关键词: 上下位关系, 语境相似度, 布朗聚类相似度, 点互信息, 模式匹配, 聚类验证

Abstract: Hyponymy has many important applications in the field of Natural Language Processing (NLP) and the automatic extraction of hyponym relation from massive text datasets is naturally one of important NLP research tasks. The emphasis and difficult point of the research is how to validate a hyponym which is extracted with simple pattern matching method is really correct. By calculating the context feature similarity ( SimCF ) and Brown clustering similarity (SimBrown ), this paper proposes a novel approach of hyponymy validation. It applies a clustering on hyponym candidates,and the clustering similarity feature is obtained by combining SimCF and SimBrown. The combination coefficient of two kinds of similarity is derived based on the SimCFs and SimBrowns between all labeled training words and their hyponyms. The model can filter roughly extraction results without any existed lexical relation dictionary or knowledge base. Evaluation on CCF NLP&CC2012 word semantic relation corpus shows that the proposed approach in this paper significantly improves the F measure value compared with other approaches including pattern matching and simple context comparison.

Key words: hyponymy relation, context similarity, Brown clustering similarity, Point Mutual Information ( PMI ), pattern matching, clustering validation