基于维基百科和模式聚类的实体关系抽取方法

该文提出了一种基于维基百科和模式聚类的方法,旨在从开放文本中抽取高准确率的中文关系实体对。首次使用从人工标注知识体系知网到维基百科实体映射的方式获取关系实例,并且充分利用了维基百科的结构化特性,该方法很好地解决了实体识别的问题,生成了准确而显著的句子实例;进一步,提出了显著性假设和关键词假设,在此基础上构建基于关键词的分类及层次聚类算法,显著提升了模式的可信度。实验结果表明该方法有效提升了句子实例及模式的质量,获得了良好的抽取性能。

A Entity Relation Extraction Method Based on Wikipedia and Pattern Clustering

This paper proposes a method to extract Chinese entity relations of high accuracy from open text based on Wikipedia and pattern clustering.We get relation instances by a mapping from HowNet to Wikipedia and via the structural characteristics of Wikipedia.Based on these,the method solves the entity recognition and generates significant sentence instances.Furthermore,significance assumption and keyword assumption are proposed to support classification and hierarchy clustering algorithm for pattern reliability.The results show that the method achieves a good performance with high-quality seeds and patterns.