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

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