Automatic Acquisition of Chinese–English Parallel Corpus from the Web

  • Ying Zhang
  • Ke Wu
  • Jianfeng Gao
  • Phil Vines
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)


Parallel corpora are a valuable resource for tasks such as cross-language information retrieval and data-driven natural language processing systems. Previously only small scale corpora have been available, thus restricting their practical use. This paper describes a system that overcomes this limitation by automatically collecting high quality parallel bilingual corpora from the web. Previous systems used a single principle feature for parallel web page verification, whereas we use multiple features to identify parallel texts via a k-nearest-neighbor classifier. Our system was evaluated using a data set containing 6500 Chinese–English candidate parallel pairs that have been manually annotated. Experiments show that the use of a k-nearest-neighbors classifier with multiple features achieves substantial improvements over the systems that use any one of these features. The system achieved a precision rate of 95% and a recall rate of 97%, and thus is a significant improvement over earlier work.


Candidate Site Feature Filter Parallel Corpus Candidate Pair Feature Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ying Zhang
    • 1
  • Ke Wu
    • 2
  • Jianfeng Gao
    • 3
  • Phil Vines
    • 1
  1. 1.RMIT UniversityMelbourneAustralia
  2. 2.Shanghai Jiaotong UniversityShanghaiChina
  3. 3.Microsoft ResearchRedmondUSA

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