Improving information retrieval in MEDLINE by modulating MeSH term weights

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.