Full-span named entity recognition with boundary regression

Figures & data

Figure 1. The architecture of our model. It is an end-to-end architecture, which can be divided into four modules, including basic network, region proposal, boundary regressor and classifier. A sentence ‘She is born in New York’ is given as an example to show the flow path of recognising named entities. This example is introduced as follows in detail.

Table 1. Distribution of different intervals.

		+1	+2	+3	+4	+5	+6	+7	+8	+9	+10
ACE2005	0	17256	6076	2727	1490	869	536	369	303	175	166
	10	110	92	89	60	61	43	40	29	31	22
	20	26	17	14	4	10	7	3	3	1	2
	30	2	1	–	–	1	–	–	1	-	1
	40	–	–	–	–	–	–	–	–	1	–
GENIA	0	25573	16592	8169	3448	1179	537	232	146	74	30
	10	22	8	4	2	2	–	1	1	–	-

Table 2. Distribution of different intervals.

Datasets	Total	Category	Number	Ratio	Instances
ACE2005	30,638	S-Length	28,954	94.50%	‘Jacques Moret’
		M-Length	1,215	3.97%	‘jailed former Deputy Prime Minister Anwar Ibrahim’
		L-Length	469	1.53%	‘two of the four pilots who took control of the planes on 9/11’
GENIA	56,020	S-Length	55,498	99.06%	‘IRF-1/ISGF2’
		M-Length	512	0.91%	‘glucocorticoid response element ( GRE )-containing DNA fragments’
		L-Length	10	0.03%	‘human T cell lymphotropic virus type I ( HTLV-I ) long terminal repeat’

Table 3. Evaluation in the English Corpus.

			ACE2005			GENIA
Methods		Models	P(%)	R(%)	F(%)	P(%)	R(%)	F(%)
Token labelling		Lu and Roth (2015)	66.3	59.2	62.5	72.5	65.2	68.7
		Katiyar and Cardie (2018)	70.6	70.4	70.5	76.7	71.1	73.8
		Ju et al. (2018)	74.2	70.3	72.2	78.5	71.3	74.7
		Wang and Lu (2018)	76.8	72.3	74.5	77.0	73.3	75.1
		Shibuya and Hovy (2020)	85.94	85.69	85.82	76.3	74.7	75.5
		Straková et al. (2019)	–	–	84.33	–	–	78.3
		Wang et al. (2020)	85.30	87.40	86.34	79.45	78.94	79.19
		Li et al. (2020)	87.16	86.59	86.88	85.18	81.12	83.75
		Shen et al. (2022)	86.27	88.60	87.42	83.24	80.35	81.77
Span Classification	Limited-span	Lin et al. (2019)	75.2	72.5	73.9	75.2	73.3	74.2
		Xia et al. (2019)	79.0	77.3	78.2	–	–	-
		Sohrab and Miwa (2018)	-	-	-	93.6	66.7	77.5
		Lin et al. (2019)	–	–	75.2	–	–	–
	Full span	Lin et al. (2019)	76.2	73.6	74.9	75.8	73.9	74.8
		Fisher and Vlachos (2019)	82.7	82.1	82.4	–	–	-
		Tan et al. (2020)	83.8	83.9	83.9	79.2	77.4	78.3
		Zheng et al. (2019)	–	–	–	75.8	73.6	74.7
		Shen et al. (2021)	86.09	87.27	86.67	80.19	80.89	80.54
		Chen et al. (2022)	86.88	84.83	85.84	80.81	77.43	79.09
		Li et al. (2022)	85.03	88.62	86.79	83.10	79.76	81.39
		Yuan et al. (2022)	87.39	90.31	88.83	80.42	82.06	81.23
	Ours	S-Length	85.52	88.66	87.06	78.58	82.26	80.38
		M-Length	67.97	75.00	71.31	64.94	86.21	74.07
		L-length	65.00	67.00	66.00	100.00	50.00	67.00
		Total+BERT	87.23	87.46	87.35	81.62	80.09	80.85
		Total+ALBERT	87.32	88.21	87.76	79.57	79.65	79.61

Table 4. Performance of Boundary Regression.

		+1	+2	+3	+4	+5	+6	+7	+8	+9	+10
ACE2005	0	88.45	85.30	85.03	81.88	89.25	81.90	64.62	76.36	65.00	85.00
	10	78.57	50.00	72.73	66.67	85.71	57.14	72.73	50.00	33.33	100.00
	20	–	0.0	100.00	–	40.00	66.67	100.00	–	–	–
	30	–	-	–	–	–	–	–	–	–	–
	40	–	–	–	–	–	–	–	–	–	–
GENIA	0	81.02	81.64	79.75	77.53	71.49	75.93	78.43	80.00	58.82	60.00
	10	66.67	80.00	–	–	100.00	–	-	–	–	–

Table 5. Ablation Study on the ACE2005 corpus.

Model	P(%)	R(%)	F(%)
full model	87.23	87.46	87.35
w/o Token Embedding	83.76	87.63	85.65
w/o Character Embeddings	83.41	86.94	85.14
w/o POS Embeddings	83.56	86.21	84.86
w/o BERT	60.77	67.54	63.98

Table 6. Performance on Region Proposals.

		ACE2005				GENIA
		RRP	P(%)	R(%)	F(%)	RRP	P(%)	R(%)	F(%)
Exhaustive Enumeration	S-Length	94.5%	85.69	88.87	87.26	99.06%	78.12	81.97	80.00
	M-Length	3.97%	67.44	75.00	71.02	0.91%	72.73	82.76	77.42
	L-Length	1.53%	70.00	67.00	69.00	0.03%	100.00	50.00	67.00
	Total	100%	84.69	88.00	86.31	100%	78.07	81.97	79.97
Interval Enumeration (k=1)	S-Length	68.06%	85.68	88.38	87.01	62.34%	78.29	82.43	80.30
	M-Length	2.13%	71.31	75.00	73.11	0.59%	73.44	81.03	77.05
	L-Length	0.91%	69.00	67.00	68.00	0.01%	100.00	50.00	67.00
	Total	71.10%	84.84	87.53	86.17	62.94%	78.24	82.40	80.27
Interval Enumeration (k=2)	S-Length	61.19%	84.45	88.63	86.49	51.80%	77.73	82.41	80.00
	M-Length	1.75%	67.67	77.59	72.29	0.47%	75.76	86.21	80.65
	L-Length	0.64%	73.00	70.00	71.00	0.01%	100.00	50.00	67.00
	Total	63.58%	83.55	87.90	85.67	52.28%	77.71	82.44	80.00
Series Enumeration	S-Length	81.02%	85.52	88.66	87.06	81.42%	78.58	82.26	80.38
	M-Length	0.99%	67.97	75.00	71.31	0.26%	71.83	87.93	79.07
	L-Length	0.14%	65.00	67.00	66.00	0.00%	100.00	50.00	67.00
	Total	82.15%	84.44	87.8	86.09	81.68%	78.76	82.15	80.42
Sparse Enumeration	S-Length	59.16%	83.91	88.77	86.27	47.75%	74.55	83.71	78.87
	M-Length	0.57%	63.97	75.00	69.05	0.13%	67.11	87.93	76.12
	L-Length	0.20%	62.00	57.00	59.00	0.00%	50.00	50.00	50.00
	Total	59.93%	82.73	87.73	85.16	47.89%	74.46	83.75	78.83

Table 7. Performance on Single and Double Enumeration.

		ACE2005				GENIA
		RRP	P(%)	R(%)	F(%)	RRP	P(%)	R(%)	F(%)
Single enumeration	[1]	56.32%	86.93	69.15	77.03	45.64%	82.13	58.10	68.06
	[3]	8.90%	80.82	80.82	80.82	14.58%	62.66	81.51	70.85
	[5]	2.83%	72.35	86.50	78.79	2.10%	56.87	85.94	68.45
	[7]	1.20%	71.91	87.03	78.75	0.41%	54.81	85.51	66.80
Double enumeration	[1, 3]	65.22%	85.81	79.61	82.59	60.23%	80.60	77.72	79.13
	[1, 5]	59.16%	82.90	84.93	83.90	47.75%	75.68	81.87	78.65
	[3, 5]	11.74%	81.14	86.30	83.64	16.69%	72.27	83.15	77.32
	[3, 7]	10.11%	81.62	86.80	84.13	15.00%	72.23	83.65	77.52

Figure 2. Performance on Parallel Granularities.

Figure 3. Performance on Stacked Granularities.

Figure 4. Performance on Multi-granularities.

Table 8. Evaluated on the CONLL2003 and OntoNote 5.0 Corpora.

		CONLL2003			OntoNote 5.0
	Model	P(%)	R(%)	F(%)	P(%)	R(%)	F(%)
Ma and Hovy (2016)	Bi-LSTM	–	–	91.03	86.04	86.53	86.28
Ghaddar and Langlais (2018)	Bi-LSTM	–	–	91.37	–	–	87.95
Batbaatar and Ryu (2019)	BiLSTM-CRF	82.21	70.43	75.86	77.56	64.79	70.60
Devlin et al. (2019 june)	BERT	–	–	92.8	90.01	88.35	89.16
Li et al. (2020)	MRC	92.33	94.61	93.04	92.98	89.95	91.11
Yu et al. (2020)	Biaffine	93.7	93.3	93.5	91.1	91.5	91.3
Luo et al. (2020)	Bi-LSTM	–	–	93.37	–	–	90.37
Li et al. (2022)	span-based	92.71	93.44	93.07	90.03	90.97	90.50
Ours		92.79	93.43	93.11	89.09	90.33	89.68

Table 9. Influence of Region Proposal on the CONLL2003 and OntoNote 5.0 Corpora.

		CONLL2003			OntoNote5.0
Region Proposal		P(%)	R(%)	F(%)	P(%)	R(%)	F(%)
Exhaustive	$[1,2,\dots ,M]$	92.55	93.76	93.15	90.14	90.41	90.27
Interval (k=1)	$[1,3,\dots ,M]$	92.62	93.14	92.88	88.11	89.52	88.81
Interval (k=2)	$[1,4,\dots ,M]$	92.22	93.41	92.81	88.30	89.35	88.82
Series	$[1,2,4,8,16,32]$	92.79	93.43	93.11	89.03	90.33	89.68
Sparse	$[1,5,9,15]$	90.62	93.40	91.99	87.94	88.63	88.28
Single	[1]	93.65	74.21	82.80	90.14	72.57	80.41
	[2]	86.17	83.49	84.81	87.49	82.95	85.16
	[5]	80.35	87.39	83.72	81.12	84.56	82.80
	[7]	75.69	86.43	80.70	78.51	85.31	81.77
Double	[1, 3]	91.84	88.52	90.15	89.46	87.61	88.53
	[1, 5]	89.47	89.93	89.70	88.84	88.45	88.64
	[3, 5]	87.66	91.27	89.43	85.98	89.49	87.70
	[3, 7]	88.04	89.93	88.97	85.13	89.39	87.21

Table 10. Complexity Ratio Between Different Region Proposals.

Region Proposals	Complexity Ratio	Region Proposals	Complexity Ratio
Exhaustive	1.0	Interval (k=1)	0.5294
Interval (k=2)	0.3752	Series	0.2251
Sparse	0.1333	Single	0.0384
Double	0.0752

Table 11. The Number of Model Parameter.

Model	#Param.	Model	#Param.
Li et al. (2022)	112.3M	Wang et al. (2021)	116.7M
Yan et al. (2021)	408.4M	Chen et al. (2022)	352.7M
Ours (Multi-granular BR)	499.0M	Ours (BERT+Single-granular)	372.2M

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