Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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A System for Automatic Classification of Traditional Culture Texts

전통문화 콘텐츠 표준체계를 활용한 자동 텍스트 분류 시스템

  • Hur, YunA (Department of Computer Science and Engineering, Korea University) ;
  • Lee, DongYub (Department of Computer Science and Engineering, Korea University) ;
  • Kim, Kuekyeng (Department of Computer Science and Engineering, Korea University) ;
  • Yu, Wonhee (Department of Computer Science and Engineering, Korea University) ;
  • Lim, HeuiSeok (Department of Computer Science and Engineering, Korea University)
  • Received : 2017.10.28
  • Accepted : 2017.12.20
  • Published : 2017.12.28
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Abstract

The Internet have increased the number of digital web documents related to the history and traditions of Korean Culture. However, users who search for creators or materials related to traditional cultures are not able to get the information they want and the results are not enough. Document classification is required to access this effective information. In the past, document classification has been difficult to manually and manually classify documents, but it has recently been difficult to spend a lot of time and money. Therefore, this paper develops an automatic text classification model of traditional cultural contents based on the data of the Korean information culture field composed of systematic classifications of traditional cultural contents. This study applied TF-IDF model, Bag-of-Words model, and TF-IDF/Bag-of-Words combined model to extract word frequencies for 'Korea Traditional Culture' data. And we developed the automatic text classification model of traditional cultural contents using Support Vector Machine classification algorithm.

한국 문화의 역사, 전통과 관련된 디지털 웹 문서가 증가하게 되었다. 하지만 창작자 또는 전통 문화와 관련된 소재를 찾는 사용자들은 정보를 검색해도 결과가 충분하지 않았으며 원하는 정보를 얻지 못하는 경우가 나타나고 있다. 이런 효과적인 정보를 접하기 위해서는 문서 분류가 필요하다. 과거에 문서 분류는 작업자가 수작업으로 문서 분류하여 시간과 비용이 많이 소비하는 어려움이 있었지만, 최근 기계학습 기반으로 한 자동 문서 분류를 통해 효율적인 문서 분류가 이루어진다. 이에 본 논문은 전통문화 콘텐츠를 체계적인 분류체계로 구성한 한민족정보문화마당 데이터를 기반으로 전통문화 콘텐츠 자동 텍스트 분류 모델을 개발한다. 본 연구는 한민족정보문화마당 텍스트 데이터에 대해 단어 빈도수를 추출하기 위해 TF-IDF모델, Bag-of-Words 모델, TF-IDF/Bag-of-Words를 결합한 모델을 적용하여 각각 SVM 분류 알고리즘을 사용하여 전통문화 콘텐츠 자동 텍스트 분류 모델을 개발하여 성능평가를 확인하였다.

Keywords

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