| Presentation | 2006-10-11 Bayesian approaches in Natural Language Processing Daichi MOCHIHASHI, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| 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. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Discrete data / Natural language processing / Dirichlet distribution / LDA / DM / Naive Bayes |
| Paper # | NC2006-49 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2006/10/4(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| 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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