Resolving and Generating Definite Anaphora by Modeling Hypernymy using Unlabeled Corpora

Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X), pages 37–44, New York City, June 2006. c⃝2006 Association for Computational Linguistics Resolving and Generating Definite Anaphora by Modeling Hypernymy using Unlabeled Corpora Nikesh Garera and David Yarowsky Department of Computer Science Center for Language and Speech Processing Johns Hopkins University Baltimore, MD 21218, USA {ngarera,yarowsky}@cs.jhu.edu Abstract We demonstrate an original and success- ful approach for both resolving and gen- erating definite anaphora. We propose and evaluate unsupervised models for ex- tracting hypernym relations by mining co- occurrence data of definite NPs and po- tential antecedents in an unlabeled cor- pus. The algorithm outperforms a stan- dard WordNet-based approach to resolv- ing and generating definite anaphora. It also substantially outperforms recent re- lated work using pattern-based extraction of such hypernym relations for corefer- ence resolution. 1 Introduction Successful resolution and generation of definite anaphora requires knowledge of hypernym and hy- ponym relationships. For example, determining the antecedent to the definite anaphor “the drug” in text requires knowledge of what previous noun-phrase candidates could be drugs. Likewise, generating a definite anaphor for the antecedent “Morphine” in text requires both knowledge of potential hypernyms (e.g. “the opiate”, “the narcotic”, “the drug”, and “the substance”), as well as selection of the most ap- propriate level of generality along the hypernym tree in context (i.e. the “natural” hypernym anaphor). Unfortunately existing manual hypernym databases such as WordNet are very incomplete, especially for technical vocabulary and proper names. Word- Nets are also limited or non-existent for most of the world’s languages. Finally, WordNets also do not include notation of the “natural” hypernym level for anaphora generation, and using the immediate par- ent performs quite poorly, as quantified in Section 5. In first part of this paper, we propose a novel ap- proach for resolving definite anaphora involving hy- ponymy relations. We show that it performs substan- tially better than previous approaches on the task of antecedent selection. In the second part we demon- strate how this approach can be successfully ex- tended to the problem of generating a natural def- inite NP given a specific antecedent. In order to explain the antecedent selection task for definite anaphora clearly, we provide the follow- ing example taken from the LDC Gigaword corpus (Graff et al., 2005). (1)...pseudoephedrine is found in an allergy treat- ment, which was given to Wilson by a doctor when he attended Blinn junior college in Houston. In a unanimous vote, the Norwegian sports confedera- tion ruled that Wilson had not taken the drug to en- hance his performance... In the above example, the task is to resolve the definite NP the drug to its correct antecedent pseudoephedrine, among the potential antecedents <pseudoephedrine, allergy, blinn, college, hous- ton, vote, confederation, wilson>. Only Wilson can be ruled out on syntactic grounds (Hobbs, 1978). To be able to resolve the correct antecedent from the remaining potential antecedents, the system re- quires the knowledge that pseudoephedrine is a drug. Thus, the problem is to create such …

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