Combining topic-based model and text categorisation approach for utterance understanding in human-machine dialogue
Amar Djéradi, Rachida Djeradi, Mohamed Lichouri · International Journal of Computational Science and Engineering · 2018
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%.