Resolución de la ambigüedad léxica mediante información contextual y el modelo del espacio vectorial

  1. L. Alfonso Ureña López 1
  2. Manuel García Vega 1
  3. Manuel de Buenaga Rodríguez 2
  4. José María Gómez Hidalgo 2
  1. 1 Universidad de Jaén
    info

    Universidad de Jaén

    Jaén, España

    ROR https://ror.org/0122p5f64

  2. 2 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Book:
CAEPIA'97: actas
  1. Botti, Vicent (coord.)

Publisher: Vicent Botti ; Asociación Española para la Inteligencia Artificial (AEPIA)

ISBN: 978-84-8498-765-9,  84-8498-765-5

Year of publication: 1997

Pages: 787-796

Congress: Conferencia de la Asociación Española para la Inteligencia Artificial. (7. 1997. null)

Type: Conference paper

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Abstract

The resolution of lexical ambiguity of polysemics words is a complex and useful task for many natural language processing applications. We present a new approach for word sense disambiguation based in the vector space model and a widely available training collection as linguistic resource. This approach uses a variable set of terms like local context. We have tested our disambiguator algorithm on a large documents collection, achieving high precision in the resolution of lexical ambiguity.

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