Abstract
In Japanese, entities which can easily be predicted are often omitted. Identifying appropriate antecedents associated with those ellipses, which is termed “anaphora resolution”, is crucial in natural language processing, specifically, a discourse analysis. This paper proposes a probabilistic model to resolve zero pronouns, which are one of the major ellipses in Japanese. Our proposing model can be decomposed into two models associated with syntactic and semantic properties, so as to optimize a parameter estimation. A syntactic model is trained based on corpora annotated with anaphoric relations. However, a semantic model is trained based on a largescale unannotated corpora to counter the data sparseness problem. We also propose a notion of certainty to improve the accuracy of zero pronoun resolution. We show the effectiveness of our method by way of experiments.
