Structured generative models for unsupervised named-entity clustering

Micha Elsner*, Eugene Charniak, Mark Johnson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

34 Citations (Scopus)

Abstract

We describe a generative model for clustering named entities which also models named entity internal structure, clustering related words by role. The model is entirely unsupervised; it uses features from the named entity itself and its syntactic context, and coreference information from an unsupervised pronoun re-solver. The model scores 86% on the MUC-7 named-entity dataset. To our knowledge, this is the best reported score for a fully unsuper-vised model, and the best score for a generative model.

Original languageEnglish
Title of host publicationProceeding NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics (ACL)
Pages164-172
Number of pages9
ISBN (Print)9781932432411
Publication statusPublished - May 2009
Externally publishedYes
EventAnnual Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies, NAACL HLT (10th : 2009) - Boulder, United States
Duration: 31 May 20095 Jun 2009

Other

OtherAnnual Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies, NAACL HLT (10th : 2009)
CountryUnited States
CityBoulder
Period31/05/095/06/09

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