Ensemble Approach for Cross Language Information Retrieval

  • Dinesh Mavaluru
  • R. Shriram
  • W. Aisha Banu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7182)


Cross language information retrieval (CLIR) is a sub field of information retrieval (IR) which deals with retrieval of content from one language (source language) for a search query expressed in another language (target language) in the Web. Cross Language Information Retrieval evolved as a field due to the fact that majority of the content in the web is in English. Hence there is a need for dynamic translation of web content for a query expressed in the native language. The biggest problem is that of ambiguity of the query expressed in the native language. The ambiguity of languages is typically not a problem for human beings who can infer the appropriate word sense or meaning based on context, but search engines cannot usually overcome these limitations. Hence, methods and mechanisms to provide native languages access to information from the web are needed. There is a need, to not only retrieve the relevant results but also, present the content behind the results in a user understandable manner. The research in the domain has so far focused in terms of techniques that make use support vector machines, suffix tree approach, Boolean models, and iterative results clustering. This research work focuses on a methodology of personalized context based cross language information retrieval using ensemble-learning approach. The source language for this research is taken, as English and the target language is Telugu. The methodology has tested for various queries and the results are shown in this work.


Information Retrieval Cross Language Information Retrieval Ontology Summarization 


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  1. 1.
    Makin., R., Pandey., N., Pingali, P., Varma, V.: Experiments in Cross lingual IR among Indian Languages. In: International Workshop on Cross Language Information Processing (CLIP 2007), Genoa, July 9-10 (2007)Google Scholar
  2. 2.
    Saraswathi, S., Siddhiqaa, M., Kalaimagal, K.: Bilingual Information Retrieval System for English and Tamil. Journal of Computing 2(4) (April 2010)Google Scholar
  3. 3.
    Lazarinis, F., Jesus, S., John, V.: Current research issues and trends in non-English Web searching. Springer Science (2009)Google Scholar
  4. 4.
    Vijayanand, K., Seenivasan, R.P.: Named Entity Recognition and Transliteration for Telugu Language. Language in India, Special Volume: Problems of Parsing in Indian Languages (May 2011),
  5. 5.
    Sieg, A., Mobasher, B., Burke, R.: Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search. IEEE Intelligent Informatics Bulletin 8(1) (November 2007)Google Scholar
  6. 6.
    Carpineto, C., Romano, G., Snidero, M.: Mobile information Retrieval with Search Results Clustering: Prototypes and Evaluation. Journal of the American Society for Information Science and Technology 60(5), 877–895 (2009)CrossRefGoogle Scholar
  7. 7.
    Huo, Z., Zhao, J., Hu, X.: Web Data Management for Mobile Users, Network and Parallel Computing Workshops. In: IFIP International Conference on NPC Workshops, September 18-21 (2007)Google Scholar
  8. 8.
    Banu, W.A., Kader, P.S.A.: A Hybrid Context Based Approach for Web Information Retrieval. International Journal of Computer Applications, article 5 (2010)Google Scholar
  9. 9.
    Nasharuddin, N.A., Abdullah, M.: Cross-lingual Information Retrieval: State-of-the-Art. Electronic Journal of Computer Science and Information Technology 2 (2010)Google Scholar
  10. 10.
    Petrelli, D., Levin, S., Beaulieu, M., Sanderson, M.: Which user interaction for cross-language information retrieval? Design issues and reflections. Journal of the American Society for Information Science and Technology 57(5), 709–722Google Scholar
  11. 11.
    Damjanovic, V., Gasevic, D., Devedzic, V.: Semiotics for Ontologies and Knowledge Representation. In: Proc. of Wissens Management, pp. 571–574 (2005)Google Scholar
  12. 12.
    Zhou, D., Truran, M., Brailsford, T., Ashman, H.: A Hybrid Technique for English-Chinese Cross Language Information Retrieval. ACM Transactions on Asian Language Information Processing (2008)Google Scholar
  13. 13.
    Wang, X., Broder, A., Gabrilovich, E., Josifovski, V., Pang, B.: Cross-language query classification using web search for exogenous knowledge. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining (February 2009)Google Scholar
  14. 14.
    Maeda, A., Kimura, F.: An Approach to Cross-Age and Cross-Cultural Information Access for Digital Humanities. In: Digital Resources for the Humanities and Arts 2008 Conference (DRHA 2008), Cambridge, U.K (September 2008)Google Scholar
  15. 15.
    Khan, A., Naveed, A.M.: Corpus Based Mapping of Urdu Characters for Cell Phones. In: Proceedings of the Conference on Language & Technology (2009)Google Scholar
  16. 16.
    Prasad, P., Varma, V.: Hindi and Telugu to English Cross Language Information Retrieval at CLEF 2006. In: Working Notes for the CLEF 2006 Workshop (Cross Language Adhoc Task), Alicante, Spain, September 20-22 (2006)Google Scholar
  17. 17.
    Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. The MIT Press (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dinesh Mavaluru
    • 1
  • R. Shriram
    • 1
  • W. Aisha Banu
    • 1
  1. 1.School of Computer and Information SciencesB.S. Abdur Rahman UniversityChennaiIndia

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