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

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 SwissProt annotation, and to identify significant terms in the documents.