Text categorization methods for automatic estimation of verbal intelligence

Fernández Martínez, Fernando; Zablotskaya, Kseniya y Minker, Wolfgang (2012). Text categorization methods for automatic estimation of verbal intelligence. "Expert Systems with Applications", v. 39 (n. 10); pp. 9807-9820. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2012.02.173.

Descripción

Título: Text categorization methods for automatic estimation of verbal intelligence
Autor/es:
  • Fernández Martínez, Fernando
  • Zablotskaya, Kseniya
  • Minker, Wolfgang
Tipo de Documento: Artículo
Título de Revista/Publicación: Expert Systems with Applications
Fecha: Agosto 2012
Volumen: 39
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF–IDF weighting scheme. The Naive Bayes, k-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained.

Más información

ID de Registro: 15937
Identificador DC: http://oa.upm.es/15937/
Identificador OAI: oai:oa.upm.es:15937
Identificador DOI: 10.1016/j.eswa.2012.02.173
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0957417412004368
Depositado por: Memoria Investigacion
Depositado el: 22 Jun 2013 10:56
Ultima Modificación: 21 Abr 2016 16:15
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