• Title/Summary/Keyword: 인용문 인식 규칙

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A Rule-based Approach to Identifying Citation Text from Korean Academic Literature (한국어 학술 문헌의 본문 인용문 인식을 위한 규칙 기반 방법)

  • Kang, In-Su
    • Journal of the Korean Society for information Management
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    • v.29 no.4
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    • pp.43-60
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    • 2012
  • Identifying citing sentences from article full-text is a prerequisite for creating a variety of future academic information services such as citation-based automatic summarization, automatic generation of review articles, sentiment analysis of citing statements, information retrieval based on citation contexts, etc. However, finding citing sentences is not easy due to the existence of implicit citing sentences which do not have explicit citation markers. While several methods have been proposed to attack this problem for English, it is difficult to find such automatic methods for Korean academic literature. This article presents a rule-based approach to identifying Korean citing sentences. Experiments show that the proposed method could find 30% of implicit citing sentences in our test data in nearly 70% precision.

Recognition of Korean Implicit Citation Sentences Using Machine Learning with Lexical Features (어휘 자질 기반 기계 학습을 사용한 한국어 암묵 인용문 인식)

  • Kang, In-Su
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.16 no.8
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    • pp.5565-5570
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    • 2015
  • Implicit citation sentence recognition is to locate citation sentences which lacks explicit citation markers, from articles' full-text. State-of-the-art approaches exploit word ngrams, clue words, researcher's surnames, mentions of previous methods, and distance relative to nearest explicit citation sentences, etc., reaching over 50% performance. However, most previous works have been conducted on English. As for Korean, a rule-based method using positive/negative clue patterns was reported to attain the performance of 42%, requiring further improvement. This study attempted to learn to recognize implicit citation sentences from Korean literatures' full-text using Korean lexical features. Different lexical feature units such as Eojeol, morpheme, and Eumjeol were evaluated to determine proper lexical features for Korean implicit citation sentence recognition. In addition, lexical features were combined with the position features representing backward/forward proximities to explicit citation sentences, improving the performance up to over 50%.