Improving information retrieval in MEDLINE by modulating MeSH term weights
- Authors
- Shin, K.; Han, S.Y.
- Issue Date
- Jun-2004
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS, v.3136, pp 388 - 394
- Pages
- 7
- Journal Title
- NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS
- Volume
- 3136
- Start Page
- 388
- End Page
- 394
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65569
- DOI
- 10.1007/978-3-540-27779-8_36
- ISSN
- 0302-9743
1611-3349
- Abstract
- MEDLINE is a widely used very large database of natural language medical data, mainly abstracts of research papers in medical domain. The documents in it are manually supplied with keywords from a controlled vocabulary, called MeSH terms. We show that (1) a vector space model-based retrieval system applied to the full text of the documents gives much better results than the Boolean model-based system supplied with MEDLINE, and (2) assigning greater weights to the MeSH terms than to the terms in the text of the documents provides even better results than the standard vector space model. The resulting system outperforms the retrieval system supplied with MEDLINE as much as 2.4 times.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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