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 proceedingConference contribution

    2 Citations (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 languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages577-582
    Number of pages6
    DOIs
    Publication statusPublished - 2005
    Event2nd Asia Information Retrieval Symposium, AIRS 2005 - Jeju Island, Korea, Republic of
    Duration: 2005 Oct 132005 Oct 15

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3689 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other2nd Asia Information Retrieval Symposium, AIRS 2005
    Country/TerritoryKorea, Republic of
    CityJeju Island
    Period05/10/1305/10/15

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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