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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.9 P.663-676

http://doi.org/10.1631/jzus.C1000104


Using an integrated feature set to generalize and justify the Chinese-to-English transferring rule of the ‘ZHE’ aspect


Author(s):  Yun-hua Qu, Tian-jiong Tao, Serge Sharoff, Narisong Jin, Ruo-yuan Gao, Nan Zhang, Yu-ting Yang, Cheng-zhi Xu

Affiliation(s):  School of International Studies, Zhejiang University, Hangzhou 310058, China, Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang 621900, China, Center for Translation Studies, University of Leeds, Leeds LS2 9JT, UK, Center for Applied Linguistics, Hangzhou Normal University, Hangzhou 310012, China, School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China, Sensor Network and Application Research Center, Graduate School, Chinese Academy of Sciences, Beijing 100049, China, School of Economics, Zhejiang University, Hangzhou 310027, China, Zhejiang University City College, Hangzhou 310015, China

Corresponding email(s):   qu163hua@163.com

Key Words:  ZHE aspect transferring rule (ZHE Rule), Machine learning, Decision tree, Aspect classification, Integrated feature set


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Yun-hua Qu, Tian-jiong Tao, Serge Sharoff, Narisong Jin, Ruo-yuan Gao, Nan Zhang, Yu-ting Yang, Cheng-zhi Xu. Using an integrated feature set to generalize and justify the Chinese-to-English transferring rule of the ‘ZHE’ aspect[J]. Journal of Zhejiang University Science C, 2010, 11(9): 663-676.

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year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000104"
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%A Ruo-yuan Gao
%A Nan Zhang
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A1 - Ruo-yuan Gao
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A1 - Yu-ting Yang
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DOI - 10.1631/jzus.C1000104


Abstract: 
In machine translation (MT) practice, there is an urgent need for constructing a set of Chinese-to-English aspect transferring rules to define the transferring conditions. The integrated feature set was used to generalize and justify the Chinese-to-English transferring rule of the ‘ZHE’ aspect (ZHE Rule). A ZHE classification model was built in this study. The impacts of each set of temporal, lexical aspectual, and syntactic features, and their integrated impacts, on the accuracy of the ZHE Rule were tested. Over 600 misclassified corpus sentences were manually examined. A 10-fold cross-validation was used with a decision tree algorithm. The main results are: (1) The ZHE Rule was generalized and justified to have a higher accuracy under the two metrics: the precision rate and the areas under the receiver operating characteristic curve (AUC). (2) The temporal, lexical aspectual, and syntactic feature sets have an integrated contribution to the accuracy of the ZHE Rule. The syntactic and temporal features have an impact on ZHE aspect derivations, while the lexical aspectual features are not predictive of ZHE aspect derivation. (3) While associated with active verbs, the ZHE aspect can denote a perfective situation. This study suggests that the temporal and syntactic features are the predictive ZHE aspect classification features and that the ZHE Rule with an overall precision rate of 80.1% is accurate enough to be further explored in MT practice. The machine learning method, decision tree, can be applied to the automatic aspect transferring in MT research and aspectual interpretations in linguistic research.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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