RECOGNITION OF GENE/PROTEIN NAMES USING CONDITIONAL RANDOM FIELDS

With the overwhelming amount of publicly available data in the biomedical field, traditional tasks performed by expert database annotators rapidly became hard and very expensive. This situation led to the development of computerized systems to extract information in a structured manner. The first step of such systems requires the identification of named entities (e.g. gene/protein names), a task called Named Entity Recognition (NER). Much of the current research to tackle this problem is based on Machine Learning (ML) techniques, which demand careful and sensitive definition of the several used methods. This article presents a NER system using Conditional Random Fields (CRFs) as the machine learning technique, combining the best techniques recently described in the literature. The proposed system uses biomedical knowledge and a large set of orthographic and morphological features. An F-measure of 0,7936 was obtained on the BioCreative II Gene Mention corpus, achieving a significantly better performance than similar baseline systems.