Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data
Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han
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 workflow: 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.- Anthology ID:
- 2023.emnlp-demo.36
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Yansong Feng, Els Lefever
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 389–402
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-demo.36
- DOI:
- 10.18653/v1/2023.emnlp-demo.36
- Cite (ACL):
- Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, and Jiawei Han. 2023. Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 389–402, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (Zhong et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.emnlp-demo.36.pdf