Presentation 1997/10/17
A Parameter Estimation of a Probabilistic Context Free Grammar Expressing the Dependency Constraints on a Sparse Sample
Yoichi TOMIURA, Takeshi NISHIDA, Toru HITAKA,
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Abstract(in English) We can disambiguate syntactic structures of a sentence based on a Probabilistic Context Free Grammar (PCFG) where dependency constraints are expressed in the production rules. But to describe dependency constraints in detail causes an explosion of the number of production rules, and the explosion makes it difficult to collect enough size of sample to get a reliable maximum likelihood estimate of parameters in the PCFG. This paper propose a new estimator of parameters in a PCFG expressing the dependency constraints. The mean error of the proposed estimator is practically smaller than the one of maximum likelihood estimator, and this tendency is conspicuous on a small size of sample.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Paremater Estimation / Smoothing / PCFG / Dependency Constraints
Paper # NLC97-32
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Committee NLC
Conference Date 1997/10/17(1days)
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Paper Information
Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Parameter Estimation of a Probabilistic Context Free Grammar Expressing the Dependency Constraints on a Sparse Sample
Sub Title (in English)
Keyword(1) Paremater Estimation
Keyword(2) Smoothing
Keyword(3) PCFG
Keyword(4) Dependency Constraints
1st Author's Name Yoichi TOMIURA
1st Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University()
2nd Author's Name Takeshi NISHIDA
2nd Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University
3rd Author's Name Toru HITAKA
3rd Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University
Date 1997/10/17
Paper # NLC97-32
Volume (vol) vol.97
Number (no) 330
Page pp.pp.-
#Pages 7
Date of Issue