| Presentation | 1995/12/15 Back-off Method for N-gram Smoothing based on Binomial Posteriori Distribution Takeshi KAWABATA, Masafumi TAMOTO, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The n-gram language models are powerful for treating natural spoken languages, however need large amounts of spoken language corpus for estimating reliable model parameters. For estimating n-gram probabilities from sparse data, Katz's back-off smoothing method is promising. However, this approach is sometimes unstable because it uses singleton heuristics based on Turing's formula. This paper proposes a new back-off method based on Binomial Posteriori Distribution of n-gram probabilities, which achieves stable and more effective n-gram smoothing by sophisticated calculation formula with no heuristics. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Natural language / n-gram / back-off / JUNO |
| Paper # | NLC95-58,SP95-93 |
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| Conference Information | |
| Committee | NLC |
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| Conference Date | 1995/12/15(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Natural Language Understanding and Models of Communication (NLC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Back-off Method for N-gram Smoothing based on Binomial Posteriori Distribution |
| Sub Title (in English) | |
| Keyword(1) | Natural language |
| Keyword(2) | n-gram |
| Keyword(3) | back-off |
| Keyword(4) | JUNO |
| 1st Author's Name | Takeshi KAWABATA |
| 1st Author's Affiliation | NTT Basic Research Laboratories() |
| 2nd Author's Name | Masafumi TAMOTO |
| 2nd Author's Affiliation | NTT Basic Research Laboratories |
| Date | 1995/12/15 |
| Paper # | NLC95-58,SP95-93 |
| Volume (vol) | vol.95 |
| Number (no) | 429 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |