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Document Re-ordering Based on Key Terms in Top Retrieved Documents

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Abstract

In this paper, we propose a method to improve the precision of top retrieved documents by re-ordering the retrieved documents in the initial retrieval. To re-order the documents, we first automatically extract key terms from top N (N<=30) retrieved documents, then we collect key terms that occur in query and their document frequencies in top N retrieved documents, finally we use these collected terms to re-order the initially retrieved documents. Each collected term is assigned a weight by its length and its document frequency in top N retrieved documents. Each document is re-ranked by the sum of weights of collected terms it contains. In our experiments on 42 query topics in NTCIR3 Cross Lingual Information Retrieval (CLIR) dataset, an average 17.8%-27.5% improvement can be made for top 10 documents and an average 6.6%-12% improvement can be made for top 100 documents at relax/rigid relevance judgment and different parameter setting.

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References

  1. Bear, J., Israel, D., Petit, J., Martin, D.: Using Information Extraction to Improve Document Retrieval. In: Proceedings of the Sixth Text Retrieval Conference (1997)

    Google Scholar 

  2. Fuhr, N.: Probabilistic Models in Information Retrieval. The Computer Journal 35(3), 243–254 (1992)

    Article MATH  Google Scholar 

  3. Ji, D.H., Yang, L.P., Nie, Y.: Chinese Language IR based on Term Extraction. In: The Third NTCIR Workshop (2002)

    Google Scholar 

  4. Kamps, J.: Improving Retrieval Effectiveness by Reranking Documents Based on Controlled Vocabulary. In: The 21th European Conference on Information Retrieval (2004)

    Google Scholar 

  5. Kwok, K.L.: Comparing Representation in Chinese Information Retrieval. In: Proceedings of the ACM SIGIR 1997, pp. 34–41 (1997)

    Google Scholar 

  6. Lee, K., Park, Y., Choi, K.S.: Document Re-ranking model using clusters. Information Processing & Management V (2001)

    Google Scholar 

  7. Li, P.: Research on Improvement of Single Chinese Character Indexing Method. Journal of the China Society for Scientific and Technical Information 18(5) (1999)

    Google Scholar 

  8. Mitra., M., Singhal, A., Buckley, C.: Improving Automatic Query Expansion. In: Proc. ACM SIGIR 1998 (August 1998)

    Google Scholar 

  9. Nie, J.Y., Gao, J., Zhang, J., Zhou, M.: On the Use of Words and N-grams for Chinese Information Retrieval. In: Proceedings of the Fifth International Workshop on Information Retrieval with Asian Languages, IRAL 2000, pp. 141–148 (2000)

    Google Scholar 

  10. Qu, Y.L., Xu, G.W., Wang, J.: Rerank Method Based on Individual Thesaurus. In: Proceedings of NTCIR2 Workshop (2000)

    Google Scholar 

  11. Robertson, S.E., Walker, S.: Microsoft Cambridge at TREC-9: Filtering track. In: NIST Special Pub. 500-264: The Eight Text Retrieval Conference (TREC-8), Gaithersburg, MD, pp. 151–161 (2001)

    Google Scholar 

  12. Salton, G., Mcgill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)

    MATH  Google Scholar 

  13. Schutze, H.: The hypertext concordance: a better back-of-the-book index. In: Proceedings of First Workshop on Computational Terminology, pp. 101–104 (1998)

    Google Scholar 

  14. Yang, L.P., Ji, D.H., Tang, L.: Document Re-ranking Based on Automatically Acquired Key Terms in Chinese Information Retrieval. In: Proceedings of 20th International Conference on Computational Linguistics, COLING 2004 (2004)

    Google Scholar 

  15. Yang, L.P., Ji, D.H., Tang, L.: Chinese Information Retrieval based on Terms and Ontology. In: Proceedings of NTCIR4 Workshop (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Infocomm Research, 21, Heng Mui Keng Terrace, 119613, Singapore

    Yang Lingpeng, Ji Donghong, Nie Yu & Zhou Guodong

Authors
  1. Yang Lingpeng

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  2. Ji Donghong

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  3. Nie Yu

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  4. Zhou Guodong

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Editor information

Editors and Affiliations

  1. National Polytechnic Institute, Center for Computing Research, 07738, Mexico City, México

    Alexander Gelbukh

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© 2005 Springer-Verlag Berlin Heidelberg

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Lingpeng, Y., Donghong, J., Yu, N., Guodong, Z. (2005). Document Re-ordering Based on Key Terms in Top Retrieved Documents. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2005. Lecture Notes in Computer Science, vol 3406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_61

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