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

• Published in: Bioinformatics 2003, Vol.19 Suppl 1 (suppl_1), p.i91-i94
• Main Authors: Dobrokhotov, Pavel B, Goutte, Cyril, Veuthey, Anne-Lise, Gaussier, Eric
• Format: Article
• Language: English
• Subjects: Abstracting and Indexing as Topic - methods; Algorithms; Artificial Intelligence; Databases, Protein; Documentation - methods; Models, Statistical; Natural Language Processing; Pattern Recognition, Automated; Periodicals as Topic - classification; Proteins - chemistry; Proteins - genetics; PubMed
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btg1011

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. 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.