CLC number: TP391.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-06-30
Cited: 1
Clicked: 8787
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.
@article{title="Using an integrated feature set to generalize and justify the Chinese-to-English transferring rule of the ‘ZHE’ aspect",
author="Yun-hua Qu, Tian-jiong Tao, Serge Sharoff, Narisong Jin, Ruo-yuan Gao, Nan Zhang, Yu-ting Yang, Cheng-zhi Xu",
journal="Journal of Zhejiang University Science C",
volume="11",
number="9",
pages="663-676",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000104"
}
%0 Journal Article
%T Using an integrated feature set to generalize and justify the Chinese-to-English transferring rule of the ‘ZHE’ aspect
%A Yun-hua Qu
%A Tian-jiong Tao
%A Serge Sharoff
%A Narisong Jin
%A Ruo-yuan Gao
%A Nan Zhang
%A Yu-ting Yang
%A Cheng-zhi Xu
%J Journal of Zhejiang University SCIENCE C
%V 11
%N 9
%P 663-676
%@ 1869-1951
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000104
TY - JOUR
T1 - Using an integrated feature set to generalize and justify the Chinese-to-English transferring rule of the ‘ZHE’ aspect
A1 - Yun-hua Qu
A1 - Tian-jiong Tao
A1 - Serge Sharoff
A1 - Narisong Jin
A1 - Ruo-yuan Gao
A1 - Nan Zhang
A1 - Yu-ting Yang
A1 - Cheng-zhi Xu
J0 - Journal of Zhejiang University Science C
VL - 11
IS - 9
SP - 663
EP - 676
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
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.
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