Title: Combining topic-based model and text categorisation approach for utterance understanding in human-machine dialogue

Authors: Mohamed Lichouri; Rachida Djeradi; Amar Djeradi

Addresses: Laboratory of Spoken Communication and Signal Processing, Faculty of Electronics and Computer Science (FEI), USTHB, BP 32, El Alia, Bab Ezzouar, 16111, Algiers, Algeria ' Laboratory of Spoken Communication and Signal Processing, Faculty of Electronics and Computer Science (FEI), USTHB, BP 32, El Alia, Bab Ezzouar, 16111, Algiers, Algeria ' Laboratory of Spoken Communication and Signal Processing, Faculty of Electronics and Computer Science (FEI), USTHB, BP 32, El Alia, Bab Ezzouar, 16111, Algiers, Algeria

Abstract: In the present paper, we suggest an implementation of an automatic understanding system of the statement in human-machine communication. The architecture we adopt is based on a stochastic approach that assumes that the understanding of a statement is nothing but a simple theme identification process. Therefore, we present a new theme identification method based on a documentary retrieval technique which is text (document) classification (Bawakid and Oussalah, 2010). The method we suggest was validated on a basic platform that gives information related to university schooling management (querying a student database), taking into consideration a textual input in French. This method has achieved a theme identification rate of 95% and a correct utterance understanding rate of about 91.66%.

Keywords: communication; human-machine dialogue; understanding; utterance; thematic; text classification; topic model.

DOI: 10.1504/IJCSE.2018.094429

International Journal of Computational Science and Engineering, 2018 Vol.17 No.1, pp.109 - 117

Received: 26 Dec 2015
Accepted: 30 Aug 2016
Published online: 03 Sep 2018 *

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