Adapting support vector optimisation algorithms to textual gender classification

Javier Gomez (), Cesar Alfaro (), Felipe Ortega (), Javier M. Moguerza (), Maria Jesus Algar () and Raul Moreno ()
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Javier Gomez: Rey Juan Carlos University
Cesar Alfaro: Rey Juan Carlos University
Felipe Ortega: Rey Juan Carlos University
Javier M. Moguerza: Rey Juan Carlos University
Maria Jesus Algar: Rey Juan Carlos University
Raul Moreno: Rey Juan Carlos University

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2024, vol. 32, issue 3, No 5, 463-488

Abstract: Abstract In this paper, we focus on the problem of determining the gender of the person described in a biographical text. Since support vector machine classifiers are well suited for text classification 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 classification algorithms. The proposed method allows real-time classification algorithm training. Moreover, these results confirm the advantage of leveraging additional gender information in strongly inflected languages, like Spanish, for this task.

Keywords: Support vector machines; Machine learning; Nonlinear optimisation; Text mining; Gender identification; 68U15; 68T50 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11750-024-00671-1

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