Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (9): 145-150.DOI: 10.3778/j.issn.1002-8331.1710-0153

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Research on two-stage entity relation extraction based on three-way decisions

ZHU Yanhui1,2, LI Fei1,2, HU Junfei1,2, QIAN Jisheng3, WANG Tianji1,2   

  1. 1.School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan 412008, China
    2.Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Hunan University of Technology, Zhuzhou, Hunan 412008, China
    3.The People’s Bank of China Tongling Central Sub-branch, Tongling, Anhui 244000, China
  • Online:2018-05-01 Published:2018-05-15

基于三支决策的两阶段实体关系抽取研究

朱艳辉1,2,李  飞1,2,胡骏飞1,2,钱继胜3,王天吉1,2   

  1. 1.湖南工业大学 计算机学院,湖南 株洲 412008
    2.湖南工业大学 湖南省智能信息感知及处理技术重点实验室,湖南 株洲 412008
    3.中国人民银行 铜陵市中心支行,安徽 铜陵 244000

Abstract: As one of the important research topics in information extraction, entity relationship extraction is of great significance to the construction of knowledge graph data layer. This paper proposes a two-stage classification technique based on three-way decisions to extract the entity relationship. Firstly, the SVM three-decisions classifier is constructed to implement the first phase entity relation extraction. The softmax multi-class function is used as a probability function of three-way decisions, Then, the KNN classifier is used to classify the three-way decisions middle domain sample into two-stage classification. According to the corpus of ACE2005 as the experimental data, the results of the three-way decisions two-stage classification are compared with the traditional SVM method. The experimental results show that the two-stage entity relation extraction method based on three-way decisions has achieved good classification effect.

Key words: entity relation extraction, three-way decisions, Support Vector Machine(SVM), K-Nearest Neighbor(KNN), softmax function

摘要: 实体关系抽取作为信息抽取研究的重要研究课题之一,对知识图谱数据层的构建有着重要的意义。提出一种基于三支决策的两阶段分类技术实现实体关系抽取,首先构建SVM三支决策分类器实现第一阶段实体关系抽取,采用softmax多分类函数作为三支决策概率函数,然后采用KNN分类器对三支决策分类后的中间域样本进行二阶段分类。以ACE2005的语料作为实验数据,将三支决策两阶段分类结果与传统SVM方法分类结果进行比较,实验结果表明,基于三支决策的两阶段实体关系抽取方法取得了很好的分类效果。

关键词: 实体关系抽取, 三支决策, 支持向量机(SVM), K最近邻(KNN), softmax函数