Latent Argumentative Pruning for Compact MEDLINE Indexing View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2005

AUTHORS

Patrick Ruch , Robert Baud , Johann Marty , Antoine Geissbühler , Imad Tbahriti , Anne-Lise Veuthey

ABSTRACT

PURPOSE: We evaluate how argumentation in scientific articles can be used to propose an original index pruning strategy, which significantly reduce the size of the engine’s indexes but having a limited impact on retrieval effectiveness. METHODS: A Bayesian classifier trained on explicitly structured MEDLINE abstracts generates these argumentative categories. The categories are used to generate four different argumentative indexes. A fifth index contains the complete abstract, together with the title and the list of Medical Subject Headings (MeSH) terms. This last index is used as baseline to compare results obtained when only a specific argumentative index is retrieved. RESULTS and CONCLUSION: When titles and medical subject headings are also stored in the respective indexes, querying PURPOSE and CONCLUSION indexes can respectively achieves 78.4% and 74.3% of the baseline, while the size if the index is divided by two. It is concluded that argumentation can be a powerful index pruning strategy in complement to more traditionnal approaches. More... »

PAGES

246-250

Book

TITLE

Artificial Intelligence in Medicine

ISBN

978-3-540-27831-3
978-3-540-31884-2

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11527770_36

DOI

http://dx.doi.org/10.1007/11527770_36

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048777026


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