title: Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation.
abstract: BACKGROUND
  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.
authors.affiliation: Swiss Institute of Bioinformatics, CMU, 1 Michel-Servet - CH-1211 Geneva 4, Switzerland. Pavel.Dobrokhotov@isb-sib.ch
authors.forename: Pavel B
authors.initials: PB
authors.lastname: Dobrokhotov
authors.forename: Cyril
authors.initials: C
authors.lastname: Goutte
authors.forename: Anne-Lise
authors.initials: AL
authors.lastname: Veuthey
authors.forename: Eric
authors.initials: E
authors.lastname: Gaussier
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D030562:Databases, Protein; D004282:Documentation / Q000379:methods; D015233:Models, Statistical; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D010506:Periodicals as Topic / Q000145:classification*; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D039781:PubMed
chemical_list: D011506:Proteins