Gómez, JavierAlfaro, CesarOrtega, FelipeMoguerza, Javier M.Algar, Maria JesusMoreno, Raul2024-06-262024-06-262024-04-13Gomez, J., Alfaro, C., Ortega, F. et al. Adapting support vector optimisation algorithms to textual gender classification. TOP (2024). https://doi.org/10.1007/s11750-024-00671-11863-8279 (online)1134-5764 (print)https://hdl.handle.net/10115/35140Open Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureIn 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 taskengAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Support vector machinesMachine learningNonlinear optimisationText miningGender identificationinfo:eu-repo/semantics/article10.1007/s11750-024-00671-1info:eu-repo/semantics/openAccess