• Title/Summary/Keyword: CITATION

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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%.

Study about Research Data Citation Based on DCI (Data Citation Index) (Data Citation Index를 기반으로 한 연구데이터 인용에 관한 연구)

  • Cho, Jane
    • Journal of the Korean Society for Library and Information Science
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    • v.50 no.1
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    • pp.189-207
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    • 2016
  • Sharing and reutilizing of research data could not only enhance efficiency and transparency of research process, but also create new science through data integrating and reinterpretationing. Diverse policies about research data sharing and reutilizing have been developing, along with extending of research evaluating spectrum that across research data citation rate to social impact of research output. This study analyzed the scale and citation number of research data which has not been analyzed before in korea through data citation index using Kruskal-Wallis H analysis. As result, genetics and biotechnology are identified as subject areas which have most huge number of research data, however the subject areas that have been highly cited are identified as economics and social study such as, demographic and employment. And Uk Data Archive, Inter-university Consortium for Political and Social Research are analyzed as data repositories which have most highly cited research data. And the data study which describes methodology of data survey, type and so on shows high citation rate than other data type. In the result of altmetrics of research data, data study of social science shows relatively high impact than other areas.

A Comparative Analysis of Ego-Centered Journal Citation Identities in Library and Information Science (국내 문헌정보학 주요 저널의 자아 인용정체성 분석)

  • Hea-Jin Kim
    • Journal of the Korean Society for information Management
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    • v.41 no.2
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    • pp.1-18
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    • 2024
  • This study aims to compare ego-centered journal citation identities among four domestic journals in library and information science. Ego-centered citation identity refers to the set of authors that an author frequently cites. The target journals for this study are Journal of the Korean Society for Library and Information Science (KSLIS), Journal of the Korean Biblia Society for Library and Information Science (KBIBLIA), Journal of Korean Library and Information Science Society (KLISS), and Journal of the Korean Society for Information Management (KOSIM). As a result of citation/citee ratio (CCR), self-citing rates (SCR), and journal co-cited analysis, the journal citation identities of four journals contained the other three journals besides the ego journal and JASIST. Furthermore, KOSIM had the most diverse range of journal citation identity and the four journals mattered the intra-journal information. KLISS showed the most unique cited journal network structure among the four journals.