Exploring the boundaries: gene and protein identification in biomedical text

We present a maximum-entropy based system incorporating a diverse set of features for identifying genes and proteins in biomedical s. This system was entered in the BioCreative comparative evaluation and achieved the best performance in the "open" evaluation and the second-best performance in the "closed" evaluation. Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge including full MEDLINE abstracts and web searches.

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Jenny Rose Finkel, Shipra Dingare, Christopher D. Manning, Malvina Nissim, Beatrice Alex, Claire Grover
• Keywords: BioCreative Comparative Evaluation, BMCBI 2005, Central Contributions, Diverse Set