This paper presents a probabilistic model for Japanese zero anaphora resolution. First, this model recognizes discourse entities and links all mentions to them. Zero pronouns are then detected by case structure analysis based on automatically constructed case frames. Their appropriate antecedents are selected from the entities with high salience scores, based on the case frames and several preferences on the relation between a zero pronoun and an antecedent. Case structure and zero anaphora relation are simultaneously determined based on probabilistic evaluation metrics.
REF proposed a lexicalized probabilistic model for zero anaphora resolution, which adopted an entity-mention model and simultaneously resolved predicateargument structures and zero anaphora.
A Fully-Lexicalized Probabilistic Model for Japanese Zero Anaphora Resolution
International Conference On Computational Linguistics
Computer Science