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Abstract
Natural Language Processing (NLP) offers promising potential for advancing chemistry research by exploring understudied topics, accelerating the research process, and reducing the costs of lab experimentation (1). For example, the U.S. military seeks to utilize NLP to replace toxic and limited resources, like Teflon, with more accessible alternatives. Our objective is to assess the accuracy of two models, Skip-Gram and BERT, after training them on chemistry data. Our work with Skip-Gram aims to replicate and enhance prior research results, while our work with BERT aims to highlight its Sub-Word method for analysis.
Publication Date
2023
Keywords
Natural language processing, language modeling, visualization, transformers
Disciplines
Engineering
Faculty Advisor/Mentor
Bridget McInnes
Is Part Of
VCU College of Engineering Summer REU Program
Rights
© The Author(s)