TAILIEUCHUNG - Báo cáo khoa học: "A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models"

This paper presents a supervised pronoun anaphora resolution system based on factorial hidden Markov models (FHMMs). The basic idea is that the hidden states of FHMMs are an explicit short-term memory with an antecedent 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 pronouns. | A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models Dingcheng Li University of Minnesota Twin Cities Minnesosta lixxx345@ Tim Miller University of Wisconsin Milwaukee Wisconsin tmill@ William Schuler The Ohio State University Columbus Ohio schuler@ Abstract This paper presents a supervised pronoun anaphora resolution system based on factorial hidden Markov models FHMMs . The basic idea is that the hidden states of FHMMs are an explicit short-term memory with an antecedent 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 pronouns. A system implementing this model is evaluated on the ACE corpus with promising performance. 1 Introduction Pronoun anaphora resolution is the task of finding the correct antecedent for a given pronominal anaphor in a document. It is a subtask of coreference resolution which is the process of determining whether two or more linguistic expressions in a document refer to the same entity. Adopting terminology 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. Mentions usually fall into three categories - proper mentions proper names nominal mentions descriptions 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 1169 and Wellner 2003 decompose the task into a collection of pairwise or mention set coreference decisions. Decisions for each pair or each group of mentions are based on probabilities of features extracted by discriminative learning models. The .

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