基于主体掩码的实体关系抽取方法

doi:10.11959/j.issn.2096-0271.2021022

Abstract

Abstract:

Entity relationship extraction technology can automatically extract information from massive unstructured texts to construct large-scale knowledge graph, enrich the content of existing knowledge graph, and provide support for reasoning and application of knowledge graph.Although the cascading entity relation extraction technology has achieved good results, it has some shortcomings in the vector representation of the subject and the decoding of pointer network.In order to solve the representation problem of subject vectors, attention mechanism and mask mechanism were used to generate subject vectors.In addition, to solve the problem that long entities have been decoded in pointer network due to missing label, an entity sequence marker task was introduced to assist pointer network decoding entities.There is a 0.88% improvement over the previous model on the large-scale entity relationship dataset DuIE 2.0.

Key words: RoBERTa, entity relation extraction, subject mask

CLC Number:

TP391

Shenpeng ZHENG, Xiaojun CHEN, Yang XIANG, Ruchao SHEN. An entity relation extraction method based on subject mask[J]. Big Data Research, 2021, 7(3): 3-14.

Figures/Tables 5

References 24

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难点	类型	样例	样例分析
实体嵌套	共享前缀	他的自传《李敖自传》	“李敖”与“李敖自传”
	共享后缀	抗日剧《我的兄弟叫顺溜》	“我的兄弟叫顺溜”与“顺溜”
	包含	《百变梅艳芳之烈焰红唇》	“梅艳芳”与“百变梅艳芳之烈焰红唇”
关系重叠	实体在多个关系中	陈奕迅与妻子徐濠萦共同演唱了《Style》	<Style,歌手,陈奕迅>
			<陈奕迅,妻子,徐濠萦>
	实体间有多个关系	《无路用的人》是张震岳作词作曲的	<无路用的人,作词人,张震岳>
			<无路用的人,作曲人,张震岳>

模型	Dev-精确率	Dev-召回率	Dev-F1	Test-F1
Official	-	-	-	0.710 6
Pipline	0.715 2	0.748 8	0.731 7	0.720 3
Mean	0.706 0	0.788 6	0.745 0	0.742 0
Max	0.702 8	0.795 0	0.746 0	0.742 7
Transf	0.719 2	0.786 3	0.751 3	0.747 5
Transf + seq	0.742 2	0.766 4	0.754 1	0.750 8

	安	徽	四	创	电	子	股	份	有	限	公	司
安	0.06	0.09	0.14	0.02	0.03	0.02	0.06	0.05	0.06	0.03	0.24	0.18
徽	0.14	0.03	0.05	0.01	0.03	0.01	0.08	0.05	0.09	0.06	0.25	0.19
…	…	…	…	…	…	…	…	…	…	…	…	…
1	0.06	0.07	0.03	0.04	0.04	0.03	0.10	0.13	0.14	0.16	0.13	0.07
.	0.11	0.03	0.05	0.01	0.03	0.02	0.12	0.11	0.11	0.15	0.17	0.11
59	0.10	0.04	0.04	0.02	0.02	0.03	0.13	0.11	0.14	0.12	0.15	0.10
亿	0.03	0.03	0.08	0.06	0.06	0.04	0.12	0.09	0.20	0.16	0.10	0.03
…	…	…	…	…	…	…	…	…	…	…	…	…

An entity relation extraction method based on subject mask

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