An automatic code classification system by using memory-based learning and information retrieval technique

Heui Seok Lim; Won Kyu Hoon Lee; Hyeon Chul Kim; Soon Young Jeong; Heon Chang Yu

doi:10.1007/11562382_53

An automatic code classification system by using memory-based learning and information retrieval technique

Heui Seok Lim, Won Kyu Hoon Lee, Hyeon Chul Kim, Soon Young Jeong, Heon Chang Yu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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 baised 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.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	577-582
Number of pages	6
DOIs	https://doi.org/10.1007/11562382_53
Publication status	Published - 2005
Event	2nd Asia Information Retrieval Symposium, AIRS 2005 - Jeju Island, Korea, Republic of Duration: 2005 Oct 13 → 2005 Oct 15

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3689 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2nd Asia Information Retrieval Symposium, AIRS 2005
Country/Territory	Korea, Republic of
City	Jeju Island
Period	05/10/13 → 05/10/15

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/11562382_53

Cite this

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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 577-582). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3689 LNCS). https://doi.org/10.1007/11562382_53

An automatic code classification system by using memory-based learning and information retrieval technique. / Lim, Heui Seok; Lee, Won Kyu Hoon; Kim, Hyeon Chul et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 577-582 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3689 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lim, HS, Lee, WKH, Kim, HC, Jeong, SY & Yu, HC 2005, An automatic code classification system by using memory-based learning and information retrieval technique. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3689 LNCS, pp. 577-582, 2nd Asia Information Retrieval Symposium, AIRS 2005, Jeju Island, Korea, Republic of, 05/10/13. https://doi.org/10.1007/11562382_53

Lim HS, Lee WKH, Kim HC, Jeong SY, Yu HC. An automatic code classification system by using memory-based learning and information retrieval technique. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 577-582. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11562382_53

Lim, Heui Seok ; Lee, Won Kyu Hoon ; Kim, Hyeon Chul et al. / An automatic code classification system by using memory-based learning and information retrieval technique. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. pp. 577-582 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 baised 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.",

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AB - 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 baised 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.

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An automatic code classification system by using memory-based learning and information retrieval technique

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