Articles producció científica> Filologies Romàniques

A White-Box Sociolinguistic Model for Gender Detection

  • Datos identificativos

    Identificador: imarina:9247856
    Autores:
    Morales Sanchez, DamianMoreno, AntonioJimenez Lopez, Maria Dolores
    Resumen:
    Within the area of Natural Language Processing, we approached the Author Profiling task as a text classification problem. Based on the author's writing style, sociodemographic information, such as the author's gender, age, or native language can be predicted. The exponential growth of user-generated data and the development of Machine-Learning techniques have led to significant advances in automatic gender detection. Unfortunately, gender detection models often become black-boxes in terms of interpretability. In this paper, we propose a tree-based computational model for gender detection made up of 198 features. Unlike the previous works on gender detection, we organized the features from a linguistic perspective into six categories: orthographic, morphological, lexical, syntactic, digital, and pragmatics-discursive. We implemented a Decision-Tree classifier to evaluate the performance of all feature combinations, and the experiments revealed that, on average, the classification accuracy increased up to 3.25% with the addition of feature sets. The maximum classification accuracy was reached by a three-level model that combined lexical, syntactic, and digital features. We present the most relevant features for gender detection according to the trees generated by the classifier and contextualize the significance of the computational results with the linguistic patterns defined by previous research in relation to gender.
  • Otros:

    Autor según el artículo: Morales Sanchez, Damian; Moreno, Antonio; Jimenez Lopez, Maria Dolores
    Departamento: Enginyeria Informàtica i Matemàtiques Filologies Romàniques
    Autor/es de la URV: Jiménez López, María Dolores / Morales Sánchez, Damián / Moreno Ribas, Antonio
    Palabras clave: Machine learning Gender detection Computational sociolinguistics Author profiling Author
    Resumen: Within the area of Natural Language Processing, we approached the Author Profiling task as a text classification problem. Based on the author's writing style, sociodemographic information, such as the author's gender, age, or native language can be predicted. The exponential growth of user-generated data and the development of Machine-Learning techniques have led to significant advances in automatic gender detection. Unfortunately, gender detection models often become black-boxes in terms of interpretability. In this paper, we propose a tree-based computational model for gender detection made up of 198 features. Unlike the previous works on gender detection, we organized the features from a linguistic perspective into six categories: orthographic, morphological, lexical, syntactic, digital, and pragmatics-discursive. We implemented a Decision-Tree classifier to evaluate the performance of all feature combinations, and the experiments revealed that, on average, the classification accuracy increased up to 3.25% with the addition of feature sets. The maximum classification accuracy was reached by a three-level model that combined lexical, syntactic, and digital features. We present the most relevant features for gender detection according to the trees generated by the classifier and contextualize the significance of the computational results with the linguistic patterns defined by previous research in relation to gender.
    Áreas temáticas: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: damian.morales@urv.cat damian.morales@urv.cat antonio.moreno@urv.cat mariadolores.jimenez@urv.cat
    Identificador del autor: 0000-0003-3945-2314 0000-0001-5544-3210
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2076-3417/12/5/2676
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Applied Sciences-Basel. 12 (5): 2676-
    Referencia de l'ítem segons les normes APA: Morales Sanchez, Damian; Moreno, Antonio; Jimenez Lopez, Maria Dolores (2022). A White-Box Sociolinguistic Model for Gender Detection. Applied Sciences-Basel, 12(5), 2676-. DOI: 10.3390/app12052676
    DOI del artículo: 10.3390/app12052676
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Chemistry, Multidisciplinary,Computer Science Applications,Engineering (Miscellaneous),Engineering, Multidisciplinary,Fluid Flow and Transfer Processes,Instrumentation,Materials Science (Miscellaneous),Materials Science, Multidisciplinary,Physics, Applied,Process Chemistry and Technology
    Machine learning
    Gender detection
    Computational sociolinguistics
    Author profiling
    Author
    Química
    Process chemistry and technology
    Physics, applied
    Materials science, multidisciplinary
    Materials science (miscellaneous)
    Materials science (all)
    Materiais
    Instrumentation
    General materials science
    General engineering
    Fluid flow and transfer processes
    Engineering, multidisciplinary
    Engineering (miscellaneous)
    Engineering (all)
    Engenharias ii
    Engenharias i
    Computer science applications
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências agrárias i
    Ciência de alimentos
    Chemistry, multidisciplinary
    Biodiversidade
    Astronomia / física
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