Identifying and tracking entity mentions in a maximum entropy framework

Authors:
A. Ittycheriah;L. Lita;N. Kambhatla;N. Nicolov;S. Roukos;M. Stys
Affiliations:
I.B.M. T.J. Watson Research Center, Yorktown, NY;I.B.M. T.J. Watson Research Center, Yorktown, NY;I.B.M. T.J. Watson Research Center, Yorktown, NY;I.B.M. T.J. Watson Research Center, Yorktown, NY;I.B.M. T.J. Watson Research Center, Yorktown, NY;I.B.M. T.J. Watson Research Center, Yorktown, NY
Venue:
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Year:
2003

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Abstract

We present a system for identifying and tracking named, nominal, and pronominal mentions of entities within a text document. Our maximum entropy model for mention detection combines two pre-existing named entity taggers (built to extract different entity categories) and other syntactic and morphological feature streams to achieve competitive performance. We developed a novel maximum entropy model for tracking all mentions of an entity within a document. We participated in the Automatic Content Extraction (ACE) evaluation and performed well. We describe our system and present results of the ACE evaluation.