[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

An Automatic Code Classification System by Using Memory-Based Learning and Information Retrieval Technique

  • Conference paper
Information Retrieval Technology (AIRS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3689))

Included in the following conference series:

Abstract

This paper proposes an automatic code classification for Korean census data by using information retrieval technique and memoory-based learning technique. The purpose of the proposed system is to convert natural language responses on survey questionnaires into corresponding numeric codes according to standard code book from the Census Bureau. The system was trained by memory based learning and experimented with 46,762 industry records and occupation 36,286 records. It was evaluated by using 10-fold cross-validation method. As experimental results, the proposed system showed 99.10% and 92.88% production rates for level 2 and level 5 codes respectively.

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

Access this chapter

Log in via an institution

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. Apeel, M.V., Hellerman, E.: Census Bureau Experiments with Automated Industry and Occupation Coding. In: Proceedings of the American Statistical Association, pp. 32–40 (1983)

    Google Scholar 

  2. Baeza-Yates, Ribeiro-Neto: Modern Information Retrieval. Addison-Wesley, Reading (1999)

    Google Scholar 

  3. Chen, B., Creecy, R.H., Appel, M.: On Error Control of Automated Industry and Occupation Coding. Journal of Official Statistics 9(4), 729–745 (1993)

    Google Scholar 

  4. Creecy, R.H., Masand, B.M., Smith, S.J., Walts, D.L.: Trading MIPS and Memory for Knowledge Engineering. Communications of the ACM 35(8), 48–64 (1992)

    Article  Google Scholar 

  5. Gilman, D.W., Appel, M.V.: Automated Coding Research At the Census Bureau. U.S. Census Bureau, http://www.census.gov/srd/papers/pdf/rr94-4.pdf

  6. Korean Standard Industrial Classification. National Statistical Office (January 2000)

    Google Scholar 

  7. Korean Standard Classification of Occupations. National Statistical Office (January 2000)

    Google Scholar 

  8. Lee, D.G.: A High Speed Index Term Extracting System Considering the Morphological Configuration of Noun. M.S. Thesis, Dept. of Computer Science and Engineering, Korea Univ., Korea (2000)

    Google Scholar 

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

    MATH  Google Scholar 

  10. Rowe, E., Wong, C.: An Introduction to the ACRT Coding System. Bureau of the Census Statistical Research Report Series No. RR94/02 (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Software, Hanshin University, Korea

    Heui Seok Lim

  2. Dept. of Computer Science Educatoin, Korea University, Korea

    Won Kyu Hoon Lee, Hyeon Chul Kim, Soon Young Jeong & Heon Chang Yu

Authors
  1. Heui Seok Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Won Kyu Hoon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyeon Chul Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Soon Young Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Heon Chang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, 790-784, Pohang, Korea

    Gary Geunbae Lee

  2. Computer and Communication Media Research, NEC Corp., Miyazaki 4-1-1, Miyamae-ku, 216-8555, Kawasaki, Japan

    Akio Yamada

  3. Human-Computer Communications Laboratory, Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong

    Helen Meng

  4. School of Engineering, Information and Communications University, 119, Munjiro, Yuseong-gu, 305-732, Daejeon, Korea

    Sung Hyon Myaeng

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lim, H.S., Lee, W.K.H., Kim, H.C., Jeong, S.Y., Yu, H.C. (2005). An Automatic Code Classification System by Using Memory-Based Learning and Information Retrieval Technique. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_53

Download citation

  • DOI: https://doi.org/10.1007/11562382_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29186-2

  • Online ISBN: 978-3-540-32001-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation