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        <title>FORCE: A Benchmark Dataset for Foodborne Disease Outbreak and Recall Event Extraction from News</title>
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    <abstract>The escalating prevalence of food safety incidents within the food supply chain necessitates immediate action to protect consumers. These incidents encompass a spectrum of issues, including food product contamination and deliberate food and feed adulteration for economic gain leading to outbreaks and recalls. Understanding the origins and pathways of contamination is imperative for prevention and mitigation. In this paper, we introduce FORCE Foodborne disease Outbreak and ReCall Event extraction from openweb). Our proposed model leverages a multi-tasking sequence labeling architecture in conjunction with transformer-based document embeddings. We have compiled a substantial annotated corpus comprising relevant articles published between 2011 and 2023 to train and evaluate the model. The dataset will be publicly released with the paper. The event detection model demonstrates fair accuracy in identifying food-related incidents and outbreaks associated with organizations, as assessed through cross-validation techniques.</abstract>
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%0 Conference Proceedings
%T FORCE: A Benchmark Dataset for Foodborne Disease Outbreak and Recall Event Extraction from News
%A Jana, Sudeshna
%A Sinha, Manjira
%A Dasgupta, Tirthankar
%Y Xu, Dongfang
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F jana-etal-2024-force
%X The escalating prevalence of food safety incidents within the food supply chain necessitates immediate action to protect consumers. These incidents encompass a spectrum of issues, including food product contamination and deliberate food and feed adulteration for economic gain leading to outbreaks and recalls. Understanding the origins and pathways of contamination is imperative for prevention and mitigation. In this paper, we introduce FORCE Foodborne disease Outbreak and ReCall Event extraction from openweb). Our proposed model leverages a multi-tasking sequence labeling architecture in conjunction with transformer-based document embeddings. We have compiled a substantial annotated corpus comprising relevant articles published between 2011 and 2023 to train and evaluate the model. The dataset will be publicly released with the paper. The event detection model demonstrates fair accuracy in identifying food-related incidents and outbreaks associated with organizations, as assessed through cross-validation techniques.
%U https://aclanthology.org/2024.smm4h-1.38
%P 163-169

Markdown (Informal)

[FORCE: A Benchmark Dataset for Foodborne Disease Outbreak and Recall Event Extraction from News](https://aclanthology.org/2024.smm4h-1.38) (Jana et al., SMM4H-WS 2024)

• FORCE: A Benchmark Dataset for Foodborne Disease Outbreak and Recall Event Extraction from News (Jana et al., SMM4H-WS 2024)

ACL

• Sudeshna Jana, Manjira Sinha, and Tirthankar Dasgupta. 2024. FORCE: A Benchmark Dataset for Foodborne Disease Outbreak and Recall Event Extraction from News. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 163–169, Bangkok, Thailand. Association for Computational Linguistics.