@inproceedings{zhong-etal-2023-reaction,
title = "Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data",
author = "Zhong, Ming and
Ouyang, Siru and
Jiao, Yizhu and
Kargupta, Priyanka and
Luo, Leo and
Shen, Yanzhen and
Zhou, Bobby and
Zhong, Xianrui and
Liu, Xuan and
Li, Hongxiang and
Xiao, Jinfeng and
Jiang, Minhao and
Hu, Vivian and
Wang, Xuan and
Ji, Heng and
Burke, Martin and
Zhao, Huimin and
Han, Jiawei",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.36",
doi = "10.18653/v1/2023.emnlp-demo.36",
pages = "389--402",
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.",
}
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%0 Conference Proceedings
%T Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data
%A Zhong, Ming
%A Ouyang, Siru
%A Jiao, Yizhu
%A Kargupta, Priyanka
%A Luo, Leo
%A Shen, Yanzhen
%A Zhou, Bobby
%A Zhong, Xianrui
%A Liu, Xuan
%A Li, Hongxiang
%A Xiao, Jinfeng
%A Jiang, Minhao
%A Hu, Vivian
%A Wang, Xuan
%A Ji, Heng
%A Burke, Martin
%A Zhao, Huimin
%A Han, Jiawei
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhong-etal-2023-reaction
%X 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.
%R 10.18653/v1/2023.emnlp-demo.36
%U https://aclanthology.org/2023.emnlp-demo.36
%U https://doi.org/10.18653/v1/2023.emnlp-demo.36
%P 389-402
Markdown (Informal)
[Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data](https://aclanthology.org/2023.emnlp-demo.36) (Zhong et al., EMNLP 2023)
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.