Open AccessA system for identifying named entities in biomedical text: how results from two evaluations reflect on both the system and the evaluations
Author(s) -
Dingare Shipra,
Nissim Malvina,
Finkel Jenny,
Manning Christopher,
Grover Claire
Publication year - 2005
Publication title -
comparative and functional genomics
Language(s) - English
Resource type - Journals
eISSN - 1532-6268
pISSN - 1531-6912
DOI - 10.1002/cfg.457
Subject(s) - computer science , named entity recognition , parsing , biomedical text mining , annotation , identification (biology) , information retrieval , natural language processing , named entity , adaptation (eye) , artificial intelligence , principle of maximum entropy , data mining , text mining , botany , physics , management , optics , economics , biology , task (project management)
We present a maximum entropy‐based system for identifying named entities (NEs) in biomedical abstracts and present its performance in the only two biomedical named entity recognition (NER) comparative evaluations that have been held to date, namely BioCreative and Coling BioNLP. Our system obtained an exact match F‐score of 83.2% in the BioCreative evaluation and 70.1% in the BioNLP evaluation. We discuss our system in detail, including its rich use of local features, attention to correct boundary identification, innovative use of external knowledge resources, including parsing and web searches, and rapid adaptation to new NE sets. We also discuss in depth problems with data annotation in the evaluations which caused the final performance to be lower than optimal. Copyright © 2005 John Wiley & Sons, Ltd.