Balancing Manual and Automatic Indexing for Retrieval of Paper Abstracts

  • Kwangcheol Shin
  • Sang-Yong Han
  • Alexander Gelbukh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3206)


MEDLINE is a widely used very large database of abstracts of research papers in medical domain. Abstracts in it are manually supplied with keywords from a controlled vocabulary called MeSH. The MeSH keywords assigned to a specific document are subdivided into MeSH major headings, which express the main topic of the document, and MeSH minor headings, which express additional information about the document’s topic. The search engine supplied with MEDLINE uses Boolean retrieval model with only MeSH keywords used for indexing. We show that (1) vector space retrieval model with the full text of the abstracts indexed gives much better results; (2) assigning greater weights to the MeSH keywords than to the terms appearing in the text of the abstracts gives slightly better results, and (3) assigning slightly greater weight to major MeSH terms than to minor MeSH terms further improves the results.


Search Engine Full Text MeSH Term Vector Space Model Boolean Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kwangcheol Shin
    • 1
  • Sang-Yong Han
    • 1
  • Alexander Gelbukh
    • 1
    • 2
  1. 1.Computer Science and Engineering DepartmentChung-Ang UniversitySeoulKorea
  2. 2.National Polytechnic InstituteCenter for Computing ResearchMexico

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