Identifying Chemical Reactions and Their Associated Attributes in Patents.

Autor: Mahendran D; Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States., Gurdin G; Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States., Lewinski N; Department of Life Science and Chemical Engineering, Virginia Commonwealth University, Richmond, VA, United States., Tang C; Department of Life Science and Chemical Engineering, Virginia Commonwealth University, Richmond, VA, United States., McInnes BT; Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States.
Jazyk: angličtina
Zdroj: Frontiers in research metrics and analytics [Front Res Metr Anal] 2021 Jul 12; Vol. 6, pp. 688353. Date of Electronic Publication: 2021 Jul 12 (Print Publication: 2021).
DOI: 10.3389/frma.2021.688353
Abstrakt: Chemical patents are an essential source of information about novel chemicals and chemical reactions. However, with the increasing volume of such patents, mining information about these chemicals and chemical reactions has become a time-intensive and laborious endeavor. In this study, we present a system to extract chemical reaction events from patents automatically. Our approach consists of two steps: 1) named entity recognition (NER)-the automatic identification of chemical reaction parameters from the corresponding text, and 2) event extraction (EE)-the automatic classifying and linking of entities based on their relationships to each other. For our NER system, we evaluate bidirectional long short-term memory (BiLSTM)-based and bidirectional encoder representations from transformer (BERT)-based methods. For our EE system, we evaluate BERT-based, convolutional neural network (CNN)-based, and rule-based methods. We evaluate our NER and EE components independently and as an end-to-end system, reporting the precision, recall, and F 1 score. Our results show that the BiLSTM-based method performed best at identifying the entities, and the CNN-based method performed best at extracting events.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2021 Mahendran, Gurdin, Lewinski, Tang and McInnes.)
Databáze: MEDLINE
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