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Chemical Named Entity Recognition Using Deep Learning Techniques: A Review
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Author(s): Hema R. (Department of Computer Science, University of Madras, India)and Ajantha Devi (AP3 Solutions, India)
Copyright: 2021
Pages: 15
Source title:
Deep Natural Language Processing and AI Applications for Industry 5.0
Source Author(s)/Editor(s): Poonam Tanwar (Manav Rachna International Institute of Research and Studies, India), Arti Saxena (Manav Rachna International Institute of Research and Studies, India)and C. Priya (Vels Institute of Science, Technology, and Advanced Studies, India)
DOI: 10.4018/978-1-7998-7728-8.ch004
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Abstract
Chemical entities can be represented in different forms like chemical names, chemical formulae, and chemical structures. Because of the different classification frameworks for chemical names, the task of distinguishing proof or extraction of chemical elements with less ambiguous is considered a major test. Compound named entity recognition (NER) is the initial phase in any chemical-related data extraction strategy. The majority of the chemical NER is done utilizing dictionary-based, rule-based, and machine learning procedures. Recently, deep learning methods have evolved, and, in this chapter, the authors sketch out the various deep learning techniques applied for chemical NER. First, the authors introduced the fundamental concepts of chemical named entity recognition, the textual contents of chemical documents, and how these chemicals are represented in chemical literature. The chapter concludes with the strengths and weaknesses of the above methods and also the types of the chemical entities extracted.
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