Noun Phrase Coreference as Clustering

This paper introduces a new, unsupervised algorithm for noun phrase coreference resolution. It differs from existing methods in that it views corererence resolution as a clustering task. In an evaluation on the MUC-6 coreference resolution corpus, the algorithm achieves an F-measure of 53.6%~ placing it firmly between the worst (40%) and best (65%) systems in the MUC-6 evaluation. More importantly, the clustering approach outperforms the only MUC-6 system to treat coreference resolution as a learning problem. The clustering algorithm appears to provide a flexible mechanism for coordinating the application of context-independent and context-dependent constraints and preferences for accurate partitioning of noun phrases into coreference equivalence classes. 1 I n t r o d u c t i o n Many natural language processing (NLP) applications require accurate noun phrase coreference resolution: They require a means for determining which noun phrases in a text or dialogue refer to the same real-world entity. The vast majority of algorithms for noun phrase coreference combine syntactic and, less often, semantic cues via a set of hand-crafted heuristics and filters. All but one system in the MUC-6 coreference performance evaluation (MUC, 1995), for example, handled coreference resolution in this manner. This same reliance on complicated hand-crafted algorithms is true even for the narrower task of pronoun resolution. Some exceptions exist, however. Ge et al. (1998) present a probabilistic model for pronoun resolution trained on a small subset of the Penn Treebank Wall Street Journal corpus (Marcus et al., 1993). Dagan and Itai (1991) develop a statistical filter for resolution of the pronoun "it" that selects among syntactically viable antecedents based on relevant subject-verb-object cooccurrences. Aone and Bennett (1995) and McCarthy and Lehnert (1995) employ decision tree algorithms to handle a broader subset of general noun phrase coreference problems. This paper presents a new corpus-based approach to noun phrase coreference. We believe that it is the first such unsupervised technique developed for the general noun phrase coreference task. In short, we view the task of noun phrase coreference resolution as a clustering task. First, each noun phrase in a document is represented as a vector of attribute-value pairs. Given the feature vector for each noun phrase, the clustering algorithm coordinates the application of context-independent and context-dependent coreference constraints and preferences to partition the noun phrases into equivalence classes, one class for each real-world entity mentioned in the text. Context-independent coreference constraints and preferences are those that apply to two noun phrases in isolation. Context-dependent coreference decisions, on the other hand, consider the relationship of each noun phrase to surrounding noun phrases. In an evaluation on the MUC-6 coreference resolution corpus, our clustering approach achieves an F-measure of 53.6%, placing it firmly between the worst (40%) and best (65%) systems in the MUC6 evaluation. More importantly, the clustering approach outperforms the only MUC-6 system to view coreference resolution as a learning problem: The RESOLVE system (McCarthy and Lehnert, 1995) employs decision tree induction and achieves an Fmeasure of 47% on the MUC-6 data set. Furthermore, our approach has a number of important advantages over existing learning and non-learning methods for coreference resolution: • The approach is largely unsupervised, so no annotated training corpus is required. • Although evaluated in an information extraction context, the approach is domainindependent. • As noted above, the clustering approach provides a flexible mechanism for coordinating context-independent and context-dependent coreference constraints and preferences for partitioning noun phrases into coreference equivalence classes.