Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers

Chen Chen and Vincent Ng.
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 2014.

Click here for the PDF version. The talk slides are available here.

Abstract

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-of-the-art supervised counterparts in performance when resolving the Chinese zero pronouns in the OntoNotes corpus.

BibTeX entry

@InProceedings{Chen+Ng:14d,
  author = {Chen Chen and Vincent Ng},
  title = {Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing},
  pages = {763--774}, 
  year = 2014}