Graph-based Semi-supervised Gene Mention Tagging

The rapidly growing biomedical literature has been a challenging target for natural language processing algorithms. One of the tasks these algorithms focus on is called named entity recognition (NER), often employed to tag gene mentions. Here we describe a new approach for this task, an approach that uses graphbased semi-supervised learning to train a Conditional Random Field (CRF) model. Benchmarking it on the BioCreative II Gene Mention tagging task, we achieved statistically significant improvements in Fmeasure over BANNER, a widely used biomedical NER system. We note that our tool is transductive and modular in nature, and can be integrated with other CRF-based supervised NER tools.