Journal of Intelligent Information Systems

, Volume 27, Issue 2, pp 117–133 | Cite as

Using KCCA for Japanese–English cross-language information retrieval and document classification

  • Yaoyong LiEmail author
  • John Shawe-Taylor


Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm based on KCCA is studied for cross-language information retrieval. We apply the algorithm in Japanese–English cross-language information retrieval. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. Computational complexity is an important issue when applying KCCA to large dataset as in information retrieval. We experimentally evaluate several methods to alleviate the problem of applying KCCA to large datasets. We also investigate cross-language document classification using KCCA as well as other methods. Our results show that it is feasible to use a classifier learned in one language to classify the documents in other languages.


Cross-language information retrieval Machine learning Kernel canonical correlation analysis Unsupervised learning Cross-language Japanese–English document retrieval and classification 


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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of SheffieldSheffieldUK
  2. 2.ISIS Group, School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK

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