An Expectation Maximization Approach to Pronoun Resolution

Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL), pages 88–95, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics An Expectation Maximization Approach to Pronoun Resolution Colin Cherry and Shane Bergsma Department of Computing Science University of Alberta Edmonton, Alberta, Canada, T6G 2E8 {colinc,bergsma}@cs.ualberta.ca Abstract We propose an unsupervised Expectation Maximization approach to pronoun reso- lution. The system learns from a fixed list of potential antecedents for each pro- noun. We show that unsupervised learn- ing is possible in this context, as the per- formance 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. 1 Introduction Coreference resolution is the process of determin- ing which expressions in text refer to the same real- world entity. Pronoun resolution is the important yet challenging subset of coreference resolution where a system attempts to establish coreference between a pronominal anaphor, such as a third-person pronoun like he, she, it, or they, and a preceding noun phrase, called an antecedent. In the following example, a pronoun resolution system must determine the cor- rect antecedent for the pronouns “his” and “he.” (1) When the president entered the arena with his family, he was serenaded by a mariachi band. Pronoun resolution has applications across many areas of Natural Language Processing, particularly in the field of information extraction. Resolving a pronoun to a noun phrase can provide a new inter- pretation of a given sentence, giving a Question An- swering system, for example, more data to consider. Our approach is a synthesis of linguistic and sta- tistical methods. For each pronoun, a list of an- tecedent candidates derived from the parsed corpus is presented to the Expectation Maximization (EM) learner. Special cases, such as pleonastic, reflex- ive and cataphoric pronouns are dealt with linguisti- cally during list construction. This allows us to train on and resolve all third-person pronouns in a large Question Answering corpus. We learn lexicalized gender/number, language, and antecedent probabil- ity models. These models, tied to individual words, can not be learned with sufficient coverage from la- beled data. Pronouns are resolved by choosing the most likely antecedent in the candidate list accord- ing to these distributions. The resulting resolution accuracy is comparable to supervised methods. We gain further performance improvement by ini- tializing EM with a gender/number model derived from special cases in the training data. This model is shown to perform reliably on its own. We also demonstrate how the models learned through our un- supervised method can be used as features in a su- pervised pronoun resolution system. 2 Related Work Pronoun resolution typically employs some com- bination of constraints and preferences to select the antecedent from preceding noun phrase candi- dates. Constraints filter the candidate list of improb- able antecedents, while preferences encourage se- lection of antecedents that are more recent, frequent, etc. Implementation of constraints and preferences can be based on empirical insight (Lappin and Le- ass, …

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