Abstract
Chemical reactions, as a core entity in the realm of chemistry, hold crucial implications in diverse areas ranging from hands-on laboratory research to advanced computational drug design. Despite a burgeoning interest in employing NLP techniques to extract these reactions, aligning this task with the real-world requirements of chemistry practitioners remains an ongoing challenge. In this paper, we present REACTION MINER, a system specifically designed to interact with raw scientific literature, delivering precise and more informative chemical reactions. Going beyond mere extraction, REACTION MINER integrates a holistic work-flow: it accepts PDF files as input, bypassing the need for pre-processing and bolstering user accessibility. Subsequently, a text segmentation module ensures that the refined text encapsulates complete chemical reactions, augmenting the accuracy of extraction. Moreover, REACTION MINER broadens the scope of existing pre-defined reaction roles, including vital attributes previously neglected, thereby offering a more comprehensive depiction of chemical reactions. Evaluations conducted by chemistry domain users highlight the efficacy of each module in our system, demonstrating REACTION MINER as a powerful tool in this field.
Original language | English (US) |
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Pages | 389-402 |
Number of pages | 14 |
State | Published - 2023 |
Event | 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Hybrid, Singapore, Singapore Duration: Dec 6 2023 → Dec 10 2023 |
Conference
Conference | 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 |
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Country/Territory | Singapore |
City | Hybrid, Singapore |
Period | 12/6/23 → 12/10/23 |
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems