Corpus-Based Identification of Non-Anaphoric Noun Phrases

Corpus-Based Identification of Non-Anaphoric Noun Phrases David L. Bean and Ellen Riloff Department of Computer Science University of Utah Salt Lake City, Utah 84112 {bean,riloff}@cs.utah.edu Abstract Coreference resolution involves finding antecedents for anaphoric discourse entities, such as definite noun phrases. But many definite noun phrases are not anaphoric because their meaning can be un- derstood from general world knowledge (e.g., "the White House" or "the news media"). We have developed a corpus-based algorithm for automat- ically identifying definite noun phrases that are non-anaphoric, which has the potential to improve the efficiency and accuracy of coreference resolu- tion systems. Our algorithm generates lists of non- anaphoric noun phrases and noun phrase patterns from a training corpus and uses them to recognize non-anaphoric noun phrases in new texts. Using 1600 MUC-4 terrorism news articles as the training corpus, our approach achieved 78% recall and 87% precision at identifying such noun phrases in 50 test documents. 1 Introduction Most automated approaches to coreference res- olution attempt to locate an antecedent for ev- ery potentially coreferent discourse entity (DE) in a text. The problem with this approach is that a large number of DE's may not have an- tecedents. While some discourse entities such as pronouns are almost always referential, def- inite descriptions I may not be. Earlier work found that nearly 50% of definite descriptions had no prior referents (Vieira and Poesio, 1997), and we found that number to be even higher, 63%, in our corpus. Some non-anaphoric def- inite descriptions can be identified by looking for syntactic clues like attached prepositional phrases or restrictive relative clauses. But other definite descriptions are non-anaphoric because readers understand their meaning due to com- mon knowledge. For example, readers of this 1In this work, we define a definite description to be a noun phrase beginning with the. paper will probably understand the real world referents of "the F.B.I.," "the White House," and "the Golden Gate Bridge." These are in- stances of definite descriptions that a corefer- ence resolver does not need to resolve because they each fully specify a cognitive representa- tion of the entity in the reader's mind. One way to address this problem is to cre- ate a list of all non-anaphoric NPs that could be used as a filter prior to coreference resolu- tion, but hand coding such a list is a daunt- ing and intractable task. We propose a corpus- based mechanism to identify non-anaphoric NPs automatically. We will refer to non-anaphoric definite noun phrases as existential NPs (Allen, 1995). Our algorithm uses statistical methods to generate lists of existential noun phrases and noun phrase patterns from a training corpus. These lists are then used to recognize existen- tial NPs in new texts. 2 Prior Research Computational coreference resolvers fall into two categories: systems that make no at- tempt to identify non-anaphoric discourse en- tities prior to coreference resolution, and those that apply a filter to discourse entities, identify- ing a subset of them that are anaphoric. Those that do not practice …

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