Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation
Pavel B. Dobrokhotov, Cyril Goutte, Anne‐Lise Veuthey, Éric Gaussier · Bioinformatics · 2003
Abstract 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: [email protected]; [email protected] *To whom correspondence should be addressed.