Presentation 2006-10-11
Bayesian approaches in Natural Language Processing
Daichi MOCHIHASHI,
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Abstract(in English) This paper overviews Bayesian approaches in natural language processing that are becoming prominent. Without any knowledge of natural language processing, Bayesian approaches to both discriminative learning and generative modeling are described. Especially, naive bayes and its full unsupervised Bayesian modeling, DM, and LDA are developed. These Bayesian approaches permit interesting joint modeling with continuous data, such as images arid musics.
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Keyword(in English) Discrete data / Natural language processing / Dirichlet distribution / LDA / DM / Naive Bayes
Paper # NC2006-49
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Committee NC
Conference Date 2006/10/4(1days)
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Registration To Neurocomputing (NC)
Language JPN
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Title (in English) Bayesian approaches in Natural Language Processing
Sub Title (in English)
Keyword(1) Discrete data
Keyword(2) Natural language processing
Keyword(3) Dirichlet distribution
Keyword(4) LDA
Keyword(5) DM
Keyword(6) Naive Bayes
1st Author's Name Daichi MOCHIHASHI
1st Author's Affiliation ATR Spoken Language Communication Research Laboratories:National Institute of Information and Communications Technology()
Date 2006-10-11
Paper # NC2006-49
Volume (vol) vol.106
Number (no) 279
Page pp.pp.-
#Pages 6
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