Morpho-Syntactic Tagging of Text in “Baoule” Language Based on Hidden Markov Models (HMM)
- 1 Ecole Supérieure Africaine des TICs (ESATIC), Abidjan, Côte d’Ivoire
- 2 Institut National Polytechnique Félix Houphouët Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire
- 3 Institut National Polytechnique Félix Houphouët Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire
- 4 Institut National Polytechnique Félix Houphouët Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire
Abstract
The label text is a very important tool for the automatic processing of language. It is used in several applications such as morphological and syntactic text analysis, index-ing, retrieval, finished networks deterministic (in which all combinations of words that are accepted by the grammar are listed) or by statistical grammars (e.g., an n-gram in which the probabilities of sequences of n words in a specific order are given), etc. In this article, we developed a morphosyntactic labeling system language “Baoule” using hidden Markov models. This will allow us to build a tagged reference corpus and rep-resent major grammatical rules faced “Baoule” language in general. To estimate the parameters of this model, we used a training corpus manually labeled using a set of morpho-syntactic labels. We then proceed to an improvement of the system through the re-estimation procedure parameters of this model.
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