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Adapting support vector optimisation algorithms to textual gender classifcation

  • Javier Gomez [1] ; Cesar Alfaro [1] ; Felipe Ortega [1] ; Javier M. Moguerza [1] Árbol académico ; Maria Jesus Algar [1] ; Raul Moreno [2]
    1. [1] Research Centre for Intelligent Information Technologies (CETINIA-DSLAB), Rey Juan Carlos University, Calle Tulipán, Móstoles 28933, Madrid, Spain
    2. [2] Doctorate Programme in Information Technologies and Communications, Rey Juan Carlos University, Calle Tulipán, Móstoles 28933, Madrid, Spain
  • Localización: Top, ISSN-e 1863-8279, ISSN 1134-5764, Vol. 32, Nº. Extra 3, 2024 (Ejemplar dedicado a: Mathematical Optimization and Machine Learning), págs. 463-488
  • Idioma: inglés
  • DOI: 10.1007/s11750-024-00671-1
  • Enlaces
  • Resumen

    • In this paper, we focus on the problem of determining the gender of the person described in a biographical text. Since support vector machine classifers are well suited for text classifcation tasks, we present a new stopping criterion for support vector optimisation algorithms tailored to this problem. This new approach exploits the geometric properties of the vector representation of such content. An experiment on a set of English and Spanish biographical articles retrieved from Wikipedia illustrates this approach and compares it to other machine learning classifcation algorithms. The proposed method allows real-time classifcation algorithm training. Moreover, these results confrm the advantage of leveraging additional gender information in strongly infected languages, like Spanish, for this task.

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