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Question Answering For Toxicological Information Extraction

Authors Bruno Carlos Luís Ferreira, Hugo Gonçalo Oliveira , Hugo Amaro, Ângela Laranjeiro, Catarina Silva

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Author Details

Bruno Carlos Luís Ferreira
  • DEI, CISUC, University of Coimbra, Portugal
Hugo Gonçalo Oliveira
  • DEI, CISUC, University of Coimbra, Portugal
Hugo Amaro
  • LIS, Instituto Pedro Nunes, Portugal
Ângela Laranjeiro
  • Cosmedesk, Coimbra, Portugal
Catarina Silva
  • DEI, CISUC, University of Coimbra, Portugal

Funding

This work was partially funded by: the project SafetyDesk: Smart Toxicological Analysis of Chemical Substances (CENTRO-01-0247-FEDER-113485), co-financed by the European Regional Development Fund (FEDER), through Portugal 2020 (PT2020), and by the Regional Operational Programme Centro 2020; and national funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project CISUC - UID/CEC/00326/2020 and by the European Social Fund, through the Regional Operational Program Centro 2020.

Cite AsGet BibTex

Bruno Carlos Luís Ferreira, Hugo Gonçalo Oliveira, Hugo Amaro, Ângela Laranjeiro, and Catarina Silva. Question Answering For Toxicological Information Extraction. In 11th Symposium on Languages, Applications and Technologies (SLATE 2022). Open Access Series in Informatics (OASIcs), Volume 104, pp. 3:1-3:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/OASIcs.SLATE.2022.3

Abstract

Working with large amounts of text data has become hectic and time-consuming. 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. Experimental results confirm that our approach achieves promising results which can be applicable in the business scenario, especially if further revised by humans.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Information extraction
Keywords
  • Information Extraction
  • Question Answering
  • Transformers
  • Toxicological Analysis

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