Skip to main content

A New Approach for Automatic Building Field Association Words Using Selective Passage Retrieval

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4252))

  • 2447 Accesses

Abstract

Large collections of full-text document are now commonly used in automated information retrieval. When the stored document texts are long, the retrieval of complete documents may not be in the users’ best interest and extract Filed Association (FA) words is not accurate. In such circumstances, efficient and effective retrieval FA words may be obtained by using passage retrieval strategies designed to retrieve text excerpts of varying size in response to statements of user interest.

New approaches are described in this study for implementing selective passage retrieval systems, and identifying text passage response to particular user needs. Moreover an automated system is using for extract accurate FAwords from that passage and evaluate the usefulness of the proposed method. From the experimental results, when passage retrieval are accessible leading to the retrieval of additional extracted relevant FA word with corresponding improvements in Recall and Precision. Therefore, Recall and Precision improved by 30% than using whole texts and traditional methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aoe, J., Morita, K., Mochizuki, H.: An Efficient Retrieval Algorithm of Collocate Information Using Tree Structure. Transaction of the IPSJ 39(9), 2563–2571 (1989)

    Google Scholar 

  2. Atlam, E.-S., Morita, K., Fuketa, M., Aoe, J.: A New Method For Selecting English Compound Terms and its Knowledge Representation. Information Processing & Management Journal 38, 807–821 (2000)

    Article  Google Scholar 

  3. Atlam, E.-S., Fuketa, M., Morita, K., Aoe, J.: Document Similarity measurement using Field association terms. Information Processing & Management Journal 39, 809–824 (2003)

    Article  Google Scholar 

  4. Atlam, E.-S., Elmarhomy, G., Fuketa, M., Morita, K., Aoe, J.-i.: Automatic building of new Field Association word candidates using search. Information Processing & Management Journal 42(4), 951–962 (2006)

    Article  Google Scholar 

  5. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman and Hall, Boca Raton (1984)

    MATH  Google Scholar 

  6. Callen, J.P.: Passage and level evidence in document retrieval. In: Proc. of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 302–310 (1994)

    Google Scholar 

  7. Dozawa, T.: Innovative Multi Information Dictionary Imidas 1999. Annual Series. Zueisha Publication Co., Japan (1999) (In Japanese)

    Google Scholar 

  8. Iwayama, M., Tokunaga, T.: Probabilistic Passage Categorization and Its Application. Journal of Natural language Processing 6(3), 181–198 (1999)

    Google Scholar 

  9. Kaszkiel, M., Zobel, J.: Passage retrieval revised. In: Proc. of the 20th Annual International ACM SIGIR Conference on Research and Development in information Retrieval, pp. 178–185 (1997)

    Google Scholar 

  10. Kawabe, K., Matsumoto, Y.: Acquisition of normal lexical knowledge based on basic level category. Information Processing Society of Japan, SIG note, NL125-9, 87–92 (1998)

    Google Scholar 

  11. Melucii, M.: Passage Retrieval and a Probabilistic technique. Information Processing and Management 34(1), 43–68 (1998)

    Article  Google Scholar 

  12. Risvik, K.M., Michelsen, R.: Search Engines and Web Dynamics. Computer Networks 39, 289–302 (2002)

    Article  Google Scholar 

  13. Salton, G., McGill, M.J.: Introduction of Modern Information Retrieval. McGraw-Hill, New York (1983)

    Google Scholar 

  14. Salton, G., Allan, J., Buckley, C.: Approaches to Passage Retrieval in Full Text Information Systems. In: The Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1993) (1993)

    Google Scholar 

  15. Salton, G.: Automatic text Processing-The Transformation, Analysis, and Retrieval of Information by Computer. Addison Wesley Publishing Company, Reading (1989)

    Google Scholar 

  16. Tsuji, T., Nigazawa, H., Okada, M., Aoe, J.: Early Field Recognition by Using Field Association Words. In: The Proceeding of the 18th International Conference on Computer Processing of Oriental Language, vol. 2, pp. 301–304 (1999)

    Google Scholar 

  17. Tsuji, T., Fuketa, M., Morita, K., Aoe, J.: An Efficient Method of Determining Field Association Terms of Compound Words. Journal of Natural Language Processing 7(2), 3–26 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Atlam, ES., Ghada, E., Morita, K., Aoe, Ji. (2006). A New Approach for Automatic Building Field Association Words Using Selective Passage Retrieval. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_41

Download citation

  • DOI: https://doi.org/10.1007/11893004_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46537-9

  • Online ISBN: 978-3-540-46539-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics