Co-training and Self-training for Word Sense Disambiguation Metadata

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Title

  • Main Title Co-training and Self-training for Word Sense Disambiguation

Creator

  • Author: Mihalcea, Rada, 1974-
    Creator Type: Personal
    Creator Info: University of North Texas

Contributor

  • Organizer of meeting: Association for Computational Linguistics. Special Interest Group on Natural Language Learning.
    Contributor Type: Organization

Date

  • Creation: 2004-05

Language

  • English

Description

  • Content Description: This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.
  • Physical Description: 8 p.

Subject

  • Keyword: word sense disambiguations
  • Keyword: optimal parameter settings
  • Keyword: sense classifiers
  • Keyword: bootstrapping

Source

  • Conference: Conference on Natural Language Learning (CoNLL), 2004, Boston, Massachusetts, United States

Collection

  • Name: UNT Scholarly Works
    Code: UNTSW

Institution

  • Name: UNT College of Engineering
    Code: UNTCOE

Rights

  • Rights Access: public

Resource Type

  • Paper

Format

  • Text

Identifier

  • Archival Resource Key: ark:/67531/metadc30955

Degree

  • Academic Department: Computer Science and Engineering

Note