Chinese Zero Pronoun Resolution

Abstract

Semantic information that has been proven to be necessary to the resolution of common noun phrases is typically ignored by most existing Chinese zero pronoun resolvers. This is because that zero pronouns convey no descriptive information, which makes it almost impossible to calculate semantic similarities between the zero pronoun and its candidate antecedents. Moreover, most of traditional approaches are based on the single-candidate model, which considers the candidate antecedents of a zero pronoun in isolation and thus overlooks their reciprocities. To address these problems, we first propose a neural-network-based zero pronoun resolver ( NZR ) that is capable of generating vector-space semantics of zero pronouns and candidate antecedents. On the basis of NZR , we develop the collaborative filtering-based framework for Chinese zero pronoun resolution task, exploring the reciprocities between the candidate antecedents of a zero pronoun to more rationally re-estimate their importance. Experimental results on the Chinese portion of the OntoNotes 5.0 corpus are encouraging: Our proposed model substantially surpasses the Chinese zero pronoun resolution baseline systems.