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.
CITATION STYLE
Chen, C., & Ng, V. (2014). Chinese zero pronoun resolution: An unsupervised probabilistic model rivaling supervised resolvers. In EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 763–774). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/d14-1084
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