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Rethinking Language Models Within the Framework of Dynamic Bayesian Networks

Identifieur interne : 006196 ( Main/Exploration ); précédent : 006195; suivant : 006197

Rethinking Language Models Within the Framework of Dynamic Bayesian Networks

Auteurs : Murat Deviren [France] ; Khalid Daoudi [France] ; Kamel Smaïli [France]

Source :

RBID : ISTEX:E692E17DEBBF885A77D0F89D43D06FFFB7377D88

Descripteurs français

English descriptors

Abstract

Abstract: We present a new approach for language modeling based on dynamic Bayesian networks. The philosophy behind this architecture is to learn from data the appropriate relations of dependency between the linguistic variables used in language modeling process. It is an original and coherent framework that processes words and classes in the same model. This approach leads to new data-driven language models capable of outperforming classical ones, sometimes with lower computational complexity. We present experiments on a small and medium corpora. The results show that this new technique is very promising and deserves further investigations.

Url:
DOI: 10.1007/11424918_47


Affiliations:


Links toward previous steps (curation, corpus...)


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