Improving Definite Anaphora Resolution by Effective Weight Learning and Web-Based Knowledge Acquisition

Abstract

In this paper, effective Chinese definite anaphora resolution is addressed by using feature weight learning and Web-based knowledge acquisition. The presented salience measurement is based on entropy-based weighting on selecting antecedent candidates. The knowledge acquisition model is aimed to extract more semantic features, such as gender, number, and semantic compatibility by employing multiple resources and Web mining. The resolution is justified with a real corpus and compared with a classification-based model. Experimental results show that our approach yields 72.5% success rate on 426 anaphoric instances. In comparison with a general classification-based approach, the performance is improved by 4.7%.