Journal of Computing Science and Engineering

  • 제3권3호
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  • Pages.165-180
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  • 2009
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  • 1976-4677(pISSN)
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  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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HKIB-20000 & HKIB-40075: Hangul Benchmark Collections for Text Categorization Research

  • Kim, Jin-Suk (Department of Information Technology Research, KISTI) ;
  • Choe, Ho-Seop (Department of Information Technology Research, KISTI) ;
  • You, Beom-Jong (Department of Information Technology Research, KISTI) ;
  • Seo, Jeong-Hyun (Department of Cyber Environment Development, KISTI) ;
  • Lee, Suk-Hoon (Department of Information & Statistics, Chungnam National University) ;
  • Ra, Dong-Yul (Computer & Telecommunication Engineering Division, Yonsei University)
  • 발행 : 2009.09.30
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초록

The HKIB, or Hankookilbo, test collections are two archives of Korean newswire stories manually categorized with semi-hierarchical or hierarchical category taxonomies. The base newswire stories were made available by the Hankook Ilbo (The Korea Daily) for research purposes. At first, Chungnam National University and KISTI collaborated to manually tag 40,075 news stories with categories by semi-hierarchical and balanced three-level classification scheme, where each news story has only one level-3 category (single-labeling). We refer to this original data set as HKIB-40075 test collection. And then Yonsei University and KISTI collaborated to select 20,000 newswire stories from the HKIB-40075 test collection, to rearrange the classification scheme to be fully hierarchical but unbalanced, and to assign one or more categories to each news story (multi-labeling). We refer to this modified data set as HKIB-20000 test collection. We benchmark a k-NN categorization algorithm both on HKIB-20000 and on HKIB-40075, illustrating properties of the collections, providing baseline results for future studies, and suggesting new directions for further research on Korean text categorization problem.

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참고문헌

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피인용 문헌

  1. A Study on Feature Selection for kNN Classifier using Document Frequency and Collection Frequency vol.44, pp.1, 2013, https://doi.org/10.16981/kliss.44.1.201303.27