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