Using Discourse Analysis to Improve Text Categorization in MEDLINE

PROBLEM: Automatic keyword assignment has been largely studied in medical informatics in the context of the MEDLINE database, both for helping search in MEDLINE and in order to provide an indicative “gist” of the content of an article. Automatic assignment of Medical Subject Headings (MeSH), which is formally an automatic text categorization task, has been proposed using different methods or combination of methods, including machine learning (naïve Bayes, neural networks…), linguistically-motivated methods (syntactic parsing, semantic tagging, or information retrieval. METHODS: In the present study, we propose to evaluate the impact of the argumentative structures of scientific articles to improve the categorization effectiveness of a categorizer, which combines linguistically-motivated and information retrieval methods. Our argumentative categorizer, which uses representation levels inherited from the field of discourse analysis, is able to sentences of an abstract in four cla...

• Type: Conference
• Year: 2007
• Where: MEDINFO
• Authors: Patrick Ruch, Antoine Geissbühler, Julien Gobeill, Frédéric Lisacek, Imad Tbahriti, Anne-Lise Veuthey, Alan R. Aronson
• Keywords: Argumentative, Healthcare, Information Retrieval, MEDINFO 2007, Text Categorization