Chemical Named Entity Recognition Using Deep Learning Techniques

Author:

R. Hema1,Devi Ajantha2ORCID

Affiliation:

1. Department of Computer Science, University of Madras, India

2. AP3 Solutions, India

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.

Publisher

IGI Global

Reference53 articles.

1. Akhondi, S. A., Singh, B., & van der Host, E. (2013). A dictionary-and grammar-based chemical named entity recognizer. BioCreative Challenge Evaluation Workshop, 2, 113.

2. PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts

3. Representation Learning: A Review and New Perspectives

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