Abstract
The importance of information extraction is well known due to its conceptual simplicity and potential usefulness but domain-specific task makes the process more tractable than others. Biomedical named entity recognition one such active research area that identifies biomedical entities and serves as a support system for the downstream task such as knowledge base construction, knowledge discovery, etc. The key challenge behind biomedical named entity recognition lies in the features and methods selection owing to higher complexity in the related entities. The researches have shown promising result but correctly identifying a chunk of text is an important task as it contains lots of important details which need to be analyzed to make sense out of it. This survey attempts to provide important insights of biomedical named entity recognition task to help biomedical research community.
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References
B. Alshaikhdeeb, K. Ahmad, Biomedical named entity recognition: a review. Int. J. Adv. Sci. Eng. Inf. Technol. 6, 889 (2016)
L. Gong, Y. Yuan, Y. Wei, X. Sun, A hybrid approach for biomedical entity name recognition, in 2009 2nd International Conference on Biomedical Engineering and Informatics (Tianjin, 2009), pp. 1–5
A.W. Qureshi, F. Azam, U. Qamar, A relation extraction framework for biomedical text using hybrid feature set. Comput. Math. Methods Med. (2015)
S. Zhang, N. Elhadad, Unsupervised biomedical named entity recognition: experiments with clinical and biological texts. J. Biomed. Inf. 46(6), 1088–1098 (2013)
T. Munkhdalai et al., Bio named entity recognition based on co-training algorithm, in 2012 26th International Conference on Advanced Information Networking and Applications Workshops (Fukuoka, 2012), pp. 857–862
A. Goyal, V. Gupta, M. Kumar, Recent named entity recognition and classification techniques: a systematic review. Comput. Sci. Rev. 29, 21–43 (2018)
K. Li et al., Hadoop recognition of biomedical named entity using conditional random fields, in IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 11 (2015), pp. 3040–3051
L. Li, R. Zhou, D. Huang, Two-phase biomedical named entity recognition using CRFs. Comput. Biol. Chem. 33(4), 334–338 (2009)
M. Habibi, L. Weber, M. Neves, D.L. Wiegandt, U. Leser, Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics 33(14), i37–i48 (2017)
U. Kanimozhi, D. Manjula, A CRF based machine learning approach for biomedical named entity recognition, in 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) (Tindivanam, 2017), pp. 335–342
R. Phan, T.M. Luu, R. Davey, G. Chetty, Biomedical named entity recognition based on hybrid multistage CNN-RNN learner, in 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) (Sydney, Australia, 2018), pp. 128–135
J.M. Giorgi, G.D. Bader, Transfer learning for biomedical named entity recognition with neural networks. Bioinformatics 34(23), 4087–4094 (2018)
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Suman, S., Dash, A., Rautaray, S.S. (2021). A Literature Survey on Biomedical Named Entity Recognition. In: Priyadarshi, N., Padmanaban, S., Ghadai, R.K., Panda, A.R., Patel, R. (eds) Advances in Power Systems and Energy Management. ETAEERE ETAEERE 2020 2020. Lecture Notes in Electrical Engineering, vol 690. Springer, Singapore. https://doi.org/10.1007/978-981-15-7504-4_12
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DOI: https://doi.org/10.1007/978-981-15-7504-4_12
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