Journal Article

MeSH Up: effective MeSH text classification for improved document retrieval

Dolf Trieschnigg, Piotr Pezik, Vivian Lee, Franciska de Jong, Wessel Kraaij and Dietrich Rebholz-Schuhmann

in Bioinformatics

Volume 25, issue 11, pages 1412-1418
Published in print June 2009 | ISSN: 1367-4803
Published online April 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp249

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology

GO

Show Summary Details

Preview

Motivation: Controlled vocabularies such as the Medical Subject Headings (MeSH) thesaurus and the Gene Ontology (GO) provide an efficient way of accessing and organizing biomedical information by reducing the ambiguity inherent to free-text data. Different methods of automating the assignment of MeSH concepts have been proposed to replace manual annotation, but they are either limited to a small subset of MeSH or have only been compared with a limited number of other systems.

Results: We compare the performance of six MeSH classification systems [MetaMap, EAGL, a language and a vector space model-based approach, a K-Nearest Neighbor (KNN) approach and MTI] in terms of reproducing and complementing manual MeSH annotations. A KNN system clearly outperforms the other published approaches and scales well with large amounts of text using the full MeSH thesaurus. Our measurements demonstrate to what extent manual MeSH annotations can be reproduced and how they can be complemented by automatic annotations. We also show that a statistically significant improvement can be obtained in information retrieval (IR) when the text of a user's query is automatically annotated with MeSH concepts, compared to using the original textual query alone.

Conclusions: The annotation of biomedical texts using controlled vocabularies such as MeSH can be automated to improve text-only IR. Furthermore, the automatic MeSH annotation system we propose is highly scalable and it generates improvements in IR comparable with those observed for manual annotations.

Contact: trieschn@ewi.utwente.nl

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6272 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.