Applying a Named Entity Recognition Model to Chemical Patents

Authors

WanXiang Chen, University at BuffaloFollow
Zachery Nolan, University of MemphisFollow
Christina Tang, Virginia Commonwealth UniversityFollow
Bridget T. McInnes, Virginia Commonwealth UniversityFollow

Files

Abstract

Named Entity Recognition (NER) is an application of Natural Language Processing (NLP) that involves recording entities contained in a text excerpt and classifying them into predefined category types. Unstructured text, or text without a predefined format is converted into structured text that can serve as input to an NER model. Here, we use a pretrained NER model to extract and classify chemical entities from patents/ This streamlines the process of determining the number and types of chemical agents used in a specific patent. This information may be useful for researchers, pharmaceutical companies, and agencies such as the FDA.

Publication Date

2024

Keywords

Natural Language Processing, Information Extraction, Chemical Named Entity Recognition

Disciplines

Engineering

Faculty Advisor/Mentor

Bridget McInnes

Is Part Of

VCU College of Engineering Summer REU Program

Rights

© The Author(s)