2023/07/04 by Ming Zhong, Zhong, Ming, Siru Ouyang +11 · 2 citations
Computer Science · Materials Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2307.01448
openalex publication_date 2023/07/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design. Despite the importance of extracting structured reactions from scientific literature, data annotation for this purpose is cost-prohibitive due to the significant labor required from domain experts. Consequently, the scarcity of sufficient training data poses an obstacle to the progress of related models in this domain. In this paper, we propose ReactIE, which combines two weakly supervised approaches for pre-training. Our method utilizes frequent patterns within the text as linguistic cues to identify specific characteristics of chemical reactions. Additionally, we adopt synthetic data from patent records as distant supervision to incorporate domain knowledge into the model. Experiments demonstrate that ReactIE achieves substantial improvements and outperforms all existing baselines.