基于三支决策的两阶段实体关系抽取研究

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

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。

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.