A Probabilistic Model for Japanese Zero Pronoun Resolution Integrating Syntactic and Semantic Features

This paper proposes a method to resolve Japanese zero pronouns by identifying their antecedents. Our method uses a probabilistic model, which is decomposed into syntactic and semantic properties. A syntactic model is trained based on corpora annotated with anaphoric relations. However, a semantic model is trained based on a large-scale unannotated corpus, so as to counter the data sparseness problem. We also propose the notion of certainty to improve the accuracy of zero pronoun resolution. We show the effectiveness of our proposed method by way of experiments.