CLASSIFICATION OF BAOULE SENTENCES ACCORDING TO FREQUENCY AND SEGMENTATION OF TERMS VIA CONVOLUTIONAL NEURAL NETWORKS
- Ecole Superieure Africaine Des Technologies dInformation Et De La Communication (ESATIC), Cote dIvoire.
- Institut National Polytechnique Felix Houphouet Boigny (INP-HB), Cote dIvoire.
- Universite Virtuellede Cote dIvoire (UVCI).
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In the Baoule language, several sentences express the same fact. Classification of sentences is a task of Natural Language Processing (NLP). Deep learning has turned out to be a kind of method that has a significant effect in this area. In this paper, we propose a convolutional neural network (CNN) based system for sentence classification. We introduce into this system a word representation model to capture semantic characteristics by encoding the frequency of terms and segmenting the sentence into clauses. The experimental results show that our system produces satisfactory results.
[Hyacinthe Kouassi Konan, Francis Adles Kouassi, Guy L. Diety and Olivier Asseu (2022); CLASSIFICATION OF BAOULE SENTENCES ACCORDING TO FREQUENCY AND SEGMENTATION OF TERMS VIA CONVOLUTIONAL NEURAL NETWORKS Int. J. of Adv. Res. 10 (Jan). 62-67] (ISSN 2320-5407). www.journalijar.com
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