An Expectation Maximization Approach to Pronoun Resolution
- Colin Cherry ,
- Shane Bergsma
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005) |
Published by Association for Computational Linguistics
We propose an unsupervised Expectation Maximization approach to pronoun resolution. The system learns from a fixed list of potential antecedents for each pronoun. We show that unsupervised learning is possible in this context, as the performance of our system is comparable to supervised methods. Our results indicate that a probabilistic gender/number model, determined automatically from unlabeled text, is a powerful feature for this task.