@inproceedings{ahmad-etal-2025-csecu,
title = "{CSECU}-Learners at {S}em{E}val-2025 Task 9: Enhancing Transformer Model for Explainable Food Hazard Detection in Text",
author = "Ahmad, Monir and
Hossain, Md. Akram and
Chy, Abu Nowshed",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.168/",
pages = "1263--1268",
ISBN = "979-8-89176-273-2",
abstract = "Food contamination and associated illnesses represent significant global health challenges, leading to thousands of deaths worldwide. As the volume of food-related incident reports on web platforms continues to grow, there is a pressing demand for systems capable of detecting food hazards effectively. Furthermore, explainability in food risk detection is crucial for building trust in automated systems, allowing humans to validate predictions. SemEval-2025 Task 9 proposes a food hazard detection challenge to address this issue, utilizing content extracted from websites. This task is divided into two sub-tasks. Sub-task 1 involves classifying the type of hazard and product, while sub-task 2 focuses on identifying precise hazard and product ``vectors'' to offer detailed explanations for the predictions. This paper presents our participation in this task, where we introduce a transformer-based method. We fine-tune an enhanced version of the BERT transformer to process lengthy food incident reports. Additionally, we combine the transformer{'}s contextual embeddings to enhance its contextual representation for hazard and product ``vectors'' prediction. The experimental results reveal the competitive performance of our proposed method in this task. We have released our code at https://github.com/AhmadMonirCSECU/SemEval-2025{\_}Task9."
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<abstract>Food contamination and associated illnesses represent significant global health challenges, leading to thousands of deaths worldwide. As the volume of food-related incident reports on web platforms continues to grow, there is a pressing demand for systems capable of detecting food hazards effectively. Furthermore, explainability in food risk detection is crucial for building trust in automated systems, allowing humans to validate predictions. SemEval-2025 Task 9 proposes a food hazard detection challenge to address this issue, utilizing content extracted from websites. This task is divided into two sub-tasks. Sub-task 1 involves classifying the type of hazard and product, while sub-task 2 focuses on identifying precise hazard and product “vectors” to offer detailed explanations for the predictions. This paper presents our participation in this task, where we introduce a transformer-based method. We fine-tune an enhanced version of the BERT transformer to process lengthy food incident reports. Additionally, we combine the transformer’s contextual embeddings to enhance its contextual representation for hazard and product “vectors” prediction. The experimental results reveal the competitive performance of our proposed method in this task. We have released our code at https://github.com/AhmadMonirCSECU/SemEval-2025_Task9.</abstract>
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%0 Conference Proceedings
%T CSECU-Learners at SemEval-2025 Task 9: Enhancing Transformer Model for Explainable Food Hazard Detection in Text
%A Ahmad, Monir
%A Hossain, Md. Akram
%A Chy, Abu Nowshed
%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 ahmad-etal-2025-csecu
%X Food contamination and associated illnesses represent significant global health challenges, leading to thousands of deaths worldwide. As the volume of food-related incident reports on web platforms continues to grow, there is a pressing demand for systems capable of detecting food hazards effectively. Furthermore, explainability in food risk detection is crucial for building trust in automated systems, allowing humans to validate predictions. SemEval-2025 Task 9 proposes a food hazard detection challenge to address this issue, utilizing content extracted from websites. This task is divided into two sub-tasks. Sub-task 1 involves classifying the type of hazard and product, while sub-task 2 focuses on identifying precise hazard and product “vectors” to offer detailed explanations for the predictions. This paper presents our participation in this task, where we introduce a transformer-based method. We fine-tune an enhanced version of the BERT transformer to process lengthy food incident reports. Additionally, we combine the transformer’s contextual embeddings to enhance its contextual representation for hazard and product “vectors” prediction. The experimental results reveal the competitive performance of our proposed method in this task. We have released our code at https://github.com/AhmadMonirCSECU/SemEval-2025_Task9.
%U https://aclanthology.org/2025.semeval-1.168/
%P 1263-1268
Markdown (Informal)
[CSECU-Learners at SemEval-2025 Task 9: Enhancing Transformer Model for Explainable Food Hazard Detection in Text](https://aclanthology.org/2025.semeval-1.168/) (Ahmad et al., SemEval 2025)
ACL