Various criteria in the evaluation of biomedical named entity recognition |
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| Authors: | Richard Tzong-Han Tsai Shih-Hung Wu Wen-Chi Chou Yu-Chun Lin Ding He Jieh Hsiang Ting-Yi Sung and Wen-Lian Hsu |
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| Institution: | (1) Institute of Information Science, Academia Sinica, Nankang, Taipei 115, R.O.C, Taiwan;(2) Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, R.O.C, Taiwan;(3) Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung County 413, R.O.C, Taiwan |
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| Abstract: | Background Text mining in the biomedical domain is receiving increasing attention. A key component of this process is named entity recognition
(NER). Generally speaking, two annotated corpora, GENIA and GENETAG, are most frequently used for training and testing biomedical
named entity recognition (Bio-NER) systems. JNLPBA and BioCreAtIvE are two major Bio-NER tasks using these corpora. Both tasks
take different approaches to corpus annotation and use different matching criteria to evaluate system performance. This paper
details these differences and describes alternative criteria. We then examine the impact of different criteria and annotation
schemes on system performance by retesting systems participated in the above two tasks. |
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