Unsupervised Learning of Contextual Role Knowledge for Coreference Resolution

Unsupervised Learning of Contextual Role Knowledge for Coreference Resolution David Bean Attensity Corporation, Suite 600 Gateway One 90 South 400 West Salt Lake City, UT 84101 dbean@attensity.com Ellen Riloff School of Computing University of Utah Salt Lake City, UT 84112 riloff@cs.utah.edu Abstract We present a coreference resolver called BABAR that uses contextual role knowledge to evaluate possible antecedents for an anaphor. BABAR uses information extraction patterns to identify contextual roles and creates four contextual role knowledge sources using unsu- pervised learning. These knowledge sources determine whether the contexts surrounding an anaphor and antecedent are compatible. BABAR applies a Dempster-Shafer probabilis- tic model to make resolutions based on ev- idence from the contextual role knowledge sources as well as general knowledge sources. Experiments in two domains showed that the contextual role knowledge improved corefer- ence performance, especially on pronouns. 1 Introduction The problem of coreference resolution has received con- siderable attention, including theoretical discourse mod- els (e.g., (Grosz et al., 1995; Grosz and Sidner, 1998)), syntactic algorithms (e.g., (Hobbs, 1978; Lappin and Le- ass, 1994)), and supervised machine learning systems (Aone and Bennett, 1995; McCarthy and Lehnert, 1995; Ng and Cardie, 2002; Soon et al., 2001). Most compu- tational models for coreference resolution rely on prop- erties of the anaphor and candidate antecedent, such as lexical matching, grammatical and syntactic features, se- mantic agreement, and positional information. The focus of our work is on the use of contextual role knowledge for coreference resolution. A contextual role represents the role that a noun phrase plays in an event or relationship. Our work is motivated by the observa- tion that contextual roles can be critically important in determining the referent of a noun phrase. Consider the following sentences: (a) Jose Maria Martinez, Roberto Lisandy, and Dino Rossy, who were staying at a Tecun Uman hotel, were kidnapped by armed men who took them to an unknown place. (b) After they were released... (c) After they blindfolded the men... In (b) “they” refers to the kidnapping victims, but in (c) “they” refers to the armed men. The role that each noun phrase plays in the kidnapping event is key to distinguish- ing these cases. The correct resolution in sentence (b) comes from knowledge that people who are kidnapped are often subsequently released. The correct resolution in sentence (c) depends on knowledge that kidnappers fre- quently blindfold their victims. We have developed a coreference resolver called BABAR that uses contextual role knowledge to make coreference decisions. BABAR employs information ex- traction techniques to represent and learn role relation- ships. Each pattern represents the role that a noun phrase plays in the surrounding context. BABAR uses unsu- pervised learning to acquire this knowledge from plain text without the need for annotated training data. Train- ing examples are generated automatically by identifying noun phrases that can be easily resolved with their an- tecedents using lexical and syntactic heuristics. BABAR then computes statistics over the training examples mea- suring the frequency with which extraction patterns and noun phrases …

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