关键词: 中文专利依存树库, 开放式实体关系抽取, Markov逻辑网

Abstract: The main goal of information extraction is to transform unstructured or semi-structured texts into structured information, in which entity relation extraction is a major task. In general, traditional methods require pre-specified relation types. But pre-defined rules and manual labels are not adaptive to massive texts. Recently, open information extraction can solve the problems properly. In contrast with the significant achievements concerning English and other Western languages, research on Chinese open relation extraction is quite scarce. The hierarchical Chinese open entity relation extraction approach is proposed that applies Markov Logic Networks（MLN） on the base of both external and internal chunk-tags. The experimental results reveal that the origin of chunks can simplify the understanding of sentences, and both layers can be handled consistently so that engineering efforts are reduced. And on the same conditions, MLN can perform better than SVM, in which the F-score of external and internal layers can reach 77.92% and 69.20% respectively.

Key words: Chinese patents dependency treebank, open entity relation extraction, Markov Logic Networks（MLN）