A neural perceptive model for the recognition of a large canonical Arabic word vocabulary
| Autor: | Ben Cheikh, Imen, Kacem, Afef, Belaïd, Abdel |
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| Přispěvatelé: | Loria, Publications, Technologie de l'Information et de la Communication (UTIC), École Supérieure des Sciences et Technologies de Tunis, READ (READ), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: | |
| Zdroj: | International Arab Conference on Information Technology International Arab Conference on Information Technology, Dec 2009, Sana'a, Yemen |
| Popis: | International audience; This paper introduces a novel approach for the recognition of a wide vocabulary of Arabic words. Note that there is an essential difference between global and analytic approaches in pattern recognition. While the global approach is limited to reduced vocabulary, the analytic approach succeeds to recognize a wide vocabulary but meets the problems of word segmentation especially for Arabic. We have investigated the use of Arabic linguistic knowledge to improve the recognition of wide Arabic word lexicon. A neural-linguistic approach was proposed to mainly deal with canonical vocabulary of decomposable words derived from tri-consonant healthy roots. The basic idea is to factorize words by their roots and schemes. In this direction, we conceived two neural networks TNN_R and TNN_S to respectively recognize roots and schemes from structural primitives of words. The proposal approach achieved promising results. Enlarging the vocabulary from 1000 to 1700 by 100 words, again confirmed the results without altering the networks stability. |
| Databáze: | OpenAIRE |
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