Entity-Based Cross-Document Coreferencing Using the Vector Space Model

Cross-document coreference occurs when the same person, place, event, or concept is discussed in more than one text source. Computer recognition of this phenomenon is important because it helps break "the document boundary" by allowing a user to examine information about a particular entity from multiple text sources at the same time. In this paper we describe a cross-document coreference resolution algorithm which uses the Vector Space Model to resolve ambiguities between people having the same name. In addition, we also describe a scoring algorithm for evaluating the cross-document coreference chains produced by our system and we compare our algorithm to the scoring algorithm used in the MUC6 (within document) coreference task. 1 I n t r o d u c t i o n Cross-document coreference occurs when the same person, place, event, or concept is discussed in more than one text source. Computer recognition of this phenomenon is important because it helps break "the document boundary" by allowing a user to examine information about a particular entity from multiple text sources at the same time. In particular, resolving cross-document coreferences allows a user to identify trends and dependencies across documents. Cross-document coreference can also be used as the central tool for producing summaries from multiple documents, and for information fusion, both of which have been identified as advanced areas of research by the T I P S T E R Phase III program. Cross-document coreference was also identified as one of the potential tasks for the Sixth Message Understanding Conference (MUC-6) but was not included as a formal task because it was considered too ambitious (Grishman 94). In this paper we describe a highly successful crossdocument coreference resolution algorithm which uses the Vector Space Model to resolve ambiguities between people having the same name. In addition, we also describe a scoring algorithm for evaluating the cross-document coreference chains produced by our system and we compare our algorithm to the scoring algorithm used in the MUC-6 (within document) coreference task. 2 C r o s s D o c u m e n t Coreference : T h e P r o b l e m Cross-document corefereuce is a distinct technology from Named Entity recognizers like IsoQuest 's NetOwl and IBM's Textract because it a t tempts to determine whether name; matches are actually the same individual (not all John Smiths are the same). Neither NetOwl or Textract have mechanisms which try to keep same-named individuals distinct if they are different people. Cross-document coreference also differs in substantial ways from within-document coreference. Within a document there is a certain amount of consistency which cannot be expected across documents. In addition, the problems encountered during within document coreference are compounded when looking for coreferences across documents because the underlying principles of linguistics and discourse context no longer apply across documents. Because the underlying assumptions in crossdocument coreference are so distinct, they require novel approaches. 3 A r c h i t e c t u r e and the M e t h o d o l o g y Figure 1 shows the architecture of the crossdocument system developed. The system is built upon the University of Pennsylvania's within document coreference system, CAMP, which participated in the Seventh Message Understanding Conference (MUC-7) within document coreference task (MUC7 1998). Our system takes as input the coreference processed documents output by CAMP. It then passes these documents through the SentenceExtractor module which extracts, for each document, all the sentences relevant to a particular entity of interest. The VSM-Disambiguate module then uses a vector space model algorithm to compute similarities between the sentences extracted for each pair of documents.