화학공학소재연구정보센터
학회 한국화학공학회
학술대회 2022년 봄 (04/20 ~ 04/23, 제주국제컨벤션센터)
권호 28권 1호, p.1148
발표분야 [주제 12] 화학공학일반(부문위원회 발표)
제목 Automatic Chemical Reaction Data Extraction for Process Design using Artificial Intelligence-based Natural Language Processing Model
초록 The volume of chemical documents has been rapidly increasing in the last years. To evaluate the newly discovered chemical reaction in those, process design is necessary to more accurately estimate CAPEX (Capital Expenditure) and OPEX (Operating Expenditure). However, there is no database containing sufficient data for process design such as conversion, composition, temperature, pressure. Therefore, process engineers spend a lot of time searching reaction data among the growing collection of papers and patents. With the advancement of Natural language processing, chemical text mining has been noticed as key solution for transforming unstructured data into a more structured data format easy to process for analysis. In this research, we investigated the effectiveness of the state-of-the-art Artificial Intelligence-based Natural Language Processing Model, BERT(Bidirectional Encoder Representations from Transformers), on extracting reaction information.
저자 전은미, 최현수, 이철진
소속 중앙대
키워드 공정시스템(Process Systems Engineering)
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