Syntactic Category Prediction for Improving Parsing Accuracy in English-Korean Machine Translation 


Vol. 13,  No. 3, pp. 345-352, Jun.  2006
10.3745/KIPSTB.2006.13.3.345


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  Abstract

The practical English-Korean machine translation system should be able to translate long sentences quickly and accurately. The intra-sentence segmentation method has been proposed and contributed to speeding up the syntactic analysis. This paper proposes the syntactic category prediction method using decision trees for getting accurate parsing results. In parsing with segmentation, the segment is separately parsed and combined to generate the sentence structure. The syntactic category prediction would facilitate to select more accurate analysis structures after the partial parsing. Thus, we could improve the parsing accuracy by the prediction. We construct features for predicting syntactic categories from the parsed corpus of Wall Street Journal and generate decision trees. In the experiments, we show the performance comparisons with the predictions by human-built rules, trigram probability and neural networks. Also, we present how much the category prediction would contribute to improving the translation quality.

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[IEEE Style]

S. D. Kim, "Syntactic Category Prediction for Improving Parsing Accuracy in English-Korean Machine Translation," The KIPS Transactions:PartB , vol. 13, no. 3, pp. 345-352, 2006. DOI: 10.3745/KIPSTB.2006.13.3.345.

[ACM Style]

Sung Dong Kim. 2006. Syntactic Category Prediction for Improving Parsing Accuracy in English-Korean Machine Translation. The KIPS Transactions:PartB , 13, 3, (2006), 345-352. DOI: 10.3745/KIPSTB.2006.13.3.345.

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