Biomedical Named Entity Recognition Based on Hybrid Multistage CNN-RNN Learner

Robert Phan, Thoai Man Luu, Rachel Davey, Girija Chetty

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

This current research presents an inventive multilevel named entity recognition scheme for explaining the confrontation with biomedical entity recognition which based on divergent algorithms. The presented scheme contains multilevels, which enables Biomedical entity recognition tasks to extract and identify important biomedical concept: DNA, RNA, CELL-LINE, CELL-TYPE, PROTEIN, and O classes with ease. The BioNLP/NLPBPA 2004 challenge datasets have been used and evaluated, resulted in promising outcomes in terms of biomedical recognition model performance.

Original languageEnglish
Title of host publicationProceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018)
EditorsPhill Kyu Rhee, Daniel Howard, Rezaul Bashar
Place of PublicationNJ, United States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages128-135
Number of pages8
ISBN (Electronic)9781728104041
ISBN (Print)9781728104058
DOIs
Publication statusPublished - 3 Dec 2018
EventInternational Conference on Machine Learning and Data Engineering 2018: iCMLDE 2018 - Western Sydney University, Sydney, Australia
Duration: 3 Dec 20187 Dec 2018
http://www.icmlde.net.au/Home.aspx

Conference

ConferenceInternational Conference on Machine Learning and Data Engineering 2018
CountryAustralia
CitySydney
Period3/12/187/12/18
Internet address

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  • Cite this

    Phan, R., Luu, T. M., Davey, R., & Chetty, G. (2018). Biomedical Named Entity Recognition Based on Hybrid Multistage CNN-RNN Learner. In P. K. Rhee, D. Howard, & R. Bashar (Eds.), Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018) (pp. 128-135). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icmlde.2018.00032