Using discourse analysis to improve text categorization in MEDLINE.
OBJECTIVE
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 classify sentences of an abstract in four classes: PURPOSE; METHODS; RESULTS and CONCLUSION. For the evaluation, the OHSUMED collection, a sample of MEDLINE, is used as a benchmark. For each abstract in the collection, the result of the argumentative classifier, i.e. the labeling of each sentence with an argumentative class, is used to modify the original ranking of the MeSH categorizer.


RESULTS
The most effective combination (+2%, p<0.003) strongly overweights the METHODS section and moderately the RESULTS and CONCLUSION section.


CONCLUSIONS
Although modest, the improvement brought by argumentative features for text categorization confirms that discourse analysis methods could benefit text mining in scientific digital libraries.
Ruch
Patrick
P
Medical Informatics Service, University and Hospital of Geneva, Geneva, Switzerland. patrick.ruch@sim.hcuge.ch
Geissbühler
Antoine
A
Gobeill
Julien
J
Lisacek
Frederic
F
Tbahriti
Imad
I
Veuthey
Anne-Lise
AL
Aronson
Alan R
AR
D000043:Abstracting and Indexing / Q000379:methods*; D016247:Information Storage and Retrieval; D046609:Libraries, Digital; D016239:MEDLINE; D046650:Medical Subject Headings; D009323:Natural Language Processing