A Probabilistic Feature-Based Parsing Model for Head-Final Languages(Natural Language Processing)
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概要
- 論文の詳細を見る
In this paper, we propose a probabilistic feature-based parsing model for head-final languages, which can lead to an improvement of syntactic disambiguation while reducing the parsing cost related to lexical information. For effective syntactic disambiguation, the proposed parsing model utilizes several useful features such as a syntactic label feature, a content feature, a functional feature, and a size feature. Moreover, it is designed to be suitable for representing word order variation of non-head words in head-final languages. Experimental results show that the proposed parsing model performs better than previous lexicalized parsing models, although it has much less dependence on lexical information.
- 社団法人電子情報通信学会の論文
- 2004-12-01
著者
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Park So-young
Department Of Physiology College Of Medicine Yeungnam University
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Park So-young
Department Of Computer Science Engineering Korea University
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Rim H‐c
Department Of Computer Science Engineering Korea University
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Rim Hae-chang
Department Of Computer Science Korea University
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Kwak Yong-jae
Department Of Computer Science Engineering Korea University
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Rim Hae-chang
Department Of Computer Science & Engineering Korea University
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Park S‐y
Korea Univ. Seoul Kor
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LIM Joon-Ho
Department of Computer Science Engineering, Korea University
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Lim Joon-ho
Department Of Computer Science Engineering Korea University
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