Text categorization methods for automatic estimation of verbal intelligence

详细信息	查看全文 | 推荐本文 |

• 作者：Fernando Ferná ; ndez-Mart&#237 ; nez^a ; ^{ffm@die.upm.es} ; Kseniya Zablotskaya^b ; ^{kseniya.zablotskaya@uni-ulm.de} ; Wolfgang Minker^b ; ^{wolfgang.minker@uni-ulm.de}
• 关键词：Spoken dialog systems ; Naive Bayes classification ; Rocchio approach ; k-Nearest neighbors
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：August, 2012
• 卷：39
• 期：10
• 页码：9807-9820
• 文件大小：1001 K

摘要

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.