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A Deep Learning-Based Named Entity Recognition in Biomedical Domain

Publication Type : Conference Paper

Publisher : Springer Verlag

Source : Lecture Notes in Electrical Engineering, Springer Verlag, Volume 545, p.517-526 (2019)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065497244&doi=10.1007%2f978-981-13-5802-9_47&partnerID=40&md5=4f66261432d5f7f549555cb93ee2ac2d

ISBN : 9789811358012

Keywords : Biomedical domain, Biomedical fields, Biomedical named entity recognition, Deep learning, Digital storage, Learning techniques, Long short-term memory, LSTM, Named entity recognition, NAtural language processing, Natural language processing systems, State-of-the-art system

Campus : Coimbatore

School : School of Artificial Intelligence, School of Artificial Intelligence - Coimbatore, School of Engineering

Center : Computational Engineering and Networking

Department : Computer Science, Electronics and Communication

Year : 2019

Abstract : In the biomedical field, huge amounts of data have been produced day by day. These data drives the development of the biomedical area researches in so many ways. This paper mainly focusing on biomedical named entity recognition (NER) with the aim to enhance the performance through deep learning. Impressive results in natural language processing are made possible by deep learning techniques. Deep learning enables us to use them for NLP tasks and producing huge differences in accuracy compared to traditional methods. NER is a crucial initial step in information extraction in the biomedical domain. Here we use RNN, LSTM, and GRU on GENIA version 3.02 corpus and achieves an F score of 90%, which is better than the most state-of-the-art systems. © 2019, Springer Nature Singapore Pte Ltd.

Cite this Research Publication : Gopalakrishnan, A., Soman, K.P., Premjith, B., "A Deep Learning-Based Named Entity Recognition in Biomedical Domain," (2019) Lecture Notes in Electrical Engineering, 545, pp. 517-526., DOI: 10.1007/978-981-13-5802-9_47

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