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.
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Ruch, P., Baud, R., Marty, J., Geissbühler, A., Tbahriti, I., Veuthey, AL. (2005). Latent Argumentative Pruning for Compact MEDLINE Indexing. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_36
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DOI: https://doi.org/10.1007/11527770_36
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