Text categorization methods for automatic estimation of verbal intelligence

Fernández Martínez, Fernando and Zablotskaya, Kseniya and 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.

Description

Title: Text categorization methods for automatic estimation of verbal intelligence
Author/s:
  • Fernández Martínez, Fernando
  • Zablotskaya, Kseniya
  • Minker, Wolfgang
Item Type: Article
Título de Revista/Publicación: Expert Systems with Applications
Date: August 2012
ISSN: 0957-4174
Volume: 39
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 15937
DC Identifier: http://oa.upm.es/15937/
OAI Identifier: oai:oa.upm.es:15937
DOI: 10.1016/j.eswa.2012.02.173
Official URL: http://www.sciencedirect.com/science/article/pii/S0957417412004368
Deposited by: Memoria Investigacion
Deposited on: 22 Jun 2013 10:56
Last Modified: 21 Apr 2016 16:15
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