Une nouvelle approche de modélisation du langage par des réseaux Bayésiens dynamiquesReport as inadecuate

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1 PAROLE - Analysis, perception and recognition of speech INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications

Abstract : In this paper we propose a new approach to language modeling based on dynamic Bayesian networks. The principle idea of our approach is to find the dependence relations between variables that represent different linguistic units word, class, concept,

. that constitutes a language model. In the context of this paper the linguistic units that we consider are syntactic classes and words. Our approach should not be considered as a model combination technique. Rather, it is an original and coherent methodology that processes words and classes in the same model. We attempt to identify and model the dependence of words and classes on their linguistic context. Our ultimate goal is to devise an automatic mechanism that extracts the best dependence relations between a word and its context, i.e., lexical and syntactic. Preliminary results are very encouraging, in particular the model in which a word depends not only on previous word but also on syntactic classes of two previous words. This model outperforms the bi-gram model.

Mots-clés : modélisation du langage dynamique bayesian networks language modeling réseaux bayesiens dynamiques

Author: Murat Deviren - Khalid Daoudi - Kamel Smaïli -

Source: https://hal.archives-ouvertes.fr/


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