State-of-the-art Chinese zero pronoun resolution systems are supervised, thus relying on training data containing manually resolved zero pronouns. To eliminate the reliance on annotated data, we present a generative model for unsupervised Chinese zero pronoun resolution. At the core of our model is a novel hypothesis: a probabilistic pronoun resolver trained on overt pronouns in an unsupervised manner can be used to resolve zero pronouns. Experiments demonstrate that our unsupervised model rivals its state-ofthe-art supervised counterparts in performance when resolving the Chinese zero pronouns in the OntoNotes corpus.
As mentioned before, in order to reduce reliance on annotated data, we recently proposed an unsupervised probabilistic model for Chinese AZP resolution that rivaled its supervised counterparts in performance REF ).
Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers
EMNLP
Computer Science