KR-WordRank : WordRank를 개선한 비지도학습 기반 한국어 단어 추출 방법

KR-WordRank : An Unsupervised Korean Word Extraction Method Based on WordRank

  • 김현중 (서울대학교 산업공학과) ;
  • 조성준 (서울대학교 산업공학과) ;
  • 강필성 (서울과학기술대학교 글로벌융합산업공학과)
  • Kim, Hyun-Joong (Dept. of Industrial Engineering, Seoul National University) ;
  • Cho, Sungzoon (Dept. of Industrial Engineering, Seoul National University) ;
  • Kang, Pilsung (Dept. of Industrial and Information Systems Engineering, Seoul National University of Science and Technology)
  • 투고 : 2013.11.18
  • 심사 : 2014.01.09
  • 발행 : 2014.02.15


A Word is the smallest unit for text analysis, and the premise behind most text-mining algorithms is that the words in given documents can be perfectly recognized. However, the newly coined words, spelling and spacing errors, and domain adaptation problems make it difficult to recognize words correctly. To make matters worse, obtaining a sufficient amount of training data that can be used in any situation is not only unrealistic but also inefficient. Therefore, an automatical word extraction method which does not require a training process is desperately needed. WordRank, the most widely used unsupervised word extraction algorithm for Chinese and Japanese, shows a poor word extraction performance in Korean due to different language structures. In this paper, we first discuss why WordRank has a poor performance in Korean, and propose a customized WordRank algorithm for Korean, named KR-WordRank, by considering its linguistic characteristics and by improving the robustness to noise in text documents. Experiment results show that the performance of KR-WordRank is significantly better than that of the original WordRank in Korean. In addition, it is found that not only can our proposed algorithm extract proper words but also identify candidate keywords for an effective document summarization.


Word Extraction;Keyword Extraction;Text Mining;Unsupervised Learning;WordRank


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