A system for identifying named entities in biomedical text: how results from two evaluations reflect on both the system and the evaluations

Shipra Dingare; Malvina Nissim; Jenny Finkel; Christopher Manning; Claire Grover

doi:10.1002/cfg.457

A system for identifying named entities in biomedical text: how results from two evaluations reflect on both the system and the evaluations

Comp Funct Genomics. 2005;6(1-2):77-85. doi: 10.1002/cfg.457.

Authors

Shipra Dingare¹, Malvina Nissim, Jenny Finkel, Christopher Manning, Claire Grover

Affiliation

¹ Institute for Communicating and Collaborative Systems, University of Edinburgh, 2 Buccleuch Place, Edinburgh EH8 9LW, UK.

PMID: 18629295
PMCID: PMC2448599
DOI: 10.1002/cfg.457

Abstract

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.