Chinese Entity Synonym Extraction from the Web

Abstract

Entity synonyms play an important role in natural language processing applications, such as query expansion and question answering. There are three main distribution characteristics in texts on the web: (1) appearing in parallel structures; (2) occurring with specific patterns in sentences; and (3) distributed in similar contexts. These characteristics are largely complementary. Existing methods, such as pattern-based and context-based methods, only consider one characteristic for synonym extraction and ignore the complementarity among them. For increasing accuracy and recall, we propose a novel method that integrates the three characteristics for extracting synonyms from the web, where Entity Synonym Network (ESN) is built to incorporate synonymous knowledge. To further improve accuracy, we treat synonym detection as a ranking problem and use the Spreading Activation model as a ranking means to detect the hard noise in ESN. Experimental results show our method achieves better accuracy and recall than the state-of-the-art methods.