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Descripción
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| In this paper we investigate whether conventional text categorisation 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 Dialogue System may be useful when defining an optimal dialogue strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologues) 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 monologues 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 monologues were represented as feature vectors using the classical TF-IDF weighting scheme. The Naive Bayes, k-nearest neighbours 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. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Expert Systems With Applications |
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ISSN
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0957-4174 |
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Factor de impacto JCR
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2,203 |
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Información de impacto
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Volumen
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39 |
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DOI
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Número de revista
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10 |
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Desde la página
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9807 |
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Hasta la página
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9820 |
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Mes
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SIN MES |
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Ranking
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COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 22/111 ENGINEERING, ELECTRICAL & ELECTRONIC 41/245 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 5/77 |