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Bioinformatics Vol. 19 Suppl. 1 2003
Pages i91-i94
© 2003 Oxford University Press

Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation

Pavel B. Dobrokhotov *,1, Cyril Goutte 2,*, Anne-Lise Veuthey 1 and Eric Gaussier 2

1 Swiss Institute of Bioinformatics, CMU, 1 Michel-Servet - CH-1211 Genève 4, Switzerland
2 Xerox Research Centre Europe, 6 ch. de Maupertuis - F-38240 Meylan, France

Received on January 6, 2003 ; accepted on February 20, 2003

Motivation: Searching relevant publications for manual database annotation is a tedious task. In this paper, we apply a combination of Natural Language Processing (NLP) and probabilistic classification to re-rank documents returned by PubMed according to their relevance to Swiss-Prot annotation, and to identify significant terms in the documents.

Results: With a Probabilistic Latent Categoriser (PLC) we obtained 69% recall and 59% precision for relevant documents in a representative query. As the PLC technique provides the relative contribution of each term to the final document score, we used the Kullback-Leibler symmetric divergence to determine the most discriminating words for Swiss-Prot medical annotation. This information should allow curators to understand classification results better. It also has great value for fine-tuning the linguistic pre-processing of documents, which in turn can improve the overall classifier performance.

Availability: The medical annotation dataset is available from the authors upon request

Contact: Pavel.Dobrokhotov{at}isb-sib.ch; Cyril.Goutte{at}xrce.xerox.com

* To whom correspondence should be addressed.


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