Naive Probabilistic Shift-Reduce Parsing Model Using Functional Word Based Context for Agglutinative Languages(Natural Language Processing)
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概要
- 論文の詳細を見る
In this paper, we propose a naive probabilistic shift-reduce parsing model which can use contextual information more flexibly than the previous probabilistic GLR parsing models, and utilize the characteristics of agglutinative language in which the functional words are highly developed. Experimental results on Korean have shown that our model using the proposed contextual information improves the parsing accuracy more effectively than the previous models. Moreover, it is compact in model size, and is robust with a small training set.
- 社団法人電子情報通信学会の論文
- 2004-09-01
著者
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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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Rim Hae-chang
Dept. Of Computer Science And Engineering Korea University
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Kwak Yong-jae
Department Of Computer Science Engineering Korea University
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Rim Hae-chang
Dept. Of Computer And Radio Communications 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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KWAK Yong-Jae
Dept. of Computer Science & Engineering, Korea University
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PARK So-Young
Dept. of Computer Science & Engineering, Korea University
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LIM Joon-Ho
Dept. of Computer Science & Engineering, Korea University
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Lim Joon-ho
Department Of Computer Science Engineering Korea University
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