ajst-3251	54	8	experiment	experiment	NOUN
ajst-3251	54	9	because	because	SCONJ
ajst-3251	54	10	the	the	DET
ajst-3251	54	11	difference	difference	NOUN
ajst-3251	54	12	in	in	ADP
ajst-3251	54	13	data	datum	NOUN
ajst-3251	54	14	magnitude	magnitude	NOUN
ajst-3251	54	15	was	be	AUX
ajst-3251	54	16	not	not	PART
ajst-3251	54	17	very	very	ADV
ajst-3251	54	18	large	large	ADJ
ajst-3251	54	19	.	.	PUNCT
ajst-3251	55	1	for	for	ADP
ajst-3251	55	2	the	the	DET
ajst-3251	55	3	processed	process	VERB
ajst-3251	55	4	dataset	dataset	NOUN
ajst-3251	55	5	,	,	PUNCT
ajst-3251	55	6	90	90	NUM
ajst-3251	55	7	%	%	NOUN
ajst-3251	55	8	of	of	ADP
ajst-3251	55	9	the	the	DET
ajst-3251	55	10	training	training	NOUN
ajst-3251	55	11	data	datum	NOUN
ajst-3251	55	12	and	and	CCONJ
ajst-3251	55	13	10	10	NUM
ajst-3251	55	14	%	%	NOUN
ajst-3251	55	15	of	of	ADP
ajst-3251	55	16	the	the	DET
ajst-3251	55	17	validation	validation	NOUN
ajst-3251	55	18	data	datum	NOUN
ajst-3251	55	19	are	be	AUX
ajst-3251	55	20	allocated	allocate	VERB
ajst-3251	55	21	.	.	PUNCT
ajst-3251	56	1	some	some	DET
ajst-3251	56	2	data	datum	NOUN
ajst-3251	56	3	are	be	AUX
ajst-3251	56	4	shown	show	VERB
ajst-3251	56	5	in	in	ADP
ajst-3251	56	6	the	the	DET
ajst-3251	56	7	figure	figure	NOUN
ajst-3251	56	8	below	below	ADP
ajst-3251	56	9	:	:	PUNCT
ajst-3251	56	10	51	51	NUM
ajst-3251	56	11	table	table	NOUN
ajst-3251	56	12	2	2	NUM
ajst-3251	56	13	.	.	X
ajst-3251	56	14	partial	partial	ADJ
ajst-3251	56	15	data	datum	NOUN
ajst-3251	56	16	display	display	VERB
ajst-3251	56	17	3.2	3.2	NUM
ajst-3251	56	18	.	.	PUNCT
ajst-3251	57	1	construction	construction	NOUN
ajst-3251	57	2	of	of	ADP
ajst-3251	57	3	deep	deep	ADJ
ajst-3251	57	4	learning	learning	NOUN
ajst-3251	57	5	model	model	NOUN
ajst-3251	57	6	in	in	ADP
ajst-3251	57	7	all	all	DET
ajst-3251	57	8	the	the	DET
ajst-3251	57	9	descriptions	description	NOUN
ajst-3251	57	10	in	in	ADP
ajst-3251	57	11	this	this	DET
ajst-3251	57	12	article	article	NOUN
ajst-3251	57	13	,	,	PUNCT
ajst-3251	57	14	the	the	DET
ajst-3251	57	15	model	model	NOUN
ajst-3251	57	16	depth	depth	NOUN
ajst-3251	57	17	does	do	AUX
ajst-3251	57	18	not	not	PART
ajst-3251	57	19	include	include	VERB
ajst-3251	57	20	the	the	DET
ajst-3251	57	21	input	input	NOUN
ajst-3251	57	22	layer	layer	NOUN
ajst-3251	57	23	.	.	PUNCT
ajst-3251	58	1	the	the	DET
ajst-3251	58	2	model	model	NOUN
ajst-3251	58	3	of	of	ADP
ajst-3251	58	4	deep	deep	ADJ
ajst-3251	58	5	learning	learning	NOUN
ajst-3251	58	6	includes	include	VERB
ajst-3251	58	7	input	input	NOUN
ajst-3251	58	8	layer	layer	NOUN
ajst-3251	58	9	,	,	PUNCT
ajst-3251	58	10	hidden	hide	VERB
ajst-3251	58	11	layer	layer	NOUN
ajst-3251	58	12	and	and	CCONJ
ajst-3251	58	13	output	output	NOUN
ajst-3251	58	14	layer	layer	NOUN
ajst-3251	58	15	.	.	PUNCT
ajst-3251	59	1	according	accord	VERB
ajst-3251	59	2	to	to	ADP
ajst-3251	59	3	the	the	DET
ajst-3251	59	4	actual	actual	ADJ
ajst-3251	59	5	requirements	requirement	NOUN
ajst-3251	59	6	of	of	ADP
ajst-3251	59	7	this	this	DET
ajst-3251	59	8	paper	paper	NOUN
ajst-3251	59	9	,	,	PUNCT
ajst-3251	59	10	the	the	DET
ajst-3251	59	11	number	number	NOUN
ajst-3251	59	12	of	of	ADP
ajst-3251	59	13	input	input	NOUN
ajst-3251	59	14	parameters	parameter	NOUN
ajst-3251	59	15	is	be	AUX
ajst-3251	59	16	12	12	NUM
ajst-3251	59	17	,	,	PUNCT
ajst-3251	59	18	and	and	CCONJ
ajst-3251	59	19	the	the	DET
ajst-3251	59	20	prediction	prediction	NOUN
ajst-3251	59	21	result	result	NOUN
ajst-3251	59	22	is	be	AUX
ajst-3251	59	23	a	a	DET
ajst-3251	59	24	result	result	NOUN
ajst-3251	59	25	of	of	ADP
ajst-3251	59	26	drilling	drill	VERB
ajst-3251	59	27	speed	speed	NOUN
ajst-3251	59	28	.	.	PUNCT
ajst-3251	60	1	therefore	therefore	ADV
ajst-3251	60	2	,	,	PUNCT
ajst-3251	60	3	this	this	DET
ajst-3251	60	4	model	model	NOUN
ajst-3251	60	5	has	have	VERB
ajst-3251	60	6	12	12	NUM
ajst-3251	60	7	input	input	NOUN
ajst-3251	60	8	nodes	node	NOUN
ajst-3251	60	9	in	in	ADP
ajst-3251	60	10	the	the	DET
ajst-3251	60	11	input	input	NOUN
ajst-3251	60	12	layer	layer	NOUN
ajst-3251	60	13	and	and	CCONJ
ajst-3251	60	14	1	1	NUM
ajst-3251	60	15	node	node	NOUN
ajst-3251	60	16	in	in	ADP
ajst-3251	60	17	the	the	DET
ajst-3251	60	18	output	output	NOUN
ajst-3251	60	19	layer	layer	NOUN
ajst-3251	60	20	.	.	PUNCT
ajst-3251	61	1	according	accord	VERB
ajst-3251	61	2	to	to	ADP
ajst-3251	61	3	a	a	DET
ajst-3251	61	4	theory	theory	NOUN
ajst-3251	61	5	found	find	VERB
ajst-3251	61	6	in	in	ADP
ajst-3251	61	7	lippmann	lippmann	PROPN
ajst-3251	61	8	's	's	PART
ajst-3251	61	9	paper[11	paper[11	NOUN
ajst-3251	61	10	]	]	PUNCT
ajst-3251	61	11	,	,	PUNCT
ajst-3251	61	12	mlp	mlp	NOUN
ajst-3251	61	13	with	with	ADP
ajst-3251	61	14	two	two	NUM
ajst-3251	61	15	hidden	hidden	ADJ
ajst-3251	61	16	layers	layer	NOUN
ajst-3251	61	17	is	be	AUX
ajst-3251	61	18	enough	enough	ADJ
ajst-3251	61	19	to	to	PART
ajst-3251	61	20	see	see	VERB
ajst-3251	61	21	any	any	DET
ajst-3251	61	22	required	require	VERB
ajst-3251	61	23	shape	shape	NOUN
ajst-3251	61	24	of	of	ADP
ajst-3251	61	25	classification	classification	NOUN
ajst-3251	61	26	area	area	NOUN
ajst-3251	61	27	.	.	PUNCT
ajst-3251	62	1	further	further	ADJ
ajst-3251	62	2	research	research	NOUN
ajst-3251	62	3	shows	show	VERB
ajst-3251	62	4	that	that	SCONJ
ajst-3251	62	5	mlp	mlp	PROPN
ajst-3251	62	6	is	be	AUX
ajst-3251	62	7	a	a	DET
ajst-3251	62	8	universal	universal	ADJ
ajst-3251	62	9	pusher	pusher	NOUN
ajst-3251	62	10	.	.	PUNCT
ajst-3251	63	1	in	in	ADP
ajst-3251	63	2	the	the	DET
ajst-3251	63	3	case	case	NOUN
ajst-3251	63	4	of	of	ADP
ajst-3251	63	5	a	a	DET
ajst-3251	63	6	hidden	hide	VERB
ajst-3251	63	7	layer	layer	NOUN
ajst-3251	63	8	,	,	PUNCT
ajst-3251	63	9	as	as	ADV
ajst-3251	63	10	long	long	ADV
ajst-3251	63	11	as	as	SCONJ
ajst-3251	63	12	there	there	PRON
ajst-3251	63	13	are	be	VERB
ajst-3251	63	14	enough	enough	ADJ
ajst-3251	63	15	nodes	node	NOUN
ajst-3251	63	16	in	in	ADP
ajst-3251	63	17	the	the	DET
ajst-3251	63	18	hidden	hide	VERB
ajst-3251	63	19	layer	layer	NOUN
ajst-3251	63	20	,	,	PUNCT
ajst-3251	63	21	mlp	mlp	PROPN
ajst-3251	63	22	can	can	AUX
ajst-3251	63	23	approximate	approximate	VERB
ajst-3251	63	24	any	any	DET
ajst-3251	63	25	function	function	NOUN
ajst-3251	63	26	we	we	PRON
ajst-3251	63	27	need	need	VERB
ajst-3251	63	28	.	.	PUNCT
ajst-3251	64	1	in	in	ADP
ajst-3251	64	2	other	other	ADJ
ajst-3251	64	3	words	word	NOUN
ajst-3251	64	4	,	,	PUNCT
ajst-3251	64	5	a	a	DET
ajst-3251	64	6	two	two	NUM
ajst-3251	64	7	-	-	PUNCT
ajst-3251	64	8	layer	layer	NOUN
ajst-3251	64	9	mlp	mlp	NOUN
ajst-3251	64	10	can	can	AUX
ajst-3251	64	11	theoretically	theoretically	ADV
ajst-3251	64	12	solve	solve	VERB
ajst-3251	64	13	any	any	DET
ajst-3251	64	14	problem	problem	NOUN
ajst-3251	64	15	.	.	PUNCT
ajst-3251	65	1	in	in	ADP
ajst-3251	65	2	single	single	ADJ
ajst-3251	65	3	hidden	hide	VERB
ajst-3251	65	4	layer	layer	NOUN
ajst-3251	65	5	mlp	mlp	NOUN
ajst-3251	65	6	,	,	PUNCT
ajst-3251	65	7	the	the	DET
ajst-3251	65	8	biggest	big	ADJ
ajst-3251	65	9	problem	problem	NOUN
ajst-3251	65	10	is	be	AUX
ajst-3251	65	11	how	how	SCONJ
ajst-3251	65	12	to	to	PART
ajst-3251	65	13	configure	configure	VERB
ajst-3251	65	14	enough	enough	ADJ
ajst-3251	65	15	nodes	node	NOUN
ajst-3251	65	16	and	and	CCONJ
ajst-3251	65	17	allocate	allocate	VERB
ajst-3251	65	18	reasonable	reasonable	ADJ
ajst-3251	65	19	weights	weight	NOUN
ajst-3251	65	20	.	.	PUNCT
ajst-3251	66	1	however	however	ADV
ajst-3251	66	2	,	,	PUNCT
ajst-3251	66	3	many	many	ADJ
ajst-3251	66	4	counterexamples	counterexample	NOUN
ajst-3251	66	5	were	be	AUX
ajst-3251	66	6	found	find	VERB
ajst-3251	66	7	in	in	ADP
ajst-3251	66	8	the	the	DET
ajst-3251	66	9	subsequent	subsequent	ADJ
ajst-3251	66	10	study	study	NOUN
ajst-3251	66	11	of	of	ADP
ajst-3251	66	12	practical	practical	ADJ
ajst-3251	66	13	problems	problem	NOUN
ajst-3251	66	14	,	,	PUNCT
ajst-3251	66	15	such	such	ADJ
ajst-3251	66	16	as	as	ADP
ajst-3251	66	17	:	:	PUNCT
ajst-3251	66	18	single	single	ADJ
ajst-3251	66	19	-	-	PUNCT
ajst-3251	66	20	layer	layer	NOUN
ajst-3251	66	21	perceptron	perceptron	NOUN
ajst-3251	66	22	can	can	AUX
ajst-3251	66	23	not	not	PART
ajst-3251	66	24	solve	solve	VERB
ajst-3251	66	25	xor	xor	PROPN
ajst-3251	66	26	and	and	CCONJ
ajst-3251	66	27	nonlinear	nonlinear	ADJ
ajst-3251	66	28	problems	problem	NOUN
ajst-3251	66	29	[	[	X
ajst-3251	66	30	12	12	NUM
ajst-3251	66	31	]	]	PUNCT
ajst-3251	66	32	.	.	PUNCT
ajst-3251	67	1	some	some	DET
ajst-3251	67	2	functions	function	NOUN
ajst-3251	67	3	can	can	AUX
ajst-3251	67	4	not	not	PART
ajst-3251	67	5	be	be	AUX
ajst-3251	67	6	directly	directly	ADV
ajst-3251	67	7	learned	learn	VERB
ajst-3251	67	8	through	through	ADP
ajst-3251	67	9	the	the	DET
ajst-3251	67	10	mlp	mlp	NOUN
ajst-3251	67	11	of	of	ADP
ajst-3251	67	12	a	a	DET
ajst-3251	67	13	single	single	ADJ
ajst-3251	67	14	hidden	hidden	ADJ
ajst-3251	67	15	layer	layer	NOUN
ajst-3251	67	16	or	or	CCONJ
ajst-3251	67	17	require	require	VERB
ajst-3251	67	18	an	an	DET
ajst-3251	67	19	unlimited	unlimited	ADJ
ajst-3251	67	20	number	number	NOUN
ajst-3251	67	21	of	of	ADP
ajst-3251	67	22	nodes	node	NOUN
ajst-3251	67	23	[	[	X
ajst-3251	67	24	13	13	NUM
ajst-3251	67	25	]	]	PUNCT
ajst-3251	67	26	.	.	PUNCT
ajst-3251	68	1	moreover	moreover	ADV
ajst-3251	68	2	,	,	PUNCT
ajst-3251	68	3	for	for	ADP
ajst-3251	68	4	functions	function	NOUN
ajst-3251	68	5	that	that	PRON
ajst-3251	68	6	can	can	AUX
ajst-3251	68	7	be	be	AUX
ajst-3251	68	8	learned	learn	VERB
ajst-3251	68	9	through	through	ADP
ajst-3251	68	10	mlp	mlp	PROPN
ajst-3251	68	11	of	of	ADP
ajst-3251	68	12	a	a	DET
ajst-3251	68	13	large	large	ADJ
ajst-3251	68	14	enough	enough	ADJ
ajst-3251	68	15	single	single	ADJ
ajst-3251	68	16	hidden	hide	VERB
ajst-3251	68	17	layer	layer	NOUN
ajst-3251	68	18	,	,	PUNCT
ajst-3251	68	19	the	the	DET
ajst-3251	68	20	efficiency	efficiency	NOUN
ajst-3251	68	21	of	of	ADP
ajst-3251	68	22	a	a	DET
ajst-3251	68	23	single	single	ADJ
ajst-3251	68	24	hidden	hide	VERB
ajst-3251	68	25	layer	layer	NOUN
ajst-3251	68	26	is	be	AUX
ajst-3251	68	27	very	very	ADV
ajst-3251	68	28	low	low	ADJ
ajst-3251	68	29	,	,	PUNCT
ajst-3251	68	30	and	and	CCONJ
ajst-3251	68	31	it	it	PRON
ajst-3251	68	32	will	will	AUX
ajst-3251	68	33	be	be	AUX
ajst-3251	68	34	more	more	ADV
ajst-3251	68	35	effective	effective	ADJ
ajst-3251	68	36	to	to	PART
ajst-3251	68	37	use	use	VERB
ajst-3251	68	38	two	two	NUM
ajst-3251	68	39	or	or	CCONJ
ajst-3251	68	40	more	more	ADV
ajst-3251	68	41	hidden	hidden	ADJ
ajst-3251	68	42	layers	layer	NOUN
ajst-3251	68	43	to	to	PART
ajst-3251	68	44	learn	learn	VERB
ajst-3251	68	45	it	it	PRON
ajst-3251	68	46	[	[	X
ajst-3251	68	47	14	14	NUM
ajst-3251	68	48	]	]	PUNCT
ajst-3251	68	49	.	.	PUNCT
ajst-3251	69	1	in	in	ADP
ajst-3251	69	2	addition	addition	NOUN
ajst-3251	69	3	to	to	ADP
ajst-3251	69	4	the	the	DET
ajst-3251	69	5	normal	normal	ADJ
ajst-3251	69	6	hidden	hide	VERB
ajst-3251	69	7	layer	layer	NOUN
ajst-3251	69	8	settings	setting	NOUN
ajst-3251	69	9	,	,	PUNCT
ajst-3251	69	10	deep	deep	ADJ
ajst-3251	69	11	learning	learning	NOUN
ajst-3251	69	12	also	also	ADV
ajst-3251	69	13	includes	include	VERB
ajst-3251	69	14	a	a	DET
ajst-3251	69	15	large	large	ADJ
ajst-3251	69	16	number	number	NOUN
ajst-3251	69	17	of	of	ADP
ajst-3251	69	18	super	super	ADJ
ajst-3251	69	19	parameters	parameter	NOUN
ajst-3251	69	20	in	in	ADP
ajst-3251	69	21	the	the	DET
ajst-3251	69	22	process	process	NOUN
ajst-3251	69	23	of	of	ADP
ajst-3251	69	24	model	model	NOUN
ajst-3251	69	25	construction	construction	NOUN
ajst-3251	69	26	.	.	PUNCT
ajst-3251	70	1	hyperparameters	hyperparameter	NOUN
ajst-3251	70	2	are	be	AUX
ajst-3251	70	3	parameters	parameter	NOUN
ajst-3251	70	4	that	that	PRON
ajst-3251	70	5	do	do	AUX
ajst-3251	70	6	not	not	PART
ajst-3251	70	7	need	need	VERB
ajst-3251	70	8	data	datum	NOUN
ajst-3251	70	9	to	to	PART
ajst-3251	70	10	drive	drive	VERB
ajst-3251	70	11	,	,	PUNCT
ajst-3251	70	12	but	but	CCONJ
ajst-3251	70	13	are	be	AUX
ajst-3251	70	14	considered	consider	VERB
ajst-3251	70	15	to	to	PART
ajst-3251	70	16	be	be	AUX
ajst-3251	70	17	adjusted	adjust	VERB
ajst-3251	70	18	before	before	ADP
ajst-3251	70	19	or	or	CCONJ
ajst-3251	70	20	during	during	ADP
ajst-3251	70	21	training	training	NOUN
ajst-3251	70	22	.	.	PUNCT
ajst-3251	71	1	hyperparameters	hyperparameter	NOUN
ajst-3251	71	2	are	be	AUX
ajst-3251	71	3	usually	usually	ADV
ajst-3251	71	4	divided	divide	VERB
ajst-3251	71	5	into	into	ADP
ajst-3251	71	6	three	three	NUM
ajst-3251	71	7	categories	category	NOUN
ajst-3251	71	8	:	:	PUNCT
ajst-3251	71	9	network	network	NOUN
ajst-3251	71	10	parameters	parameter	NOUN
ajst-3251	71	11	,	,	PUNCT
ajst-3251	71	12	optimization	optimization	NOUN
ajst-3251	71	13	parameters	parameter	NOUN
ajst-3251	71	14	,	,	PUNCT
ajst-3251	71	15	and	and	CCONJ
ajst-3251	71	16	regularization	regularization	NOUN
ajst-3251	71	17	parameters	parameter	NOUN
ajst-3251	71	18	.	.	PUNCT
ajst-3251	72	1	network	network	NOUN
ajst-3251	72	2	parameters	parameter	NOUN
ajst-3251	72	3	refer	refer	VERB
ajst-3251	72	4	to	to	ADP
ajst-3251	72	5	the	the	DET
ajst-3251	72	6	interaction	interaction	NOUN
ajst-3251	72	7	mode	mode	NOUN
ajst-3251	72	8	(	(	PUNCT
ajst-3251	72	9	addition	addition	NOUN
ajst-3251	72	10	,	,	PUNCT
ajst-3251	72	11	multiplication	multiplication	NOUN
ajst-3251	72	12	or	or	CCONJ
ajst-3251	72	13	concatenation	concatenation	NOUN
ajst-3251	72	14	)	)	PUNCT
ajst-3251	72	15	between	between	ADP
ajst-3251	72	16	network	network	NOUN
ajst-3251	72	17	layers	layer	NOUN
ajst-3251	72	18	,	,	PUNCT
ajst-3251	72	19	the	the	DET
ajst-3251	72	20	number	number	NOUN
ajst-3251	72	21	and	and	CCONJ
ajst-3251	72	22	size	size	NOUN
ajst-3251	72	23	of	of	ADP
ajst-3251	72	24	convolution	convolution	NOUN
ajst-3251	72	25	cores	core	NOUN
ajst-3251	72	26	,	,	PUNCT
ajst-3251	72	27	the	the	DET
ajst-3251	72	28	number	number	NOUN
ajst-3251	72	29	of	of	ADP
ajst-3251	72	30	network	network	NOUN
ajst-3251	72	31	layers	layer	NOUN
ajst-3251	72	32	(	(	PUNCT
ajst-3251	72	33	also	also	ADV
ajst-3251	72	34	called	call	VERB
ajst-3251	72	35	depth	depth	NOUN
ajst-3251	72	36	)	)	PUNCT
ajst-3251	72	37	and	and	CCONJ
ajst-3251	72	38	activation	activation	NOUN
ajst-3251	72	39	function	function	NOUN
ajst-3251	72	40	.	.	PUNCT
ajst-3251	73	1	optimization	optimization	NOUN
ajst-3251	73	2	parameters	parameter	NOUN
ajst-3251	73	3	refer	refer	VERB
ajst-3251	73	4	to	to	ADP
ajst-3251	73	5	learning	learn	VERB
ajst-3251	73	6	rate	rate	NOUN
ajst-3251	73	7	,	,	PUNCT
ajst-3251	73	8	batch	batch	NOUN
ajst-3251	73	9	size	size	NOUN
ajst-3251	73	10	,	,	PUNCT
ajst-3251	73	11	parameters	parameter	NOUN
ajst-3251	73	12	of	of	ADP
ajst-3251	73	13	different	different	ADJ
ajst-3251	73	14	optimizers	optimizer	NOUN
ajst-3251	73	15	and	and	CCONJ
ajst-3251	73	16	adjustable	adjustable	ADJ
ajst-3251	73	17	parameters	parameter	NOUN
ajst-3251	73	18	of	of	ADP
ajst-3251	73	19	some	some	DET
ajst-3251	73	20	loss	loss	NOUN
ajst-3251	73	21	functions	function	NOUN
ajst-3251	73	22	.	.	PUNCT
ajst-3251	74	1	the	the	DET
ajst-3251	74	2	regularization	regularization	NOUN
ajst-3251	74	3	parameter	parameter	NOUN
ajst-3251	74	4	refers	refer	VERB
ajst-3251	74	5	to	to	ADP
ajst-3251	74	6	the	the	DET
ajst-3251	74	7	weight	weight	NOUN
ajst-3251	74	8	attenuation	attenuation	NOUN
ajst-3251	74	9	coefficient	coefficient	NOUN
ajst-3251	74	10	and	and	CCONJ
ajst-3251	74	11	the	the	DET
ajst-3251	74	12	drop	drop	NOUN
ajst-3251	74	13	out	out	ADP
ajst-3251	74	14	ratio	ratio	NOUN
ajst-3251	74	15	.	.	PUNCT
ajst-3251	75	1	in	in	ADP
ajst-3251	75	2	the	the	DET
ajst-3251	75	3	process	process	NOUN
ajst-3251	75	4	of	of	ADP
ajst-3251	75	5	establishing	establish	VERB
ajst-3251	75	6	the	the	DET
ajst-3251	75	7	model	model	NOUN
ajst-3251	75	8	,	,	PUNCT
ajst-3251	75	9	it	it	PRON
ajst-3251	75	10	is	be	AUX
ajst-3251	75	11	necessary	necessary	ADJ
ajst-3251	75	12	to	to	PART
ajst-3251	75	13	select	select	VERB
ajst-3251	75	14	a	a	DET
ajst-3251	75	15	reasonable	reasonable	ADJ
ajst-3251	75	16	activation	activation	NOUN
ajst-3251	75	17	function	function	NOUN
ajst-3251	75	18	.	.	PUNCT
ajst-3251	76	1	the	the	DET
ajst-3251	76	2	activation	activation	NOUN
ajst-3251	76	3	function	function	NOUN
ajst-3251	76	4	is	be	AUX
ajst-3251	76	5	used	use	VERB
ajst-3251	76	6	to	to	PART
ajst-3251	76	7	add	add	VERB
ajst-3251	76	8	nonlinear	nonlinear	ADJ
ajst-3251	76	9	features	feature	NOUN
ajst-3251	76	10	,	,	PUNCT
ajst-3251	76	11	so	so	SCONJ
ajst-3251	76	12	as	as	SCONJ
ajst-3251	76	13	to	to	PART
ajst-3251	76	14	overcome	overcome	VERB
ajst-3251	76	15	the	the	DET
ajst-3251	76	16	defect	defect	NOUN
ajst-3251	76	17	of	of	ADP
ajst-3251	76	18	insufficient	insufficient	ADJ
ajst-3251	76	19	representation	representation	NOUN
ajst-3251	76	20	ability	ability	NOUN
ajst-3251	76	21	of	of	ADP
ajst-3251	76	22	linear	linear	ADJ
ajst-3251	76	23	models	model	NOUN
ajst-3251	76	24	.	.	PUNCT
ajst-3251	77	1	in	in	ADP
ajst-3251	77	2	the	the	DET
ajst-3251	77	3	process	process	NOUN
ajst-3251	77	4	of	of	ADP
ajst-3251	77	5	establishing	establish	VERB
ajst-3251	77	6	this	this	DET
ajst-3251	77	7	model	model	NOUN
ajst-3251	77	8	,	,	PUNCT
ajst-3251	77	9	relu	relu	NOUN
ajst-3251	77	10	function	function	NOUN
ajst-3251	77	11	is	be	AUX
ajst-3251	77	12	selected	select	VERB
ajst-3251	77	13	as	as	ADP
ajst-3251	77	14	the	the	DET
ajst-3251	77	15	activation	activation	NOUN
ajst-3251	77	16	function	function	NOUN
ajst-3251	77	17	.	.	PUNCT
ajst-3251	78	1	relu	relu	NOUN
ajst-3251	78	2	function	function	NOUN
ajst-3251	78	3	does	do	AUX
ajst-3251	78	4	not	not	PART
ajst-3251	78	5	have	have	VERB
ajst-3251	78	6	the	the	DET
ajst-3251	78	7	problem	problem	NOUN
ajst-3251	78	8	of	of	ADP
ajst-3251	78	9	gradient	gradient	ADJ
ajst-3251	78	10	disappearance	disappearance	NOUN
ajst-3251	78	11	.	.	PUNCT
ajst-3251	79	1	secondly	secondly	ADV
ajst-3251	79	2	,	,	PUNCT
ajst-3251	79	3	it	it	PRON
ajst-3251	79	4	can	can	AUX
ajst-3251	79	5	maximize	maximize	VERB
ajst-3251	79	6	the	the	DET
ajst-3251	79	7	median	median	ADJ
ajst-3251	79	8	value	value	NOUN
ajst-3251	79	9	of	of	ADP
ajst-3251	79	10	each	each	DET
ajst-3251	79	11	feature	feature	NOUN
ajst-3251	79	12	and	and	CCONJ
ajst-3251	79	13	has	have	VERB
ajst-3251	79	14	fast	fast	ADJ
ajst-3251	79	15	operation	operation	NOUN
ajst-3251	79	16	speed	speed	NOUN
ajst-3251	79	17	.	.	PUNCT
ajst-3251	80	1	adam	adam	PROPN
ajst-3251	80	2	is	be	AUX
ajst-3251	80	3	selected	select	VERB
ajst-3251	80	4	as	as	ADP
ajst-3251	80	5	the	the	DET
ajst-3251	80	6	optimizer	optimizer	NOUN
ajst-3251	80	7	.	.	PUNCT
ajst-3251	81	1	the	the	DET
ajst-3251	81	2	optimizer	optimizer	NOUN
ajst-3251	81	3	can	can	AUX
ajst-3251	81	4	quickly	quickly	ADV
ajst-3251	81	5	obtain	obtain	VERB
ajst-3251	81	6	good	good	ADJ
ajst-3251	81	7	results	result	NOUN
ajst-3251	81	8	for	for	ADP
ajst-3251	81	9	dealing	deal	VERB
ajst-3251	81	10	with	with	ADP
ajst-3251	81	11	various	various	ADJ
ajst-3251	81	12	problems	problem	NOUN
ajst-3251	81	13	,	,	PUNCT
ajst-3251	81	14	because	because	SCONJ
ajst-3251	81	15	it	it	PRON
ajst-3251	81	16	uses	use	VERB
ajst-3251	81	17	the	the	DET
ajst-3251	81	18	same	same	ADJ
ajst-3251	81	19	learning	learning	NOUN
ajst-3251	81	20	rate	rate	NOUN
ajst-3251	81	21	for	for	ADP
ajst-3251	81	22	each	each	DET
ajst-3251	81	23	parameter	parameter	NOUN
ajst-3251	81	24	and	and	CCONJ
ajst-3251	81	25	adapts	adapt	VERB
ajst-3251	81	26	independently	independently	ADV
ajst-3251	81	27	with	with	ADP
ajst-3251	81	28	the	the	DET
ajst-3251	81	29	learning	learning	NOUN
ajst-3251	81	30	process	process	NOUN
ajst-3251	81	31	,	,	PUNCT
ajst-3251	81	32	and	and	CCONJ
ajst-3251	81	33	the	the	DET
ajst-3251	81	34	optimizer	optimizer	NOUN
ajst-3251	81	35	is	be	AUX
ajst-3251	81	36	very	very	ADV
ajst-3251	81	37	consistent	consistent	ADJ
ajst-3251	81	38	with	with	ADP
ajst-3251	81	39	the	the	DET
ajst-3251	81	40	deep	deep	ADJ
ajst-3251	81	41	learning	learning	NOUN
ajst-3251	81	42	algorithm	algorithm	NOUN
ajst-3251	81	43	.	.	PUNCT
ajst-3251	82	1	the	the	DET
ajst-3251	82	2	loss	loss	NOUN
ajst-3251	82	3	function	function	NOUN
ajst-3251	82	4	selects	select	VERB
ajst-3251	82	5	the	the	DET
ajst-3251	82	6	mean	mean	ADJ
ajst-3251	82	7	absolute	absolute	ADJ
ajst-3251	82	8	error	error	NOUN
ajst-3251	82	9	(	(	PUNCT
ajst-3251	82	10	mae	mae	PROPN
ajst-3251	82	11	)	)	PUNCT
ajst-3251	82	12	first	first	ADV
ajst-3251	82	13	.	.	PUNCT
ajst-3251	83	1	for	for	ADP
ajst-3251	83	2	the	the	DET
ajst-3251	83	3	optimization	optimization	NOUN
ajst-3251	83	4	parameters	parameter	NOUN
ajst-3251	83	5	such	such	ADJ
ajst-3251	83	6	as	as	ADP
ajst-3251	83	7	the	the	DET
ajst-3251	83	8	learning	learning	NOUN
ajst-3251	83	9	rate	rate	NOUN
ajst-3251	83	10	and	and	CCONJ
ajst-3251	83	11	the	the	DET
ajst-3251	83	12	number	number	NOUN
ajst-3251	83	13	of	of	ADP
ajst-3251	83	14	batch	batch	NOUN
ajst-3251	83	15	samples	sample	NOUN
ajst-3251	83	16	,	,	PUNCT
ajst-3251	83	17	the	the	DET
ajst-3251	83	18	neural	neural	ADJ
ajst-3251	83	19	network	network	NOUN
ajst-3251	83	20	will	will	AUX
ajst-3251	83	21	automatically	automatically	ADV
ajst-3251	83	22	initialize	initialize	VERB
ajst-3251	83	23	them	they	PRON
ajst-3251	83	24	to	to	ADP
ajst-3251	83	25	reasonable	reasonable	ADJ
ajst-3251	83	26	values	value	NOUN
ajst-3251	83	27	and	and	CCONJ
ajst-3251	83	28	adjust	adjust	VERB
ajst-3251	83	29	them	they	PRON
ajst-3251	83	30	according	accord	VERB
ajst-3251	83	31	to	to	ADP
ajst-3251	83	32	each	each	DET
ajst-3251	83	33	batch	batch	NOUN
ajst-3251	83	34	of	of	ADP
ajst-3251	83	35	learning	learn	VERB
ajst-3251	83	36	.	.	PUNCT
ajst-3251	84	1	among	among	ADP
ajst-3251	84	2	them	they	PRON
ajst-3251	84	3	,	,	PUNCT
ajst-3251	84	4	0	0	NUM
ajst-3251	84	5	,	,	PUNCT
ajst-3251	84	6	0	0	NUM
ajst-3251	84	7	,	,	PUNCT
ajst-3251	84	8	0	0	NUM
ajst-3251	84	9	in	in	ADP
ajst-3251	84	10	general	general	ADJ
ajst-3251	84	11	,	,	PUNCT
ajst-3251	84	12	how	how	SCONJ
ajst-3251	84	13	to	to	PART
ajst-3251	84	14	build	build	VERB
ajst-3251	84	15	a	a	DET
ajst-3251	84	16	reasonable	reasonable	ADJ
ajst-3251	84	17	deep	deep	ADJ
ajst-3251	84	18	learning	learning	NOUN
ajst-3251	84	19	model	model	NOUN
ajst-3251	84	20	has	have	AUX
ajst-3251	84	21	always	always	ADV
ajst-3251	84	22	been	be	AUX
ajst-3251	84	23	a	a	DET
ajst-3251	84	24	complex	complex	ADJ
ajst-3251	84	25	problem	problem	NOUN
ajst-3251	84	26	.	.	PUNCT
ajst-3251	85	1	in	in	ADP
ajst-3251	85	2	most	most	ADJ
ajst-3251	85	3	cases	case	NOUN
ajst-3251	85	4	,	,	PUNCT
ajst-3251	85	5	the	the	DET
ajst-3251	85	6	experience	experience	NOUN
ajst-3251	85	7	and	and	CCONJ
ajst-3251	85	8	experiments	experiment	NOUN
ajst-3251	85	9	of	of	ADP
ajst-3251	85	10	workers	worker	NOUN
ajst-3251	85	11	are	be	AUX
ajst-3251	85	12	used	use	VERB
ajst-3251	85	13	to	to	PART
ajst-3251	85	14	build	build	VERB
ajst-3251	85	15	appropriate	appropriate	ADJ
ajst-3251	85	16	deep	deep	ADJ
ajst-3251	85	17	learning	learning	NOUN
ajst-3251	85	18	models	model	NOUN
ajst-3251	85	19	for	for	ADP
ajst-3251	85	20	specific	specific	ADJ
ajst-3251	85	21	practical	practical	ADJ
ajst-3251	85	22	problems	problem	NOUN
ajst-3251	85	23	.	.	PUNCT
ajst-3251	86	1	in	in	ADP
ajst-3251	86	2	this	this	DET
ajst-3251	86	3	paper	paper	NOUN
ajst-3251	86	4	,	,	PUNCT
ajst-3251	86	5	we	we	PRON
ajst-3251	86	6	first	first	ADV
ajst-3251	86	7	establish	establish	VERB
ajst-3251	86	8	a	a	DET
ajst-3251	86	9	three	three	NUM
ajst-3251	86	10	-	-	PUNCT
ajst-3251	86	11	layer	layer	NOUN
ajst-3251	86	12	model	model	NOUN
ajst-3251	86	13	with	with	ADP
ajst-3251	86	14	32	32	NUM
ajst-3251	86	15	and	and	CCONJ
ajst-3251	86	16	16	16	NUM
ajst-3251	86	17	input	input	NOUN
ajst-3251	86	18	nodes	node	NOUN
ajst-3251	86	19	.	.	PUNCT
ajst-3251	87	1	the	the	DET
ajst-3251	87	2	r	r	NOUN
ajst-3251	87	3	^	^	SYM
ajst-3251	87	4	2	2	NUM
ajst-3251	87	5	correlation	correlation	NOUN
ajst-3251	87	6	coefficient	coefficient	NOUN
ajst-3251	87	7	is	be	AUX
ajst-3251	87	8	used	use	VERB
ajst-3251	87	9	as	as	ADP
ajst-3251	87	10	the	the	DET
ajst-3251	87	11	evaluation	evaluation	NOUN
ajst-3251	87	12	standard	standard	NOUN
ajst-3251	87	13	of	of	ADP
ajst-3251	87	14	the	the	DET
ajst-3251	87	15	model	model	NOUN
ajst-3251	87	16	,	,	PUNCT
ajst-3251	87	17	and	and	CCONJ
ajst-3251	87	18	the	the	DET
ajst-3251	87	19	training	training	NOUN
ajst-3251	87	20	batch	batch	NOUN
ajst-3251	87	21	is	be	AUX
ajst-3251	87	22	200	200	NUM
ajst-3251	87	23	times	time	NOUN
ajst-3251	87	24	.	.	PUNCT
ajst-3251	88	1	the	the	DET
ajst-3251	88	2	loss	loss	NOUN
ajst-3251	88	3	function	function	NOUN
ajst-3251	88	4	loss	loss	NOUN
ajst-3251	88	5	result	result	NOUN
ajst-3251	88	6	is	be	AUX
ajst-3251	88	7	shown	show	VERB
ajst-3251	88	8	in	in	ADP
ajst-3251	88	9	figure	figure	NOUN
ajst-3251	88	10	3	3	NUM
ajst-3251	88	11	.	.	PUNCT
ajst-3251	89	1	it	it	PRON
ajst-3251	89	2	can	can	AUX
ajst-3251	89	3	be	be	AUX
ajst-3251	89	4	seen	see	VERB
ajst-3251	89	5	from	from	ADP
ajst-3251	89	6	the	the	DET
ajst-3251	89	7	results	result	NOUN
ajst-3251	89	8	that	that	PRON
ajst-3251	89	9	the	the	DET
ajst-3251	89	10	model	model	NOUN
ajst-3251	89	11	has	have	AUX
ajst-3251	89	12	reached	reach	VERB
ajst-3251	89	13	the	the	DET
ajst-3251	89	14	convergence	convergence	NOUN
ajst-3251	89	15	state	state	NOUN
ajst-3251	89	16	in	in	ADP
ajst-3251	89	17	the	the	DET
ajst-3251	89	18	case	case	NOUN
ajst-3251	89	19	of	of	ADP
ajst-3251	89	20	200	200	NUM
ajst-3251	89	21	training	training	NOUN
ajst-3251	89	22	,	,	PUNCT
ajst-3251	89	23	and	and	CCONJ
ajst-3251	89	24	no	no	ADV
ajst-3251	89	25	over	over	ADP
ajst-3251	89	26	fitting	fitting	ADJ
ajst-3251	89	27	has	have	AUX
ajst-3251	89	28	occurred	occur	VERB
ajst-3251	89	29	.	.	PUNCT
ajst-3251	90	1	the	the	DET
ajst-3251	90	2	verification	verification	NOUN
ajst-3251	90	3	set	set	NOUN
ajst-3251	90	4	performs	perform	VERB
ajst-3251	90	5	well	well	ADV
ajst-3251	90	6	,	,	PUNCT
ajst-3251	90	7	with	with	ADP
ajst-3251	90	8	of	of	ADP
ajst-3251	90	9	0.8425	0.8425	NUM
ajst-3251	90	10	and	and	CCONJ
ajst-3251	90	11	processing	processing	NOUN
ajst-3251	90	12	time	time	NOUN
ajst-3251	90	13	of	of	ADP
ajst-3251	90	14	101	101	NUM
ajst-3251	90	15	seconds	second	NOUN
ajst-3251	90	16	.	.	PUNCT
ajst-3251	91	1	the	the	DET
ajst-3251	91	2	results	result	NOUN
ajst-3251	91	3	are	be	AUX
ajst-3251	91	4	shown	show	VERB
ajst-3251	91	5	in	in	ADP
ajst-3251	91	6	figure	figure	NOUN
ajst-3251	91	7	4	4	NUM
ajst-3251	91	8	.	.	PUNCT
ajst-3251	91	9	figure	figure	VERB
ajst-3251	91	10	3	3	NUM
ajst-3251	91	11	.	.	PUNCT
ajst-3251	91	12	loss	loss	NOUN
ajst-3251	91	13	function	function	NOUN
ajst-3251	91	14	of	of	ADP
ajst-3251	91	15	three	three	NUM
ajst-3251	91	16	-	-	PUNCT
ajst-3251	91	17	layer	layer	NOUN
ajst-3251	91	18	model	model	NOUN
ajst-3251	91	19	in	in	ADP
ajst-3251	91	20	training	training	NOUN
ajst-3251	91	21	set	set	NOUN
ajst-3251	91	22	and	and	CCONJ
ajst-3251	91	23	verification	verification	NOUN
ajst-3251	91	24	set	set	NOUN
ajst-3251	91	25	depth	depth	NOUN
ajst-3251	91	26	woba	woba	NOUN
ajst-3251	91	27	hkla	hkla	VERB
ajst-3251	91	28	rpma	rpma	PROPN
ajst-3251	91	29	tqa	tqa	PROPN
ajst-3251	91	30	sppa	sppa	PROPN
ajst-3251	91	31	mdia	mdia	PROPN
ajst-3251	91	32	mfoa	mfoa	PROPN
ajst-3251	91	33	bdti	bdti	PROPN
ajst-3251	91	34	mfia	mfia	PROPN
ajst-3251	91	35	tva	tva	PROPN
ajst-3251	91	36	ecdt	ecdt	PROPN
ajst-3251	91	37	1	1	NUM
ajst-3251	91	38	154.89	154.89	NUM
ajst-3251	91	39	1.4	1.4	NUM
ajst-3251	91	40	73.59	73.59	NUM
ajst-3251	91	41	0	0	NUM
ajst-3251	91	42	0.67	0.67	NUM
ajst-3251	91	43	3.09	3.09	NUM
ajst-3251	91	44	9.18	9.18	NUM
ajst-3251	91	45	5.44	5.44	NUM
ajst-3251	91	46	0	0	NUM
ajst-3251	91	47	4087.53	4087.53	NUM
ajst-3251	91	48	539.6	539.6	NUM
ajst-3251	91	49	1.03	1.03	NUM
ajst-3251	91	50	2	2	NUM
ajst-3251	91	51	155.04	155.04	NUM
ajst-3251	91	52	1.4	1.4	NUM
ajst-3251	91	53	73.59	73.59	NUM
ajst-3251	91	54	0	0	NUM
ajst-3251	91	55	0.69	0.69	NUM
ajst-3251	91	56	3.1	3.1	NUM
ajst-3251	91	57	9.18	9.18	NUM
ajst-3251	91	58	5.44	5.44	NUM
ajst-3251	91	59	0	0	NUM
ajst-3251	91	60	4093.71	4093.71	NUM
ajst-3251	91	61	539.52	539.52	NUM
ajst-3251	91	62	1.03	1.03	NUM
ajst-3251	91	63	3	3	NUM
ajst-3251	91	64	155.19	155.19	NUM
ajst-3251	91	65	1.2	1.2	NUM
ajst-3251	91	66	73.8	73.8	NUM
ajst-3251	91	67	0	0	NUM
ajst-3251	91	68	0.69	0.69	NUM
ajst-3251	91	69	3.08	3.08	NUM
ajst-3251	91	70	9.18	9.18	NUM
ajst-3251	91	71	5.44	5.44	NUM
ajst-3251	91	72	0	0	NUM
ajst-3251	91	73	4093.57	4093.57	NUM
ajst-3251	91	74	539.52	539.52	NUM
ajst-3251	91	75	1.03	1.03	NUM
ajst-3251	91	76	4	4	NUM
ajst-3251	91	77	155.35	155.35	NUM
ajst-3251	91	78	1.1	1.1	NUM
ajst-3251	91	79	73.88	73.88	NUM
ajst-3251	91	80	0	0	NUM
ajst-3251	91	81	0.88	0.88	NUM
ajst-3251	91	82	3.09	3.09	NUM
ajst-3251	91	83	9.18	9.18	NUM
ajst-3251	91	84	5.44	5.44	NUM
ajst-3251	91	85	0	0	NUM
ajst-3251	91	86	4093.41	4093.41	NUM
ajst-3251	91	87	539.41	539.41	NUM
ajst-3251	91	88	1.03	1.03	NUM
ajst-3251	91	89	5	5	NUM
ajst-3251	91	90	155.52	155.52	NUM
ajst-3251	91	91	1	1	NUM
ajst-3251	91	92	74.03	74.03	NUM
ajst-3251	91	93	0	0	NUM
ajst-3251	91	94	0.59	0.59	NUM
ajst-3251	91	95	3.08	3.08	NUM
ajst-3251	91	96	9.18	9.18	NUM
ajst-3251	91	97	5.44	5.44	NUM
ajst-3251	91	98	0	0	NUM
ajst-3251	91	99	4093.59	4093.59	NUM
ajst-3251	92	1	539.55	539.55	NUM
ajst-3251	92	2	1.03	1.03	NUM
ajst-3251	92	3	6	6	NUM
ajst-3251	92	4	155.68	155.68	NUM
ajst-3251	92	5	1	1	NUM
ajst-3251	92	6	74	74	NUM
ajst-3251	92	7	0	0	NUM
ajst-3251	92	8	0.67	0.67	NUM
ajst-3251	92	9	3.06	3.06	NUM
ajst-3251	92	10	9.18	9.18	NUM
ajst-3251	92	11	5.44	5.44	NUM
ajst-3251	92	12	0	0	NUM
ajst-3251	92	13	4093.84	4093.84	NUM
ajst-3251	92	14	539.63	539.63	NUM
ajst-3251	92	15	1.03	1.03	NUM
ajst-3251	92	16	7	7	NUM
ajst-3251	92	17	155.79	155.79	NUM
ajst-3251	92	18	0.2	0.2	NUM
ajst-3251	92	19	73.7	73.7	NUM
ajst-3251	92	20	10	10	NUM
ajst-3251	92	21	0.29	0.29	NUM
ajst-3251	92	22	6.35	6.35	NUM
ajst-3251	92	23	9.18	9.18	NUM
ajst-3251	92	24	5.44	5.44	NUM
ajst-3251	92	25	0	0	NUM
ajst-3251	92	26	4093.41	4093.41	NUM
ajst-3251	92	27	539.14	539.14	NUM
ajst-3251	92	28	1.03	1.03	NUM
ajst-3251	92	29	8	8	NUM
ajst-3251	92	30	155.95	155.95	NUM
ajst-3251	92	31	0.1	0.1	NUM
ajst-3251	92	32	73.75	73.75	NUM
ajst-3251	92	33	10	10	NUM
ajst-3251	92	34	0.55	0.55	NUM
ajst-3251	92	35	6.36	6.36	NUM
ajst-3251	92	36	9.18	9.18	NUM
ajst-3251	92	37	5.44	5.44	NUM
ajst-3251	92	38	0	0	NUM
ajst-3251	92	39	4093.56	4093.56	NUM
ajst-3251	92	40	538.96	538.96	NUM
ajst-3251	92	41	1.03	1.03	NUM
ajst-3251	92	42	9	9	NUM
ajst-3251	92	43	156.11	156.11	NUM
ajst-3251	92	44	0.1	0.1	NUM
ajst-3251	92	45	73.73	73.73	NUM
ajst-3251	92	46	10	10	NUM
ajst-3251	92	47	0.26	0.26	NUM
ajst-3251	92	48	6.42	6.42	NUM
ajst-3251	92	49	9.18	9.18	NUM
ajst-3251	92	50	5.45	5.45	NUM
ajst-3251	92	51	0	0	NUM
ajst-3251	92	52	4093.41	4093.41	NUM
ajst-3251	92	53	539.1	539.1	NUM
ajst-3251	92	54	1.03	1.03	NUM
ajst-3251	92	55	10	10	NUM
ajst-3251	92	56	156.25	156.25	NUM
ajst-3251	92	57	0.2	0.2	NUM
ajst-3251	92	58	73.67	73.67	NUM
ajst-3251	92	59	10	10	NUM
ajst-3251	92	60	0.25	0.25	NUM
ajst-3251	92	61	6.36	6.36	NUM
ajst-3251	92	62	9.18	9.18	NUM
ajst-3251	92	63	5.44	5.44	NUM
ajst-3251	92	64	0	0	NUM
ajst-3251	92	65	4093.41	4093.41	NUM
ajst-3251	92	66	539.02	539.02	NUM
ajst-3251	92	67	1.03	1.03	NUM
ajst-3251	92	68	52	52	NUM
ajst-3251	92	69	figure	figure	NOUN
ajst-3251	92	70	4	4	NUM
ajst-3251	92	71	.	.	NOUN
ajst-3251	92	72	comparison	comparison	NOUN
ajst-3251	92	73	between	between	ADP
ajst-3251	92	74	predicted	predict	VERB
ajst-3251	92	75	results	result	NOUN
ajst-3251	92	76	and	and	CCONJ
ajst-3251	92	77	actual	actual	ADJ
ajst-3251	92	78	results	result	NOUN
ajst-3251	92	79	3.3	3.3	NUM
ajst-3251	92	80	.	.	PUNCT
ajst-3251	93	1	structure	structure	NOUN
ajst-3251	93	2	optimization	optimization	NOUN
ajst-3251	93	3	of	of	ADP
ajst-3251	93	4	deep	deep	ADJ
ajst-3251	93	5	learning	learning	NOUN
ajst-3251	93	6	model	model	NOUN
ajst-3251	93	7	it	it	PRON
ajst-3251	93	8	can	can	AUX
ajst-3251	93	9	be	be	AUX
ajst-3251	93	10	seen	see	VERB
ajst-3251	93	11	from	from	ADP
ajst-3251	93	12	the	the	DET
ajst-3251	93	13	above	above	ADJ
ajst-3251	93	14	experiments	experiment	NOUN
ajst-3251	93	15	that	that	SCONJ
ajst-3251	93	16	the	the	DET
ajst-3251	93	17	original	original	ADJ
ajst-3251	93	18	three	three	NUM
ajst-3251	93	19	-	-	PUNCT
ajst-3251	93	20	layer	layer	NOUN
ajst-3251	93	21	model	model	NOUN
ajst-3251	93	22	can	can	AUX
ajst-3251	93	23	accurately	accurately	ADV
ajst-3251	93	24	predict	predict	VERB
ajst-3251	93	25	the	the	DET
ajst-3251	93	26	results	result	NOUN
ajst-3251	93	27	of	of	ADP
ajst-3251	93	28	drilling	drilling	NOUN
ajst-3251	93	29	speed	speed	NOUN
ajst-3251	93	30	,	,	PUNCT
ajst-3251	93	31	but	but	CCONJ
ajst-3251	93	32	there	there	PRON
ajst-3251	93	33	is	be	VERB
ajst-3251	93	34	a	a	DET
ajst-3251	93	35	lot	lot	NOUN
ajst-3251	93	36	of	of	ADP
ajst-3251	93	37	room	room	NOUN
ajst-3251	93	38	for	for	ADP
ajst-3251	93	39	optimization	optimization	NOUN
ajst-3251	93	40	in	in	ADP
ajst-3251	93	41	the	the	DET
ajst-3251	93	42	model	model	NOUN
ajst-3251	93	43	structure	structure	NOUN
ajst-3251	93	44	,	,	PUNCT
ajst-3251	93	45	so	so	ADV
ajst-3251	93	46	we	we	PRON
ajst-3251	93	47	optimized	optimize	VERB
ajst-3251	93	48	the	the	DET
ajst-3251	93	49	model	model	NOUN
ajst-3251	93	50	structure	structure	NOUN
ajst-3251	93	51	to	to	PART
ajst-3251	93	52	ensure	ensure	VERB
ajst-3251	93	53	the	the	DET
ajst-3251	93	54	best	well	ADV
ajst-3251	93	55	fitting	fitting	ADJ
ajst-3251	93	56	effect	effect	NOUN
ajst-3251	93	57	.	.	PUNCT
ajst-3251	94	1	generally	generally	ADV
ajst-3251	94	2	,	,	PUNCT
ajst-3251	94	3	if	if	SCONJ
ajst-3251	94	4	the	the	DET
ajst-3251	94	5	data	datum	NOUN
ajst-3251	94	6	set	set	NOUN
ajst-3251	94	7	has	have	VERB
ajst-3251	94	8	too	too	ADV
ajst-3251	94	9	few	few	ADJ
ajst-3251	94	10	samples	sample	NOUN
ajst-3251	94	11	,	,	PUNCT
ajst-3251	94	12	the	the	DET
ajst-3251	94	13	model	model	NOUN
ajst-3251	94	14	will	will	AUX
ajst-3251	94	15	perform	perform	VERB
ajst-3251	94	16	well	well	ADV
ajst-3251	94	17	in	in	ADP
ajst-3251	94	18	the	the	DET
ajst-3251	94	19	training	training	NOUN
ajst-3251	94	20	set	set	NOUN
ajst-3251	94	21	,	,	PUNCT
ajst-3251	94	22	but	but	CCONJ
ajst-3251	94	23	poorly	poorly	ADV
ajst-3251	94	24	in	in	ADP
ajst-3251	94	25	the	the	DET
ajst-3251	94	26	test	test	NOUN
ajst-3251	94	27	set	set	NOUN
ajst-3251	94	28	.	.	PUNCT
ajst-3251	95	1	this	this	DET
ajst-3251	95	2	phenomenon	phenomenon	NOUN
ajst-3251	95	3	is	be	AUX
ajst-3251	95	4	called	call	VERB
ajst-3251	95	5	overfitting	overfitte	VERB
ajst-3251	95	6	.	.	PUNCT
ajst-3251	96	1	the	the	DET
ajst-3251	96	2	basic	basic	ADJ
ajst-3251	96	3	method	method	NOUN
ajst-3251	96	4	to	to	PART
ajst-3251	96	5	solve	solve	VERB
ajst-3251	96	6	the	the	DET
ajst-3251	96	7	over	over	ADP
ajst-3251	96	8	fitting	fitting	ADJ
ajst-3251	96	9	problem	problem	NOUN
ajst-3251	96	10	is	be	AUX
ajst-3251	96	11	to	to	PART
ajst-3251	96	12	increase	increase	VERB
ajst-3251	96	13	the	the	DET
ajst-3251	96	14	number	number	NOUN
ajst-3251	96	15	of	of	ADP
ajst-3251	96	16	samples	sample	NOUN
ajst-3251	96	17	.	.	PUNCT
ajst-3251	97	1	in	in	ADP
ajst-3251	97	2	the	the	DET
ajst-3251	97	3	process	process	NOUN
ajst-3251	97	4	of	of	ADP
ajst-3251	97	5	model	model	NOUN
ajst-3251	97	6	training	training	NOUN
ajst-3251	97	7	,	,	PUNCT
ajst-3251	97	8	there	there	PRON
ajst-3251	97	9	are	be	VERB
ajst-3251	97	10	still	still	ADV
ajst-3251	97	11	25224	25224	NUM
ajst-3251	97	12	pieces	piece	NOUN
ajst-3251	97	13	of	of	ADP
ajst-3251	97	14	data	datum	NOUN
ajst-3251	97	15	in	in	ADP
ajst-3251	97	16	the	the	DET
ajst-3251	97	17	data	datum	NOUN
ajst-3251	97	18	set	set	VERB
ajst-3251	97	19	after	after	ADP
ajst-3251	97	20	data	datum	NOUN
ajst-3251	97	21	cleaning	clean	VERB
ajst-3251	97	22	,	,	PUNCT
ajst-3251	97	23	so	so	CCONJ
ajst-3251	97	24	the	the	PRON
ajst-3251	97	25	over	over	ADP
ajst-3251	97	26	fitting	fitting	ADJ
ajst-3251	97	27	problem	problem	NOUN
ajst-3251	97	28	is	be	AUX
ajst-3251	97	29	reasonably	reasonably	ADV
ajst-3251	97	30	avoided	avoid	VERB
ajst-3251	97	31	in	in	ADP
ajst-3251	97	32	the	the	DET
ajst-3251	97	33	process	process	NOUN
ajst-3251	97	34	of	of	ADP
ajst-3251	97	35	model	model	NOUN
ajst-3251	97	36	establishment	establishment	NOUN
ajst-3251	97	37	.	.	PUNCT
ajst-3251	98	1	table	table	NOUN
ajst-3251	98	2	2	2	NUM
ajst-3251	98	3	.	.	PUNCT
ajst-3251	99	1	training	training	NOUN
ajst-3251	99	2	results	result	NOUN
ajst-3251	99	3	of	of	ADP
ajst-3251	99	4	different	different	ADJ
ajst-3251	99	5	model	model	NOUN
ajst-3251	99	6	structures	structure	NOUN
ajst-3251	99	7	training	train	VERB
ajst-3251	99	8	no	no	DET
ajst-3251	99	9	number	number	NOUN
ajst-3251	99	10	of	of	ADP
ajst-3251	99	11	model	model	NOUN
ajst-3251	99	12	layers	layer	NOUN
ajst-3251	99	13	hidden	hide	VERB
ajst-3251	99	14	layer	layer	NOUN
ajst-3251	99	15	structure	structure	NOUN
ajst-3251	99	16	convergence	convergence	NOUN
ajst-3251	99	17	or	or	CCONJ
ajst-3251	99	18	not	not	PART
ajst-3251	99	19	processing	process	VERB
ajst-3251	99	20	time	time	NOUN
ajst-3251	99	21	(	(	PUNCT
ajst-3251	99	22	seconds	second	NOUN
ajst-3251	99	23	)	)	PUNCT
ajst-3251	100	1	1	1	NUM
ajst-3251	100	2	3	3	NUM
ajst-3251	100	3	32/16	32/16	NUM
ajst-3251	100	4	yes	yes	NOUN
ajst-3251	100	5	0.8425	0.8425	NUM
ajst-3251	100	6	101	101	NUM
ajst-3251	100	7	2	2	NUM
ajst-3251	100	8	3	3	NUM
ajst-3251	100	9	32/32	32/32	NUM
ajst-3251	100	10	yes	yes	NOUN
ajst-3251	100	11	0.8487	0.8487	NUM
ajst-3251	100	12	111	111	NUM
ajst-3251	100	13	3	3	NUM
ajst-3251	100	14	3	3	NUM
ajst-3251	100	15	64/32	64/32	NUM
ajst-3251	100	16	yes	yes	NOUN
ajst-3251	101	1	0.8505	0.8505	NUM
ajst-3251	101	2	106	106	NUM
ajst-3251	101	3	4	4	NUM
ajst-3251	101	4	3	3	NUM
ajst-3251	101	5	64/64	64/64	NUM
ajst-3251	101	6	yes	yes	NOUN
ajst-3251	101	7	0.8391	0.8391	NUM
ajst-3251	101	8	111	111	NUM
ajst-3251	101	9	5	5	NUM
ajst-3251	101	10	3	3	NUM
ajst-3251	101	11	64/16	64/16	NUM
ajst-3251	102	1	yes	yes	INTJ
ajst-3251	102	2	0.8455	0.8455	NUM
ajst-3251	102	3	108	108	NUM
ajst-3251	102	4	6	6	NUM
ajst-3251	102	5	3	3	NUM
ajst-3251	102	6	128/64	128/64	NUM
ajst-3251	103	1	yes	yes	INTJ
ajst-3251	103	2	0.8490	0.8490	NUM
ajst-3251	103	3	118	118	NUM
ajst-3251	103	4	n	n	NOUN
ajst-3251	103	5	/	/	SYM
ajst-3251	103	6	a	a	DET
ajst-3251	103	7	3	3	NUM
ajst-3251	103	8	128/128	128/128	NUM
ajst-3251	103	9	no	no	PRON
ajst-3251	103	10	n	n	CCONJ
ajst-3251	103	11	/	/	SYM
ajst-3251	103	12	a	a	DET
ajst-3251	103	13	n	n	NOUN
ajst-3251	103	14	/	/	SYM
ajst-3251	103	15	a	a	DET
ajst-3251	103	16	7	7	NUM
ajst-3251	103	17	4	4	NUM
ajst-3251	103	18	16/16/16	16/16/16	NUM
ajst-3251	104	1	yes	yes	NOUN
ajst-3251	104	2	0.8509	0.8509	NUM
ajst-3251	105	1	101	101	NUM
ajst-3251	105	2	8	8	NUM
ajst-3251	105	3	4	4	NUM
ajst-3251	105	4	32/32/32	32/32/32	NUM
ajst-3251	105	5	yes	yes	NOUN
ajst-3251	105	6	0.8518	0.8518	NUM
ajst-3251	105	7	110	110	NUM
ajst-3251	105	8	9	9	NUM
ajst-3251	105	9	4	4	NUM
ajst-3251	105	10	64/32/16	64/32/16	NUM
ajst-3251	106	1	yes	yes	NOUN
ajst-3251	106	2	0.8547	0.8547	NUM
ajst-3251	106	3	126	126	NUM
ajst-3251	106	4	10	10	NUM
ajst-3251	106	5	4	4	NUM
ajst-3251	106	6	64/32/32	64/32/32	NUM
ajst-3251	106	7	yes	yes	NOUN
ajst-3251	106	8	0.8506	0.8506	NUM
ajst-3251	106	9	129	129	NUM
ajst-3251	106	10	11	11	NUM
ajst-3251	106	11	4	4	NUM
ajst-3251	106	12	64/64/64	64/64/64	NUM
ajst-3251	106	13	yes	yes	NOUN
ajst-3251	106	14	0.8549	0.8549	NUM
ajst-3251	106	15	122	122	NUM
ajst-3251	106	16	12	12	NUM
ajst-3251	106	17	4	4	NUM
ajst-3251	106	18	128/64/32	128/64/32	NUM
ajst-3251	106	19	yes	yes	NUM
ajst-3251	106	20	0.8533	0.8533	NUM
ajst-3251	106	21	118	118	NUM
ajst-3251	106	22	13	13	NUM
ajst-3251	106	23	5	5	NUM
ajst-3251	106	24	128/64/32/16	128/64/32/16	NUM
ajst-3251	107	1	yes	yes	INTJ
ajst-3251	107	2	0.8615	0.8615	NUM
ajst-3251	107	3	142	142	NUM
ajst-3251	107	4	14	14	NUM
ajst-3251	107	5	5	5	NUM
ajst-3251	107	6	128/64/32/32	128/64/32/32	NUM
ajst-3251	107	7	yes	yes	NOUN
ajst-3251	107	8	0.8575	0.8575	NUM
ajst-3251	107	9	141	141	NUM
ajst-3251	107	10	15	15	NUM
ajst-3251	107	11	5	5	NUM
ajst-3251	107	12	128/32/32/32	128/32/32/32	NUM
ajst-3251	108	1	yes	yes	NOUN
ajst-3251	108	2	0.8524	0.8524	NUM
ajst-3251	108	3	141	141	NUM
ajst-3251	108	4	16	16	NUM
ajst-3251	108	5	6	6	NUM
ajst-3251	108	6	256/128/64/32/16	256/128/64/32/16	NUM
ajst-3251	108	7	yes	yes	NOUN
ajst-3251	108	8	0.8536	0.8536	NUM
ajst-3251	108	9	171	171	NUM
ajst-3251	109	1	it	it	PRON
ajst-3251	109	2	can	can	AUX
ajst-3251	109	3	be	be	AUX
ajst-3251	109	4	seen	see	VERB
ajst-3251	109	5	from	from	ADP
ajst-3251	109	6	the	the	DET
ajst-3251	109	7	training	training	NOUN
ajst-3251	109	8	results	result	NOUN
ajst-3251	109	9	that	that	SCONJ
ajst-3251	109	10	,	,	PUNCT
ajst-3251	109	11	first	first	ADV
ajst-3251	109	12	of	of	ADP
ajst-3251	109	13	all	all	PRON
ajst-3251	109	14	,	,	PUNCT
ajst-3251	109	15	when	when	SCONJ
ajst-3251	109	16	the	the	DET
ajst-3251	109	17	number	number	NOUN
ajst-3251	109	18	of	of	ADP
ajst-3251	109	19	layers	layer	NOUN
ajst-3251	109	20	is	be	AUX
ajst-3251	109	21	the	the	DET
ajst-3251	109	22	same	same	ADJ
ajst-3251	109	23	,	,	PUNCT
ajst-3251	109	24	increasing	increase	VERB
ajst-3251	109	25	the	the	DET
ajst-3251	109	26	number	number	NOUN
ajst-3251	109	27	of	of	ADP
ajst-3251	109	28	nodes	node	NOUN
ajst-3251	109	29	has	have	VERB
ajst-3251	109	30	little	little	ADJ
ajst-3251	109	31	effect	effect	NOUN
ajst-3251	109	32	on	on	ADP
ajst-3251	109	33	the	the	DET
ajst-3251	109	34	correlation	correlation	NOUN
ajst-3251	109	35	coefficient	coefficient	NOUN
ajst-3251	109	36	of	of	ADP
ajst-3251	109	37	the	the	DET
ajst-3251	109	38	model	model	NOUN
ajst-3251	109	39	,	,	PUNCT
ajst-3251	109	40	tends	tend	VERB
ajst-3251	109	41	to	to	PART
ajst-3251	109	42	be	be	AUX
ajst-3251	109	43	stable	stable	ADJ
ajst-3251	109	44	in	in	ADP
ajst-3251	109	45	general	general	ADJ
ajst-3251	109	46	,	,	PUNCT
ajst-3251	109	47	and	and	CCONJ
ajst-3251	109	48	has	have	VERB
ajst-3251	109	49	little	little	ADJ
ajst-3251	109	50	impact	impact	NOUN
ajst-3251	109	51	on	on	ADP
ajst-3251	109	52	efficiency	efficiency	NOUN
ajst-3251	109	53	.	.	PUNCT
ajst-3251	110	1	when	when	SCONJ
ajst-3251	110	2	the	the	DET
ajst-3251	110	3	node	node	ADJ
ajst-3251	110	4	structure	structure	NOUN
ajst-3251	110	5	is	be	AUX
ajst-3251	110	6	designed	design	VERB
ajst-3251	110	7	as	as	ADP
ajst-3251	110	8	a	a	DET
ajst-3251	110	9	decreasing	decrease	VERB
ajst-3251	110	10	structure	structure	NOUN
ajst-3251	110	11	,	,	PUNCT
ajst-3251	110	12	the	the	DET
ajst-3251	110	13	model	model	NOUN
ajst-3251	110	14	convergence	convergence	NOUN
ajst-3251	110	15	effect	effect	NOUN
ajst-3251	110	16	is	be	AUX
ajst-3251	110	17	good	good	ADJ
ajst-3251	110	18	,	,	PUNCT
ajst-3251	110	19	while	while	SCONJ
ajst-3251	110	20	when	when	SCONJ
ajst-3251	110	21	the	the	DET
ajst-3251	110	22	node	node	ADJ
ajst-3251	110	23	structure	structure	NOUN
ajst-3251	110	24	is	be	AUX
ajst-3251	110	25	set	set	VERB
ajst-3251	110	26	as	as	ADP
ajst-3251	110	27	a	a	DET
ajst-3251	110	28	structure	structure	NOUN
ajst-3251	110	29	with	with	ADP
ajst-3251	110	30	the	the	DET
ajst-3251	110	31	same	same	ADJ
ajst-3251	110	32	number	number	NOUN
ajst-3251	110	33	,	,	PUNCT
ajst-3251	110	34	the	the	DET
ajst-3251	110	35	model	model	NOUN
ajst-3251	110	36	prediction	prediction	NOUN
ajst-3251	110	37	effect	effect	NOUN
ajst-3251	110	38	is	be	AUX
ajst-3251	110	39	poor	poor	ADJ
ajst-3251	110	40	and	and	CCONJ
ajst-3251	110	41	the	the	DET
ajst-3251	110	42	efficiency	efficiency	NOUN
ajst-3251	110	43	is	be	AUX
ajst-3251	110	44	low	low	ADJ
ajst-3251	110	45	.	.	PUNCT
ajst-3251	111	1	in	in	ADP
ajst-3251	111	2	some	some	DET
ajst-3251	111	3	cases	case	NOUN
ajst-3251	111	4	,	,	PUNCT
ajst-3251	111	5	the	the	DET
ajst-3251	111	6	total	total	ADJ
ajst-3251	111	7	number	number	NOUN
ajst-3251	111	8	of	of	ADP
ajst-3251	111	9	runs	run	NOUN
ajst-3251	111	10	exceeds	exceed	VERB
ajst-3251	111	11	500	500	NUM
ajst-3251	111	12	,	,	PUNCT
ajst-3251	111	13	and	and	CCONJ
ajst-3251	111	14	no	no	DET
ajst-3251	111	15	obvious	obvious	ADJ
ajst-3251	111	16	convergence	convergence	NOUN
ajst-3251	111	17	effect	effect	NOUN
ajst-3251	111	18	is	be	AUX
ajst-3251	111	19	obtained	obtain	VERB
ajst-3251	111	20	.	.	PUNCT
ajst-3251	112	1	secondly	secondly	ADV
ajst-3251	112	2	,	,	PUNCT
ajst-3251	112	3	when	when	SCONJ
ajst-3251	112	4	the	the	DET
ajst-3251	112	5	depth	depth	NOUN
ajst-3251	112	6	of	of	ADP
ajst-3251	112	7	the	the	DET
ajst-3251	112	8	model	model	NOUN
ajst-3251	112	9	structure	structure	NOUN
ajst-3251	112	10	is	be	AUX
ajst-3251	112	11	increased	increase	VERB
ajst-3251	112	12	,	,	PUNCT
ajst-3251	112	13	the	the	DET
ajst-3251	112	14	correlation	correlation	NOUN
ajst-3251	112	15	coefficient	coefficient	NOUN
ajst-3251	112	16	is	be	AUX
ajst-3251	112	17	significantly	significantly	ADV
ajst-3251	112	18	improved	improve	VERB
ajst-3251	112	19	,	,	PUNCT
ajst-3251	112	20	but	but	CCONJ
ajst-3251	112	21	the	the	DET
ajst-3251	112	22	time	time	NOUN
ajst-3251	112	23	is	be	AUX
ajst-3251	112	24	increased	increase	VERB
ajst-3251	112	25	.	.	PUNCT
ajst-3251	113	1	finally	finally	ADV
ajst-3251	113	2	,	,	PUNCT
ajst-3251	113	3	when	when	SCONJ
ajst-3251	113	4	the	the	DET
ajst-3251	113	5	number	number	NOUN
ajst-3251	113	6	of	of	ADP
ajst-3251	113	7	layers	layer	NOUN
ajst-3251	113	8	is	be	AUX
ajst-3251	113	9	increased	increase	VERB
ajst-3251	113	10	to	to	ADP
ajst-3251	113	11	a	a	DET
ajst-3251	113	12	certain	certain	ADJ
ajst-3251	113	13	level	level	NOUN
ajst-3251	113	14	,	,	PUNCT
ajst-3251	113	15	the	the	DET
ajst-3251	113	16	correlation	correlation	NOUN
ajst-3251	113	17	coefficient	coefficient	NOUN
ajst-3251	113	18	is	be	AUX
ajst-3251	113	19	reduced	reduce	VERB
ajst-3251	113	20	compared	compare	VERB
ajst-3251	113	21	with	with	ADP
ajst-3251	113	22	the	the	DET
ajst-3251	113	23	previous	previous	ADJ
ajst-3251	113	24	level	level	NOUN
ajst-3251	113	25	,	,	PUNCT
ajst-3251	113	26	indicating	indicate	VERB
ajst-3251	113	27	that	that	SCONJ
ajst-3251	113	28	there	there	PRON
ajst-3251	113	29	are	be	VERB
ajst-3251	113	30	some	some	DET
ajst-3251	113	31	thresholds	threshold	NOUN
ajst-3251	113	32	for	for	ADP
ajst-3251	113	33	the	the	DET
ajst-3251	113	34	impact	impact	NOUN
ajst-3251	113	35	of	of	ADP
ajst-3251	113	36	increasing	increase	VERB
ajst-3251	113	37	the	the	DET
ajst-3251	113	38	number	number	NOUN
ajst-3251	113	39	of	of	ADP
ajst-3251	113	40	model	model	NOUN
ajst-3251	113	41	layers	layer	NOUN
ajst-3251	113	42	on	on	ADP
ajst-3251	113	43	the	the	DET
ajst-3251	113	44	accuracy	accuracy	NOUN
ajst-3251	113	45	.	.	PUNCT
ajst-3251	114	1	53	53	NUM
ajst-3251	114	2	figure	figure	NOUN
ajst-3251	114	3	5	5	NUM
ajst-3251	114	4	.	.	PUNCT
ajst-3251	114	5	accuracy	accuracy	NOUN
ajst-3251	114	6	evaluation	evaluation	NOUN
ajst-3251	114	7	of	of	ADP
ajst-3251	114	8	different	different	ADJ
ajst-3251	114	9	model	model	NOUN
ajst-3251	114	10	structures	structure	NOUN
ajst-3251	114	11	r2	r2	PROPN
ajst-3251	114	12	figure	figure	NOUN
ajst-3251	114	13	6	6	NUM
ajst-3251	114	14	.	.	PUNCT
ajst-3251	114	15	training	training	NOUN
ajst-3251	114	16	time	time	NOUN
ajst-3251	114	17	for	for	ADP
ajst-3251	114	18	different	different	ADJ
ajst-3251	114	19	model	model	NOUN
ajst-3251	114	20	structures	structure	NOUN
ajst-3251	114	21	after	after	SCONJ
ajst-3251	114	22	the	the	DET
ajst-3251	114	23	training	training	NOUN
ajst-3251	114	24	is	be	AUX
ajst-3251	114	25	completed	complete	VERB
ajst-3251	114	26	,	,	PUNCT
ajst-3251	114	27	select	select	VERB
ajst-3251	114	28	the	the	DET
ajst-3251	114	29	optimal	optimal	ADJ
ajst-3251	114	30	model	model	NOUN
ajst-3251	114	31	16	16	NUM
ajst-3251	114	32	to	to	PART
ajst-3251	114	33	increase	increase	VERB
ajst-3251	114	34	the	the	DET
ajst-3251	114	35	training	training	NOUN
ajst-3251	114	36	batch	batch	NOUN
ajst-3251	114	37	to	to	ADP
ajst-3251	114	38	1000	1000	NUM
ajst-3251	114	39	and	and	CCONJ
ajst-3251	114	40	select	select	VERB
ajst-3251	114	41	this	this	DET
ajst-3251	114	42	model	model	NOUN
ajst-3251	114	43	to	to	PART
ajst-3251	114	44	predict	predict	VERB
ajst-3251	114	45	the	the	DET
ajst-3251	114	46	penetration	penetration	NOUN
ajst-3251	114	47	rate	rate	NOUN
ajst-3251	114	48	.	.	PUNCT
ajst-3251	115	1	it	it	PRON
ajst-3251	115	2	is	be	AUX
ajst-3251	115	3	found	find	VERB
ajst-3251	115	4	that	that	SCONJ
ajst-3251	115	5	r2	r2	PROPN
ajst-3251	115	6	is	be	AUX
ajst-3251	115	7	adjusted	adjust	VERB
ajst-3251	115	8	to	to	ADP
ajst-3251	115	9	0.8647	0.8647	NUM
ajst-3251	115	10	.	.	PUNCT
ajst-3251	116	1	the	the	DET
ajst-3251	116	2	results	result	NOUN
ajst-3251	116	3	show	show	VERB
ajst-3251	116	4	that	that	SCONJ
ajst-3251	116	5	increasing	increase	VERB
ajst-3251	116	6	the	the	DET
ajst-3251	116	7	training	training	NOUN
ajst-3251	116	8	batch	batch	NOUN
ajst-3251	116	9	can	can	AUX
ajst-3251	116	10	improve	improve	VERB
ajst-3251	116	11	the	the	DET
ajst-3251	116	12	training	training	NOUN
ajst-3251	116	13	accuracy	accuracy	NOUN
ajst-3251	116	14	.	.	PUNCT
ajst-3251	117	1	figure	figure	NOUN
ajst-3251	117	2	7	7	NUM
ajst-3251	117	3	.	.	PUNCT
ajst-3251	117	4	comparison	comparison	NOUN
ajst-3251	117	5	between	between	ADP
ajst-3251	117	6	predicted	predict	VERB
ajst-3251	117	7	results	result	NOUN
ajst-3251	117	8	and	and	CCONJ
ajst-3251	117	9	actual	actual	ADJ
ajst-3251	117	10	results	result	NOUN
ajst-3251	117	11	of	of	ADP
ajst-3251	117	12	the	the	DET
ajst-3251	117	13	optimal	optimal	ADJ
ajst-3251	117	14	model	model	NOUN
ajst-3251	117	15	54	54	NUM
ajst-3251	117	16	4	4	NUM
ajst-3251	117	17	.	.	PUNCT
ajst-3251	118	1	conclusion	conclusion	NOUN
ajst-3251	118	2	(	(	PUNCT
ajst-3251	118	3	1	1	X
ajst-3251	118	4	)	)	PUNCT
ajst-3251	118	5	the	the	DET
ajst-3251	118	6	depth	depth	NOUN
ajst-3251	118	7	learning	learning	NOUN
ajst-3251	118	8	method	method	NOUN
ajst-3251	118	9	is	be	AUX
ajst-3251	118	10	more	more	ADV
ajst-3251	118	11	effective	effective	ADJ
ajst-3251	118	12	and	and	CCONJ
ajst-3251	118	13	accurate	accurate	ADJ
ajst-3251	118	14	in	in	ADP
ajst-3251	118	15	predicting	predict	VERB
ajst-3251	118	16	the	the	DET
ajst-3251	118	17	drilling	drilling	NOUN
ajst-3251	118	18	speed	speed	NOUN
ajst-3251	118	19	,	,	PUNCT
ajst-3251	118	20	and	and	CCONJ
ajst-3251	118	21	the	the	DET
ajst-3251	118	22	research	research	NOUN
ajst-3251	118	23	finally	finally	ADV
ajst-3251	118	24	improved	improve	VERB
ajst-3251	118	25	the	the	DET
ajst-3251	118	26	correlation	correlation	NOUN
ajst-3251	118	27	coefficient	coefficient	NOUN
ajst-3251	118	28	r2	r2	PROPN
ajst-3251	118	29	to	to	ADP
ajst-3251	118	30	0.8647	0.8647	NUM
ajst-3251	118	31	.	.	PUNCT
ajst-3251	119	1	(	(	PUNCT
ajst-3251	119	2	2	2	X
ajst-3251	119	3	)	)	PUNCT
ajst-3251	119	4	deep	deep	ADJ
ajst-3251	119	5	learning	learning	NOUN
ajst-3251	119	6	model	model	NOUN
ajst-3251	119	7	structure	structure	NOUN
ajst-3251	119	8	optimization	optimization	NOUN
ajst-3251	119	9	is	be	AUX
ajst-3251	119	10	more	more	ADV
ajst-3251	119	11	conducive	conducive	ADJ
ajst-3251	119	12	to	to	ADP
ajst-3251	119	13	characterizing	characterize	VERB
ajst-3251	119	14	nonlinear	nonlinear	ADJ
ajst-3251	119	15	problems	problem	NOUN
ajst-3251	119	16	,	,	PUNCT
ajst-3251	119	17	and	and	CCONJ
ajst-3251	119	18	selecting	select	VERB
ajst-3251	119	19	appropriate	appropriate	ADJ
ajst-3251	119	20	model	model	NOUN
ajst-3251	119	21	layers	layer	NOUN
ajst-3251	119	22	and	and	CCONJ
ajst-3251	119	23	nodes	node	NOUN
ajst-3251	119	24	can	can	AUX
ajst-3251	119	25	better	well	ADV
ajst-3251	119	26	match	match	VERB
ajst-3251	119	27	the	the	DET
ajst-3251	119	28	penetration	penetration	NOUN
ajst-3251	119	29	rate	rate	NOUN
ajst-3251	119	30	prediction	prediction	NOUN
ajst-3251	119	31	problem	problem	NOUN
ajst-3251	119	32	.	.	PUNCT
ajst-3251	120	1	(	(	PUNCT
ajst-3251	120	2	3	3	X
ajst-3251	120	3	)	)	PUNCT
ajst-3251	120	4	the	the	DET
ajst-3251	120	5	depth	depth	NOUN
ajst-3251	120	6	neural	neural	ADJ
ajst-3251	120	7	network	network	NOUN
ajst-3251	120	8	model	model	NOUN
ajst-3251	120	9	has	have	VERB
ajst-3251	120	10	high	high	ADJ
ajst-3251	120	11	efficiency	efficiency	NOUN
ajst-3251	120	12	and	and	CCONJ
ajst-3251	120	13	the	the	DET
ajst-3251	120	14	overall	overall	ADJ
ajst-3251	120	15	operation	operation	NOUN
ajst-3251	120	16	time	time	NOUN
ajst-3251	120	17	is	be	AUX
ajst-3251	120	18	less	less	ADJ
ajst-3251	120	19	than	than	ADP
ajst-3251	120	20	3	3	NUM
ajst-3251	120	21	minutes	minute	NOUN
ajst-3251	120	22	,	,	PUNCT
ajst-3251	120	23	which	which	PRON
ajst-3251	120	24	can	can	AUX
ajst-3251	120	25	be	be	AUX
ajst-3251	120	26	effectively	effectively	ADV
ajst-3251	120	27	used	use	VERB
ajst-3251	120	28	for	for	ADP
ajst-3251	120	29	real	real	ADJ
ajst-3251	120	30	-	-	PUNCT
ajst-3251	120	31	time	time	NOUN
ajst-3251	120	32	drilling	drilling	NOUN
ajst-3251	120	33	prediction	prediction	NOUN
ajst-3251	120	34	.	.	PUNCT
ajst-3251	121	1	references	reference	NOUN
ajst-3251	121	2	[	[	X
ajst-3251	121	3	1	1	NUM
ajst-3251	121	4	]	]	PUNCT
ajst-3251	121	5	hegde	hegde	NOUN
ajst-3251	121	6	,	,	PUNCT
ajst-3251	121	7	c.	c.	NOUN
ajst-3251	121	8	,	,	PUNCT
ajst-3251	121	9	pyrcz	pyrcz	NOUN
ajst-3251	121	10	,	,	PUNCT
ajst-3251	121	11	m.	m.	NOUN
ajst-3251	121	12	,	,	PUNCT
ajst-3251	121	13	millwater	millwater	PROPN
ajst-3251	121	14	,	,	PUNCT
ajst-3251	121	15	h.	h.	PROPN
ajst-3251	121	16	,	,	PUNCT
ajst-3251	121	17	daigle	daigle	PROPN
ajst-3251	121	18	,	,	PUNCT
ajst-3251	121	19	h.	h.	PROPN
ajst-3251	121	20	,	,	PUNCT
ajst-3251	121	21	and	and	CCONJ
ajst-3251	121	22	gray	gray	ADJ
ajst-3251	121	23	,	,	PUNCT
ajst-3251	121	24	k.	k.	PROPN
ajst-3251	121	25	,	,	PUNCT
ajst-3251	121	26	fully	fully	ADV
ajst-3251	121	27	coupled	couple	VERB
ajst-3251	121	28	end	end	NOUN
ajst-3251	121	29	-	-	PUNCT
ajst-3251	121	30	to	to	ADP
ajst-3251	121	31	-	-	PUNCT
ajst-3251	121	32	end	end	NOUN
ajst-3251	121	33	drilling	drilling	NOUN
ajst-3251	121	34	optimization	optimization	NOUN
ajst-3251	121	35	model	model	NOUN
ajst-3251	121	36	using	use	VERB
ajst-3251	121	37	machine	machine	NOUN
ajst-3251	121	38	learning	learning	NOUN
ajst-3251	121	39	,	,	PUNCT
ajst-3251	121	40	journal	journal	NOUN
ajst-3251	121	41	of	of	ADP
ajst-3251	121	42	petroleum	petroleum	NOUN
ajst-3251	121	43	science	science	NOUN
ajst-3251	121	44	and	and	CCONJ
ajst-3251	121	45	engineering	engineering	NOUN
ajst-3251	121	46	,	,	PUNCT
ajst-3251	121	47	vol	vol	NOUN
ajst-3251	121	48	.	.	PROPN
ajst-3251	121	49	186	186	NUM
ajst-3251	121	50	,	,	PUNCT
ajst-3251	121	51	2020	2020	NUM
ajst-3251	121	52	,	,	PUNCT
ajst-3251	121	53	doi	doi	NOUN
ajst-3251	121	54	:	:	PUNCT
ajst-3251	121	55	10.1016	10.1016	NUM
ajst-3251	121	56	/	/	SYM
ajst-3251	121	57	j.petrol.2019.106681	j.petrol.2019.106681	ADJ
ajst-3251	121	58	.	.	PUNCT
ajst-3251	122	1	[	[	X
ajst-3251	122	2	2	2	X
ajst-3251	122	3	]	]	PUNCT
ajst-3251	122	4	soares	soare	NOUN
ajst-3251	122	5	c	c	PROPN
ajst-3251	122	6	,	,	PUNCT
ajst-3251	122	7	gray	gray	ADJ
ajst-3251	122	8	k	k	PROPN
ajst-3251	122	9	.	.	PUNCT
ajst-3251	123	1	real	real	ADJ
ajst-3251	123	2	-	-	PUNCT
ajst-3251	123	3	time	time	NOUN
ajst-3251	123	4	predictive	predictive	ADJ
ajst-3251	123	5	capabilities	capability	NOUN
ajst-3251	123	6	of	of	ADP
ajst-3251	123	7	analytical	analytical	ADJ
ajst-3251	123	8	and	and	CCONJ
ajst-3251	123	9	machine	machine	NOUN
ajst-3251	123	10	learning	learning	NOUN
ajst-3251	123	11	rate	rate	NOUN
ajst-3251	123	12	of	of	ADP
ajst-3251	123	13	penetration	penetration	NOUN
ajst-3251	123	14	(	(	PUNCT
ajst-3251	123	15	rop	rop	PROPN
ajst-3251	123	16	)	)	PUNCT
ajst-3251	123	17	models[j	models[j	PROPN
ajst-3251	123	18	]	]	PUNCT
ajst-3251	123	19	.	.	PUNCT
ajst-3251	124	1	journal	journal	PROPN
ajst-3251	124	2	of	of	ADP
ajst-3251	124	3	petroleum	petroleum	NOUN
ajst-3251	124	4	science	science	NOUN
ajst-3251	124	5	and	and	CCONJ
ajst-3251	124	6	engineering	engineering	NOUN
ajst-3251	124	7	,	,	PUNCT
ajst-3251	124	8	2019	2019	NUM
ajst-3251	124	9	,	,	PUNCT
ajst-3251	124	10	172:934	172:934	NOUN
ajst-3251	124	11	-	-	SYM
ajst-3251	124	12	959	959	NUM
ajst-3251	124	13	.	.	PUNCT
ajst-3251	125	1	[	[	X
ajst-3251	125	2	3	3	NUM
ajst-3251	125	3	]	]	X
ajst-3251	125	4	hegde	hegde	PROPN
ajst-3251	125	5	c	c	PROPN
ajst-3251	125	6	,	,	PUNCT
ajst-3251	125	7	daigle	daigle	PROPN
ajst-3251	125	8	h	h	NOUN
ajst-3251	125	9	,	,	PUNCT
ajst-3251	125	10	millwater	millwater	PROPN
ajst-3251	125	11	h	h	PROPN
ajst-3251	125	12	,	,	PUNCT
ajst-3251	125	13	et	et	PROPN
ajst-3251	125	14	al	al	PROPN
ajst-3251	125	15	.	.	PUNCT
ajst-3251	126	1	analysis	analysis	NOUN
ajst-3251	126	2	of	of	ADP
ajst-3251	126	3	rate	rate	NOUN
ajst-3251	126	4	of	of	ADP
ajst-3251	126	5	penetration	penetration	NOUN
ajst-3251	126	6	(	(	PUNCT
ajst-3251	126	7	rop	rop	NOUN
ajst-3251	126	8	)	)	PUNCT
ajst-3251	126	9	prediction	prediction	NOUN
ajst-3251	126	10	in	in	ADP
ajst-3251	126	11	drilling	drilling	NOUN
ajst-3251	126	12	using	use	VERB
ajst-3251	126	13	physics	physics	NOUN
ajst-3251	126	14	-	-	PUNCT
ajst-3251	126	15	based	base	VERB
ajst-3251	126	16	and	and	CCONJ
ajst-3251	126	17	data	data	NOUN
ajst-3251	126	18	-	-	PUNCT
ajst-3251	126	19	driven	drive	VERB
ajst-3251	126	20	models[j	models[j	NOUN
ajst-3251	126	21	]	]	PUNCT
ajst-3251	126	22	.	.	PUNCT
ajst-3251	127	1	journal	journal	PROPN
ajst-3251	127	2	of	of	ADP
ajst-3251	127	3	petroleum	petroleum	NOUN
ajst-3251	127	4	ence	ence	NOUN
ajst-3251	127	5	and	and	CCONJ
ajst-3251	127	6	engineering	engineering	NOUN
ajst-3251	127	7	,	,	PUNCT
ajst-3251	127	8	2017:295	2017:295	PROPN
ajst-3251	127	9	-	-	PUNCT
ajst-3251	127	10	306	306	NUM
ajst-3251	127	11	.	.	PUNCT
ajst-3251	128	1	[	[	X
ajst-3251	128	2	4	4	X
ajst-3251	128	3	]	]	PUNCT
ajst-3251	128	4	soares	soar	VERB
ajst-3251	128	5	c	c	PROPN
ajst-3251	128	6	,	,	PUNCT
ajst-3251	128	7	daigle	daigle	VERB
ajst-3251	128	8	h	h	NOUN
ajst-3251	128	9	,	,	PUNCT
ajst-3251	128	10	gray	gray	ADJ
ajst-3251	128	11	k	k	PROPN
ajst-3251	128	12	.	.	PUNCT
ajst-3251	129	1	evaluation	evaluation	NOUN
ajst-3251	129	2	of	of	ADP
ajst-3251	129	3	pdc	pdc	PROPN
ajst-3251	129	4	bit	bit	NOUN
ajst-3251	129	5	rop	rop	NOUN
ajst-3251	129	6	models	model	NOUN
ajst-3251	129	7	and	and	CCONJ
ajst-3251	129	8	the	the	DET
ajst-3251	129	9	effect	effect	NOUN
ajst-3251	129	10	of	of	ADP
ajst-3251	129	11	rock	rock	NOUN
ajst-3251	129	12	strength	strength	NOUN
ajst-3251	129	13	on	on	ADP
ajst-3251	129	14	model	model	NOUN
ajst-3251	129	15	coefficients[j	coefficients[j	PROPN
ajst-3251	129	16	]	]	X
ajst-3251	129	17	.	.	PUNCT
ajst-3251	130	1	journal	journal	PROPN
ajst-3251	130	2	of	of	ADP
ajst-3251	130	3	natural	natural	ADJ
ajst-3251	130	4	gas	gas	NOUN
ajst-3251	130	5	science	science	NOUN
ajst-3251	130	6	and	and	CCONJ
ajst-3251	130	7	engineering	engineering	NOUN
ajst-3251	130	8	,	,	PUNCT
ajst-3251	130	9	2016	2016	NUM
ajst-3251	130	10	,	,	PUNCT
ajst-3251	130	11	34:1225	34:1225	NOUN
ajst-3251	130	12	-	-	SYM
ajst-3251	130	13	1236	1236	NUM
ajst-3251	130	14	.	.	PUNCT
ajst-3251	131	1	[	[	X
ajst-3251	131	2	5	5	NUM
ajst-3251	131	3	]	]	X
ajst-3251	131	4	abdulmalek	abdulmalek	ADJ
ajst-3251	131	5	ahmed	ahmed	PROPN
ajst-3251	131	6	,	,	PUNCT
ajst-3251	131	7	abdulwahab	abdulwahab	PROPN
ajst-3251	131	8	ali	ali	PROPN
ajst-3251	131	9	,	,	PUNCT
ajst-3251	131	10	salaheldin	salaheldin	VERB
ajst-3251	131	11	elkatatny	elkatatny	NOUN
ajst-3251	131	12	,	,	PUNCT
ajst-3251	131	13	abdulazeez	abdulazeez	PROPN
ajst-3251	131	14	abdulraheem	abdulraheem	NOUN
ajst-3251	131	15	.	.	PUNCT
ajst-3251	132	1	new	new	ADJ
ajst-3251	132	2	artificial	artificial	ADJ
ajst-3251	132	3	neural	neural	ADJ
ajst-3251	132	4	networks	network	NOUN
ajst-3251	132	5	model	model	NOUN
ajst-3251	132	6	for	for	ADP
ajst-3251	132	7	predicting	predict	VERB
ajst-3251	132	8	rate	rate	NOUN
ajst-3251	132	9	of	of	ADP
ajst-3251	132	10	penetration	penetration	NOUN
ajst-3251	132	11	in	in	ADP
ajst-3251	132	12	deep	deep	ADJ
ajst-3251	132	13	shale	shale	NOUN
ajst-3251	132	14	formation[j	formation[j	PROPN
ajst-3251	132	15	]	]	PUNCT
ajst-3251	132	16	.	.	PUNCT
ajst-3251	133	1	sustainability,2019,11(22	sustainability,2019,11(22	NOUN
ajst-3251	133	2	):	):	PUNCT
ajst-3251	133	3	[	[	X
ajst-3251	133	4	6	6	NUM
ajst-3251	133	5	]	]	PUNCT
ajst-3251	133	6	arabjamaloei	arabjamaloei	NOUN
ajst-3251	133	7	r	r	NOUN
ajst-3251	133	8	,	,	PUNCT
ajst-3251	133	9	shadizadeh	shadizadeh	PROPN
ajst-3251	133	10	s	s	PRON
ajst-3251	133	11	.	.	PUNCT
ajst-3251	134	1	modeling	modeling	NOUN
ajst-3251	134	2	and	and	CCONJ
ajst-3251	134	3	optimizing	optimize	VERB
ajst-3251	134	4	rate	rate	NOUN
ajst-3251	134	5	of	of	ADP
ajst-3251	134	6	penetration	penetration	NOUN
ajst-3251	134	7	using	use	VERB
ajst-3251	134	8	intelligent	intelligent	ADJ
ajst-3251	134	9	systems	system	NOUN
ajst-3251	134	10	in	in	ADP
ajst-3251	134	11	an	an	DET
ajst-3251	134	12	iranian	iranian	ADJ
ajst-3251	134	13	southern	southern	ADJ
ajst-3251	134	14	oil	oil	NOUN
ajst-3251	134	15	field	field	NOUN
ajst-3251	134	16	(	(	PUNCT
ajst-3251	134	17	ahwaz	ahwaz	NOUN
ajst-3251	134	18	oil	oil	NOUN
ajst-3251	134	19	field)[j	field)[j	PROPN
ajst-3251	134	20	]	]	PUNCT
ajst-3251	134	21	.	.	PUNCT
ajst-3251	135	1	petroleum	petroleum	NOUN
ajst-3251	135	2	science	science	NOUN
ajst-3251	135	3	and	and	CCONJ
ajst-3251	135	4	technology	technology	NOUN
ajst-3251	135	5	,	,	PUNCT
ajst-3251	135	6	2011	2011	NUM
ajst-3251	135	7	,	,	PUNCT
ajst-3251	135	8	29(16):1637	29(16):1637	NUM
ajst-3251	135	9	-	-	SYM
ajst-3251	135	10	1648	1648	NUM
ajst-3251	135	11	.	.	PUNCT
ajst-3251	136	1	[	[	X
ajst-3251	136	2	7	7	NUM
ajst-3251	136	3	]	]	X
ajst-3251	136	4	amar	amar	PROPN
ajst-3251	136	5	k	k	PROPN
ajst-3251	136	6	,	,	PUNCT
ajst-3251	136	7	ibrahim	ibrahim	PROPN
ajst-3251	136	8	a	a	PRON
ajst-3251	136	9	.	.	PUNCT
ajst-3251	137	1	rate	rate	NOUN
ajst-3251	137	2	of	of	ADP
ajst-3251	137	3	penetration	penetration	NOUN
ajst-3251	137	4	prediction	prediction	NOUN
ajst-3251	137	5	and	and	CCONJ
ajst-3251	137	6	optimization	optimization	NOUN
ajst-3251	137	7	using	use	VERB
ajst-3251	137	8	advances	advance	NOUN
ajst-3251	137	9	in	in	ADP
ajst-3251	137	10	artificial	artificial	ADJ
ajst-3251	137	11	neural	neural	ADJ
ajst-3251	137	12	networks	network	NOUN
ajst-3251	137	13	,	,	PUNCT
ajst-3251	137	14	a	a	DET
ajst-3251	137	15	comparative	comparative	ADJ
ajst-3251	137	16	study	study	NOUN
ajst-3251	137	17	.	.	PUNCT
ajst-3251	138	1	2012	2012	NUM
ajst-3251	138	2	.	.	PUNCT
ajst-3251	139	1	[	[	X
ajst-3251	139	2	8	8	NUM
ajst-3251	139	3	]	]	X
ajst-3251	139	4	bataee	bataee	PROPN
ajst-3251	139	5	m	m	PROPN
ajst-3251	139	6	,	,	PUNCT
ajst-3251	139	7	irawan	irawan	PROPN
ajst-3251	139	8	s	s	PROPN
ajst-3251	139	9	,	,	PUNCT
ajst-3251	139	10	kamyab	kamyab	X
ajst-3251	139	11	m	m	PROPN
ajst-3251	139	12	.	.	PUNCT
ajst-3251	140	1	artificial	artificial	ADJ
ajst-3251	140	2	neural	neural	ADJ
ajst-3251	140	3	network	network	NOUN
ajst-3251	140	4	model	model	NOUN
ajst-3251	140	5	for	for	ADP
ajst-3251	140	6	prediction	prediction	NOUN
ajst-3251	140	7	of	of	ADP
ajst-3251	140	8	drilling	drilling	NOUN
ajst-3251	140	9	rate	rate	NOUN
ajst-3251	140	10	of	of	ADP
ajst-3251	140	11	penetration	penetration	NOUN
ajst-3251	140	12	and	and	CCONJ
ajst-3251	140	13	optimization	optimization	NOUN
ajst-3251	140	14	of	of	ADP
ajst-3251	140	15	parameters[j	parameters[j	PROPN
ajst-3251	140	16	]	]	PUNCT
ajst-3251	140	17	.	.	PUNCT
ajst-3251	141	1	journal	journal	PROPN
ajst-3251	141	2	of	of	ADP
ajst-3251	141	3	the	the	DET
ajst-3251	141	4	japan	japan	PROPN
ajst-3251	141	5	petroleum	petroleum	PROPN
ajst-3251	141	6	institute	institute	PROPN
ajst-3251	141	7	,	,	PUNCT
ajst-3251	141	8	2014	2014	NUM
ajst-3251	141	9	,	,	PUNCT
ajst-3251	141	10	57(2):65	57(2):65	NUM
ajst-3251	141	11	-	-	SYM
ajst-3251	141	12	70	70	NUM
ajst-3251	141	13	.	.	PUNCT
ajst-3251	142	1	[	[	X
ajst-3251	142	2	9	9	NUM
ajst-3251	142	3	]	]	SYM
ajst-3251	142	4	hadi	hadi	PROPN
ajst-3251	142	5	,	,	PUNCT
ajst-3251	142	6	f.	f.	PROPN
ajst-3251	142	7	,	,	PUNCT
ajst-3251	142	8	altaie	altaie	PROPN
ajst-3251	142	9	,	,	PUNCT
ajst-3251	142	10	h.	h.	PROPN
ajst-3251	142	11	,	,	PUNCT
ajst-3251	142	12	and	and	CCONJ
ajst-3251	142	13	alkamil	alkamil	PROPN
ajst-3251	142	14	,	,	PUNCT
ajst-3251	142	15	e.	e.	PROPN
ajst-3251	142	16	2019	2019	NUM
ajst-3251	142	17	.	.	PUNCT
ajst-3251	143	1	modeling	modeling	NOUN
ajst-3251	143	2	rate	rate	NOUN
ajst-3251	143	3	of	of	ADP
ajst-3251	143	4	penetration	penetration	NOUN
ajst-3251	143	5	using	use	VERB
ajst-3251	143	6	artificial	artificial	ADJ
ajst-3251	143	7	intelligent	intelligent	ADJ
ajst-3251	143	8	system	system	NOUN
ajst-3251	143	9	and	and	CCONJ
ajst-3251	143	10	multiple	multiple	ADJ
ajst-3251	143	11	regression	regression	NOUN
ajst-3251	143	12	analysis	analysis	NOUN
ajst-3251	143	13	.	.	PUNCT
ajst-3251	144	1	paper	paper	NOUN
ajst-3251	144	2	presented	present	VERB
ajst-3251	144	3	at	at	ADP
ajst-3251	144	4	the	the	DET
ajst-3251	144	5	abu	abu	PROPN
ajst-3251	144	6	dhabi	dhabi	PROPN
ajst-3251	144	7	international	international	ADJ
ajst-3251	144	8	petroleum	petroleum	NOUN
ajst-3251	144	9	exhibition	exhibition	NOUN
ajst-3251	144	10	&	&	CCONJ
ajst-3251	144	11	conference	conference	PROPN
ajst-3251	144	12	,	,	PUNCT
ajst-3251	144	13	abu	abu	PROPN
ajst-3251	144	14	dhabi	dhabi	PROPN
ajst-3251	144	15	,	,	PUNCT
ajst-3251	144	16	uae	uae	PROPN
ajst-3251	144	17	,	,	PUNCT
ajst-3251	144	18	11–14	11–14	NUM
ajst-3251	144	19	november	november	NOUN
ajst-3251	144	20	.	.	PUNCT
ajst-3251	145	1	[	[	X
ajst-3251	145	2	10	10	NUM
ajst-3251	145	3	]	]	X
ajst-3251	145	4	aliyev	aliyev	NOUN
ajst-3251	145	5	r	r	NOUN
ajst-3251	145	6	,	,	PUNCT
ajst-3251	145	7	paul	paul	PROPN
ajst-3251	145	8	d	d	PROPN
ajst-3251	145	9	.	.	PUNCT
ajst-3251	146	1	a	a	DET
ajst-3251	146	2	novel	novel	ADJ
ajst-3251	146	3	application	application	NOUN
ajst-3251	146	4	of	of	ADP
ajst-3251	146	5	artificial	artificial	ADJ
ajst-3251	146	6	neural	neural	ADJ
ajst-3251	146	7	networks	network	NOUN
ajst-3251	146	8	to	to	PART
ajst-3251	146	9	predict	predict	VERB
ajst-3251	146	10	rate	rate	NOUN
ajst-3251	146	11	of	of	ADP
ajst-3251	146	12	penetration[c]//	penetration[c]//	PROPN
ajst-3251	146	13	spe	spe	PROPN
ajst-3251	146	14	western	western	ADJ
ajst-3251	146	15	regional	regional	ADJ
ajst-3251	146	16	meeting	meeting	NOUN
ajst-3251	146	17	.	.	PUNCT
ajst-3251	147	1	2019	2019	NUM
ajst-3251	147	2	.	.	PUNCT
ajst-3251	148	1	[	[	X
ajst-3251	148	2	11	11	NUM
ajst-3251	148	3	]	]	X
ajst-3251	148	4	lippmann	lippmann	PROPN
ajst-3251	148	5	r	r	PROPN
ajst-3251	148	6	p	p	PROPN
ajst-3251	148	7	.	.	PUNCT
ajst-3251	149	1	an	an	DET
ajst-3251	149	2	introduction	introduction	NOUN
ajst-3251	149	3	to	to	ADP
ajst-3251	149	4	computing	compute	VERB
ajst-3251	149	5	with	with	ADP
ajst-3251	149	6	neural	neural	ADJ
ajst-3251	149	7	nets[j	nets[j	NOUN
ajst-3251	149	8	]	]	PUNCT
ajst-3251	149	9	.	.	PUNCT
ajst-3251	150	1	ieee	ieee	PROPN
ajst-3251	150	2	assp	assp	PROPN
ajst-3251	150	3	magazine	magazine	NOUN
ajst-3251	150	4	,	,	PUNCT
ajst-3251	150	5	1988	1988	NUM
ajst-3251	150	6	,	,	PUNCT
ajst-3251	150	7	4(2):4	4(2):4	NOUN
ajst-3251	150	8	-	-	PUNCT
ajst-3251	150	9	22	22	NUM
ajst-3251	150	10	.	.	PUNCT
ajst-3251	151	1	[	[	X
ajst-3251	151	2	12	12	NUM
ajst-3251	151	3	]	]	PUNCT
ajst-3251	151	4	cybenko	cybenko	NOUN
ajst-3251	151	5	g	g	PROPN
ajst-3251	151	6	.	.	PUNCT
ajst-3251	152	1	approximation	approximation	NOUN
ajst-3251	152	2	by	by	ADP
ajst-3251	152	3	superpositions	superposition	NOUN
ajst-3251	152	4	of	of	ADP
ajst-3251	152	5	a	a	DET
ajst-3251	152	6	sigmoidal	sigmoidal	NOUN
ajst-3251	152	7	function[j	function[j	NOUN
ajst-3251	152	8	]	]	PUNCT
ajst-3251	152	9	.	.	PUNCT
ajst-3251	153	1	mathematics	mathematic	NOUN
ajst-3251	153	2	of	of	ADP
ajst-3251	153	3	control	control	NOUN
ajst-3251	153	4	,	,	PUNCT
ajst-3251	153	5	signals	signal	NOUN
ajst-3251	153	6	and	and	CCONJ
ajst-3251	153	7	systems	system	NOUN
ajst-3251	153	8	,	,	PUNCT
ajst-3251	153	9	1989	1989	NUM
ajst-3251	153	10	,	,	PUNCT
ajst-3251	153	11	2(4):303	2(4):303	NUM
ajst-3251	153	12	-	-	SYM
ajst-3251	153	13	314	314	NUM
ajst-3251	153	14	.	.	PUNCT
ajst-3251	154	1	[	[	X
ajst-3251	154	2	13	13	NUM
ajst-3251	154	3	]	]	PUNCT
ajst-3251	154	4	hornik	hornik	X
ajst-3251	154	5	k	k	X
ajst-3251	154	6	,	,	PUNCT
ajst-3251	154	7	stinchcombe	stinchcombe	INTJ
ajst-3251	154	8	m	m	VERB
ajst-3251	154	9	,	,	PUNCT
ajst-3251	154	10	white	white	ADJ
ajst-3251	154	11	h	h	NOUN
ajst-3251	154	12	.	.	PUNCT
ajst-3251	155	1	multilayer	multilayer	ADJ
ajst-3251	155	2	feedforward	feedforward	NOUN
ajst-3251	155	3	networks	network	NOUN
ajst-3251	155	4	are	be	AUX
ajst-3251	155	5	universal	universal	ADJ
ajst-3251	155	6	approximators[j	approximators[j	PROPN
ajst-3251	155	7	]	]	PUNCT
ajst-3251	155	8	.	.	PUNCT
ajst-3251	156	1	neural	neural	ADJ
ajst-3251	156	2	networks	network	NOUN
ajst-3251	156	3	,	,	PUNCT
ajst-3251	156	4	1989	1989	NUM
ajst-3251	156	5	,	,	PUNCT
ajst-3251	156	6	2	2	NUM
ajst-3251	156	7	(	(	PUNCT
ajst-3251	156	8	5):359	5):359	NUM
ajst-3251	156	9	-	-	PUNCT
ajst-3251	156	10	366	366	NUM
ajst-3251	156	11	.	.	PUNCT
ajst-3251	157	1	[	[	X
ajst-3251	157	2	14	14	NUM
ajst-3251	157	3	]	]	X
ajst-3251	157	4	liang	liang	PROPN
ajst-3251	157	5	s	s	PROPN
ajst-3251	157	6	,	,	PUNCT
ajst-3251	157	7	srikant	srikant	ADJ
ajst-3251	157	8	r	r	NOUN
ajst-3251	157	9	.	.	PUNCT
ajst-3251	158	1	why	why	SCONJ
ajst-3251	158	2	deep	deep	ADJ
ajst-3251	158	3	neural	neural	ADJ
ajst-3251	158	4	networks	network	NOUN
ajst-3251	158	5	for	for	ADP
ajst-3251	158	6	function	function	NOUN
ajst-3251	158	7	approximation?[j	approximation?[j	NOUN
ajst-3251	158	8	]	]	X
ajst-3251	158	9	.	.	PUNCT
ajst-3251	158	10	2016	2016	NUM
ajst-3251	158	11	.	.	PUNCT