We propose an unsupervised Expectation Maximization approach to pronoun resolution. The system learns from a \ufb01xed 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
We propose an unsupervised Expectation Maximization approach to pronoun resolution. The system learns from a \ufb01xed 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. 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