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Contrastive Analysis and Feature Selection for Korean Modal Expression in Chinese-Korean Machine Translation System

Department of Computer Science and Engineering, Electrical and Computer Engineering Division and Advanced Information Technology Research Center (AITrc), Pohang University of Science and Technology (POSTECH), San 31 Hyoja Dong, Pohang, 790-784, Korea

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JI-EUN ROH

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DONG-IL KIM

Language Engineering Institute, Department of Computer, Electron and Telecommunication Engineering, Yanbian University of Science and Technology (YUST), Yanji, Jilin, 133-000, P.R. China

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, and

JONG-HYEOK LEE

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https://doi.org/10.1142/S0219427905001298Cited by:0 (Source: Crossref)

Abstract

To generate a proper Korean predicate, a natural modal expression is the most important factor for a machine translation (MT) system. Tense, aspect, mood, negation, and voice are the major constituents related to modal expression. The linguistic encoding of a modal expression is quite different between Chinese and Korean in terms of linguistic typology and genealogy. In this paper, a new applicable categorization of Korean modality system viz. tense, aspect, mood, negation, and voice, will be proposed through a contrastive analysis of Chinese and Korean from the viewpoint of a practical MT system. In order to precisely determine the modal expression, effective feature selection frameworks for Chinese are presented with a variety of machine learning methods. As a result, our proposed approach achieved an accuracy of 83.10%.

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Vol. 18, No. 03

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Contrastive Analysis and Feature Selection for Korean Modal Expression in Chinese-Korean Machine Translation System

Abstract

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