Character level and word level embedding with bidirectional LSTM – Dynamic recurrent neural network for biomedical named entity recognition from literature-Reference-Cited by-同舟云学术

Character level and word level embedding with bidirectional LSTM – Dynamic recurrent neural network for biomedical named entity recognition from literature

Published:2020-12 Issue: Volume:112 Page:103609
ISSN:1532-0464
Container-title:Journal of Biomedical Informatics
language:en
Short-container-title:Journal of Biomedical Informatics

Author:

Gajendran Sudhakaran^ORCID,D Manjula,Sugumaran Vijayan

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference56 articles.

1. An attention-based effective neural model for drug-drug interactions extraction;Zheng;BMC Bioinf.,2017

2. A comparative study for biomedical named entity recognition;Wang;Int. J. Mach. Learn. Cyber,2018

3. Towards a protein–protein interaction information extraction system: recognizing named entities;Danger;Knowl.-Based Syst.,2014

4. Bagash FT.A biomedical named entity recognition using machine learning classifiers and rich feature set;Al-Hegami;Int. J. Comput. Sci. Netw. Secur.,2017

5. A multi-strategy approach to biological named entity recognition;Atkinson;Expert Syst. Appl.,2012

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