面向领域知识图谱的实体关系联合抽取

doi:10.3969/j.issn.1000-5641.2021.05.003

摘要/Abstract

摘要：

文本数据中的实体和关系抽取是领域知识图谱构建和更新的来源. 针对金融科技领域中文本数据存在重叠关系、训练数据缺乏标注样本等问题, 提出一种融合主动学习思想的实体关系联合抽取方法. 首先, 基于主动学习, 以增量的方式筛选出富有信息量的样本作为训练数据; 其次, 采用面向主实体的标注策略将实体关系联合抽取问题转化为序列标注问题; 最后, 基于改进的BERT-BiGRU-CRF模型实现领域实体与关系的联合抽取, 为知识图谱构建提供支撑技术, 有助于金融从业者根据领域知识进行分析、投资、交易等操作, 从而降低投资风险. 针对金融领域文本数据进行实验测试, 实验结果表明, 本文所提出的方法有效, 验证了该方法后续可用于金融知识图谱的构建.

关键词: 领域文本, 领域知识图谱, 实体关系联合抽取, 主动学习, 序列标注

Abstract:

Extraction of entities and relationships from text data is used to construct and update domain knowledge graphs. In this paper, we propose a method to jointly extract entities and relations by incorporating the concept of active learning; the proposed method addresses problems related to the overlap of vertical domain data and the lack of labeled samples in financial technology domain text data using the traditional approach. First, we select informative samples incrementally as training data sets. Next, we transform the exercise of joint extraction of entities and relations into a sequence labeling problem by labelling the main entities. Finally, we fulfill the joint extraction using the improved BERT-BiGRU-CRF model for construction of a knowledge graph, and thus facilitate financial analysis, investment, and transaction operations based on domain knowledge, thereby reducing investment risks. Experimental results with finance text data shows the effectiveness of our proposed method and verifies that the method can be successfully used to construct financial knowledge graphs.

Key words: domain text, domain knowledge graph, joint extration of entities and relations, active learning, sequence labeling

中图分类号:

TP391

付瑞, 李剑宇, 王笳辉, 岳昆, 胡矿. 面向领域知识图谱的实体关系联合抽取[J]. 华东师范大学学报（自然科学版）, 2021, 2021(5): 24-36.

Rui FU, Jianyu LI, Jiahui WANG, Kun YUE, Kuang HU. Joint extraction of entities and relations for domain knowledge graph[J]. Journal of East China Normal University(Natural Science), 2021, 2021(5): 24-36.

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数据集	实体数量	关系类别	关系数量
金融领域	5034	/person/company	2064
		/company/founders	557
		/company/place_founded	243
		/company/advisors	47
		/major_shareholder_of	304
少数民族领域	4722	饮食	434
		人口	120
		别名	223
		居住地	557
		宗教	231
		语言	220
		工艺	171
		歌舞	470
		文学作品	279
		习俗	263
		服饰	440
		建筑	214

数据集	卷积核尺寸	卷积核个数	learning_rate	batch_size	GRU units	dropout_rate
金融领域数据集	2, 3, 4	64	1e?3	32	256	0.5
少数民族领域数据集	3, 4, 5	128	5e?3	32	256	0.4

模型	命名实体识别			关系抽取
模型	P	R	F₁	P	R	F₁
BERT-BiLSTM-CRF+RBert	0.7163	0.7130	0.7146	0.5001	0.3439	0.4075
Word2vec-BiLSTM-CRF	0.6932	0.6313	0.6608	0.4185	0.4231	0.4207
Word2vec-BiGRU-CRF	0.6770	0.7083	0.6923	0.4611	0.3824	0.4182
Word2vec-BiGRU*-CRF	0.6983	0.6874	0.6928	0.4523	0.4111	0.4307
BERT-BiLSTM-CRF	0.7346	0.7176	0.7260	0.4806	0.4859	0.4832
BERT-BiGRU-CRF	0.7600	0.7037	0.7308	0.5032	0.4593	0.4802
BERT- BiGRU*-CRF	0.7530	0.7353	0.7440	0.4952	0.4787	0.4868

模型	命名实体识别			关系抽取
模型	P	R	F₁	P	R	F₁
BERT-BiLSTM-CRF+RBert	0.8579	0.8513	0.8546	0.7578	0.3715	0.4986
Word2vec-BiLSTM-CRF	0.7549	0.7821	0.7682	0.5461	0.6132	0.5777
Word2vec-BiGRU-CRF	0.7649	0.7713	0.7680	0.6401	0.5258	0.5773
Word2vec-BiGRU*-CRF	0.7761	0.7894	0.7827	0.6261	0.5739	0.5988
BERT-BiLSTM-CRF	0.8674	0.9173	0.8916	0.7761	0.6194	0.6889
BERT-BiGRU-CRF	0.8688	0.9285	0.8976	0.6561	0.7188	0.6860
BERT- BiGRU*-CRF	0.9121	0.9313	0.9216	0.7209	0.6983	0.7094

模型	金融领域			少数民族领域
模型	P	R	F₁	P	R	F₁
BERT-BiLSTM-CRF+RBert	0.3526	0.1528	0.2132	0.5714	0.3636	0.4444
Word2vec-BiLSTM-CRF	0.4259	0.3126	0.3606	0.4914	0.5969	0.5390
Word2vec-BiGRU-CRF	0.4116	0.3150	0.3569	0.4656	0.6250	0.5351
Word2vec-BiGRU*-CRF	0.4234	0.3203	0.3647	0.4815	0.6500	0.5532
BERT-BiLSTM-CRF	0.5107	0.3495	0.4150	0.5492	0.7114	0.6199
BERT-BiGRU-CRF	0.4923	0.3495	0.4135	0.6111	0.6250	0.6180
BERT- BiGRU*-CRF	0.5312	0.3564	0.4213	0.6200	0.6566	0.6378