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).
Abstract
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.
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How to Cite This Article
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, International Journal of Advanced Research (IJAR), 10 (01), 62-67, ISSN 2320-5407. DOI: https://doi.org/10.21474/IJAR01/14012
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