Investigating verbal intelligence using the TF-IDF approach

Zablotskaya, Kseniya and Fernández Martínez, Fernando and Minker, Wolfgang (2012). Investigating verbal intelligence using the TF-IDF approach. In: "LREC 2012, Eighth International Conference on Language Resources and Evaluation", 21/05/2012 - 27/05/2013, Istanbul. ISBN 978-2-9517408-7-7. pp. 1573-1576.

Description

Title: Investigating verbal intelligence using the TF-IDF approach
Author/s:
  • Zablotskaya, Kseniya
  • Fernández Martínez, Fernando
  • Minker, Wolfgang
Item Type: Presentation at Congress or Conference (Article)
Event Title: LREC 2012, Eighth International Conference on Language Resources and Evaluation
Event Dates: 21/05/2012 - 27/05/2013
Event Location: Istanbul
Title of Book: Proceedings of LREC 2012, Eighth International Conference on Language Resources and Evaluation
Date: May 2012
ISBN: 978-2-9517408-7-7
Subjects:
Freetext Keywords: Verbal intelligence, cosine similarity, TF-IDF measures
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 investigated differences in language use of speakers yielding different verbal intelligence when they describe the same event. The work is based on a corpus containing descriptions of a short film and verbal intelligence scores of the speakers. For analyzing the monologues and the film transcript, the number of reused words, lemmas, n-grams, cosine similarity and other features were calculated and compared to each other for different verbal intelligence groups. The results showed that the similarity of monologues of higher verbal intelligence speakers was greater than of lower and average verbal intelligence participants. A possible explanation of this phenomenon is that candidates yielding higher verbal intelligence have a better short-term memory. In this paper we also checked a hypothesis that differences in vocabulary of speakers yielding different verbal intelligence are sufficient enough for good classification results. For proving this hypothesis, the Nearest Neighbor classifier was trained using TF-IDF vocabulary measures. The maximum achieved accuracy was 92.86%.

More information

Item ID: 20424
DC Identifier: http://oa.upm.es/20424/
OAI Identifier: oai:oa.upm.es:20424
Official URL: http://www.lrec-conf.org/proceedings/lrec2012/pdf/352_Paper.pdf
Deposited by: Memoria Investigacion
Deposited on: 05 Oct 2013 10:42
Last Modified: 21 Apr 2016 23:13
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