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        <title>MyMy at SemEval-2025 Task 9: A Robust Knowledge-Augmented Data Approach for Reliable Food Hazard Detection</title>
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    <abstract>The Food Hazard Detection (SemEval-2025 Task 9) advances explainable classification of food-incident reports collected from web sources, including social media and regulatory agency websites, to support timely risk mitigation for public health and the economy. This task is complicated by a highly imbalanced, long-tail label distribution and the need for transparent, reliable AI. We present a robust Knowledge-Augmented Data approach that integrates Retrieval-Augmented Generation (RAG) with domain-specific knowledge from the PubMed API to enrich and balance the training data. Our method leverages domain-specific knowledge to expand datasets and curate high-quality data that enhances overall data integrity. We hypothesize that Knowledge-Augmented Data improves Macro-F1 scores, the primary evaluation metric. Our approach achieved a top-2 ranking across both subtasks, demonstrating its effectiveness in advancing NLP applications for food safety and contributing to more reliable food hazard detection.</abstract>
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%0 Conference Proceedings
%T MyMy at SemEval-2025 Task 9: A Robust Knowledge-Augmented Data Approach for Reliable Food Hazard Detection
%A Phan, Ben
%A Chiang, Jung-Hsien
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F phan-chiang-2025-mymy
%X The Food Hazard Detection (SemEval-2025 Task 9) advances explainable classification of food-incident reports collected from web sources, including social media and regulatory agency websites, to support timely risk mitigation for public health and the economy. This task is complicated by a highly imbalanced, long-tail label distribution and the need for transparent, reliable AI. We present a robust Knowledge-Augmented Data approach that integrates Retrieval-Augmented Generation (RAG) with domain-specific knowledge from the PubMed API to enrich and balance the training data. Our method leverages domain-specific knowledge to expand datasets and curate high-quality data that enhances overall data integrity. We hypothesize that Knowledge-Augmented Data improves Macro-F1 scores, the primary evaluation metric. Our approach achieved a top-2 ranking across both subtasks, demonstrating its effectiveness in advancing NLP applications for food safety and contributing to more reliable food hazard detection.
%U https://aclanthology.org/2025.semeval-1.112/
%P 812-822

Markdown (Informal)

[MyMy at SemEval-2025 Task 9: A Robust Knowledge-Augmented Data Approach for Reliable Food Hazard Detection](https://aclanthology.org/2025.semeval-1.112/) (Phan & Chiang, SemEval 2025)

• MyMy at SemEval-2025 Task 9: A Robust Knowledge-Augmented Data Approach for Reliable Food Hazard Detection (Phan & Chiang, SemEval 2025)

ACL

• Ben Phan and Jung-Hsien Chiang. 2025. MyMy at SemEval-2025 Task 9: A Robust Knowledge-Augmented Data Approach for Reliable Food Hazard Detection. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 812–822, Vienna, Austria. Association for Computational Linguistics.