An Expectation Maximization Approach to Pronoun Resolution - Cherry, Colin; Bergsma, Shane (Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), 2005)
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.