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1997
- handle: 10670/1.41xi9a
- hal: hal-01392216
- doi: 10.1109/ASRU.1997.659133
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info:eu-repo/semantics/altIdentifier/doi/10.1109/ASRU.1997.659133
info:eu-repo/semantics/OpenAccess
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Language (New words, slang, etc.) Probability Statistical inferenceCiter ce document
Brigitte Bigi et al., « Combined models for topic spotting and topic-dependent language modeling », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1109/ASRU.1997.659133
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Résumé
A new statistical method for Language Modeling and spoken document classification is proposed. It is based on a mixture of topic dependent probabilities. Each topic dependent probability is in turn a mixture of n-gram probabilities and the probability of Kullback-Lieber (KL) distances between keyword unigrams and distribution obtained from the content of a cache memory. Experimental result on topic classification using a corpus of 60 Mword from the French newspaper Le Monde show the excellent performance of the cache memory and its complementary role in providing different statistics for the decision process.