1998 EntityBasedCrossDocCorefSVM

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Subject Headings: Multi-Document Coreference Resolution Task, Multi-Document Coreference Resolution Algorithm.

Notes

Cited By

  • Amit Bagga and A. W. Biermann. (2000). “A Methodology for Cross-Document Coreference.” In: Proceedings of the Fifth Joint Conference on Information Sciences (JCIS 2000).

Quotes

Abstract

  • 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 MUC-6 (within document) coreference task.

1. Introduction

  • 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.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
1998 EntityBasedCrossDocCorefSVMAmit Bagga
Breck Baldwin
Entity-based Cross-Document Coreferencing Using the Vector Space ModelProceedings of the 17th International Conference on Computational Linguisticshttp://acl.ldc.upenn.edu/P/P98/P98-1012.pdf10.3115/980451.9808591998