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Question Answering For Toxicological Information Extraction

📅 January 1, 2022 👤 Ferreira, Bruno Carlos Luís, Gonçalo Oliveira, Hugo, Amaro, Hugo et al. 📖 DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) 📊 1,564 citations

🤖 Plain-English Summary

Working with large amounts of text data has become hectic and time-consuming. That said, we propose an approach that relies on Question Answering for acquiring information from unstructured data, in our case, English PDF documents containing information about physicochemical and toxicological properties of chemical substances.

🔑 Key Findings

  • In order to reduce human effort, costs, and make the process more efficient, companies and organizations resort to intelligent algorithms to automate and assist the manual work.
  • This problem is also present in the field of toxicological analysis of chemical substances, where information needs to be searched from multiple documents.
  • That said, we propose an approach that relies on Question Answering for acquiring information from unstructured data, in our case, English PDF documents containing information about physicochemical and toxicological properties of chemical substances.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Jan 01, 2022
Journal DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)
Authors Ferreira, Bruno Carlos Luís, Gonçalo Oliveira, Hugo, Amaro, Hugo, Laranjeiro, Ângela, Silva, Catarina
DOI 10.4230/oasics.slate.2022.3
Citations 1,564
Source OpenAlex

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