Unsupervised Training of a Finite-State Sliding-Window Part-of-Speech Tagger

  • Enrique Sánchez-Villamil
  • Mikel L. Forcada
  • Rafael C. Carrasco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3230)

Abstract

A simple, robust sliding-window part-of-speech tagger ispresented and a method is given to estimate its parameters from an untagged corpus. Its performance is compared to a standard Baum-Welch-trained hidden-Markov-model part-of-speech tagger. Transformation into a finite-state machine — behaving exactly as the tagger itself— is demonstrated.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Enrique Sánchez-Villamil
    • 1
  • Mikel L. Forcada
    • 1
  • Rafael C. Carrasco
    • 1
  1. 1.Transducens, Departament de Llenguatges i Sistemes InformàticsUniversitat d’AlacantAlacant

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