A hybrid method for entity hyponymy acquisition in Chinese complex sentences

Abstract

Extracting entity hyponymy in Chinese complex sentences can be a highly difficult process. This paper proposes a novel hybrid approach that combines parsing with supervised learning and semi-supervised learning. First, conditional random fields (CRF) model is employed to obtain the candidate domain named entity. Pattern matching is then used to acquire candidate hyponymy. Next, predicate and symbol features, syntactic analysis, and semantic roles are introduced into the CRF features template to identify the hyponymy entity pairs. Finally, analysis of both the parallel relationship of entities among sentences and entity pairs in simple sentences is conducted to obtain the hyponymy entity pairs in Chinese complex sentences. The experimental results show that the proposed method reduces the manual work required for CRF markers and has an improved overall performance in comparison with the baseline methods.