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Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity

  • David Nadeau
  • Peter D. Turney
  • Stan Matwin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4013)

Abstract

In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system’s architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands).

Keywords

Ambiguity Resolution Computational Linguistics Unknown Word Supervise System Lexical Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David Nadeau
    • 1
    • 2
  • Peter D. Turney
    • 1
  • Stan Matwin
    • 2
    • 3
  1. 1.National Research CouncilInstitute for Information TechnologyCanada
  2. 2.School of Information Technology and EngineeringUniversity of OttawaCanada
  3. 3.Institute for Computer SciencePolish Academy of SciencesPoland

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