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A comparative study for biomedical named entity recognition

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Abstract

With high-throughput technologies applied in biomedical research, the quantity of biomedical literatures grows exponentially. It becomes more and more important to quickly as well as accurately extract knowledge from manuscripts, especially in the era of big data. Named entity recognition (NER), aiming at identifying chunks of text that refers to specific entities, is essentially the initial step for information extraction. In this paper, we will review the three models of biomedical NER and two famous machine learning methods, Hidden Markov Model and Conditional Random Fields, which have been widely applied in bioinformatics. Based on these two methods, six excellent biomedical NER tools are compared in terms of programming language, feature sets, underlying mathematical methods, post-processing techniques and flowcharts. Experimental results of these tools against two widely used corpora, GENETAG and JNLPBA, are conducted. The comparison varies from different entity types to the overall performance. Furthermore, we put forward suggestions about the selection of Bio-NER tools for different applications.

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Acknowledgments

This paper is supported by the National Key Basic Research Program of China (No. 2015CB453000), National Natural Science Foundation of China (Nos. 61572228, 41101376, 61272207 and 61300147), and the Science Technology Development Project of Jilin Province of China (20130101070JC, 20130522106JH and 20140520070JH).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun, 130012, People’s Republic of China

    Xu Wang & Renchu Guan

  2. College of Earth Sciences, Jilin University, 2699 Qianjin Street, Changchun, 130012, People’s Republic of China

    Chen Yang

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  1. Xu Wang
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  2. Chen Yang
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  3. Renchu Guan
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Correspondence to Renchu Guan.

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Wang, X., Yang, C. & Guan, R. A comparative study for biomedical named entity recognition. Int. J. Mach. Learn. & Cyber. 9, 373–382 (2018). https://doi.org/10.1007/s13042-015-0426-6

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