A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1169–1178, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models Dingcheng Li University of Minnesota, Twin Cities, Minnesosta lixxx345@umn.edu Tim Miller University of Wisconsin Milwaukee, Wisconsin tmill@cs.umn.edu William Schuler The Ohio State University Columbus, Ohio schuler@ling.osu.edu Abstract This paper presents a supervised pronoun anaphora resolution system based on factorial hidden Markov models (FHMMs). The ba- sic idea is that the hidden states of FHMMs are an explicit short-term memory with an an- tecedent buffer containing recently described referents. Thus an observed pronoun can find its antecedent from the hidden buffer, or in terms of a generative model, the entries in the hidden buffer generate the corresponding pro- nouns. A system implementing this model is evaluated on the ACE corpus with promising performance. 1 Introduction Pronoun anaphora resolution is the task of find- ing the correct antecedent for a given pronominal anaphor in a document. It is a subtask of corefer- ence resolution, which is the process of determin- ing whether two or more linguistic expressions in a document refer to the same entity. Adopting ter- minology used in the Automatic Context Extraction (ACE) program (NIST, 2003), these expressions are called mentions. Each mention is a reference to some entity in the domain of discourse. Men- tions usually fall into three categories – proper men- tions (proper names), nominal mentions (descrip- tions), and pronominal mentions (pronouns). There is a great deal of related work on this subject, so the descriptions of other systems below are those which are most related or which the current model has drawn insight from. Pairwise models (Yang et al., 2004; Qiu et al., 2004) and graph-partitioning methods (McCallum and Wellner, 2003) decompose the task into a col- lection of pairwise or mention set coreference de- cisions. Decisions for each pair or each group of mentions are based on probabilities of features extracted by discriminative learning models. The aforementioned approaches have proven to be fruit- ful; however, there are some notable problems. Pair- wise modeling may fail to produce coherent parti- tions. That is, if we link results of pairwise deci- sions to each other, there may be conflicting corefer- ences. Graph-partitioning methods attempt to recon- cile pairwise scores into a final coherent clustering, but they are combinatorially harder to work with in discriminative approaches. One line of research aiming at overcoming the limitation of pairwise models is to learn a mention- ranking model to rank preceding mentions for a given anaphor (Denis and Baldridge, 2007) This ap- proach results in more coherent coreference chains. Recent years have also seen the revival of in- terest in generative models in both machine learn- ing and natural language processing. Haghighi and Klein (2007), proposed an unsupervised non- parametric Bayesian model for coreference resolu- tion. In contrast to pairwise models, this fully gener- ative model produces each mention from a combina- tion of global entity …

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