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.

Highlights

  • IntroductionThe syntactic analysis puts additional strain on the recognition system so that the studied paths correspond to words in the lexicon “Baoule” (lexical decoding), and for which, words are in proper sequence as specified by a sentence pattern

  • 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, indexing, retrieval, finished networks deterministic or by statistical grammars, etc

  • We developed a morphosyntactic labeling system language “Baoule” using hidden Markov models

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Summary

Introduction

The syntactic analysis puts additional strain on the recognition system so that the studied paths correspond to words in the lexicon “Baoule” (lexical decoding), and for which, words are in proper sequence as specified by a sentence pattern. Depending on the condition of recognition, some syntactically correct input channels are eliminated from consideration This again serves to make easier the recognition task and leads to a better system performance. Our research work offer goes beyond what is currently available and will allow a person speaking only “Baoule” language to receive and understand “Baoule” language communication expressed in French

System Overview
The Part-of-Speech Tagging
Methodology
The Viterbi Algorithm
Conclusions

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