This project aims to develop an automated tool for extracting chemical reaction information from scientific documents with high precision and recall. Leveraging recent advances in artificial intelligence, the tool will identify chemical entities, their roles, and reaction steps directly from unstructured text.
The system will be designed for easy integration into production environments, ensuring scalability and robustness. The approach should be informed by the latest international benchmarks, including the CLEF 2020 ChEMU Lab, where state-of-the-art methods achieved near-human performance in chemical reaction extraction from patents.
The resulting tool will accelerate chemical knowledge discovery and support innovation in the chemical and pharmaceutical industries.
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