用领域感知Transformer检测失眠与食品安全事件，获国际竞赛第一。
CareLab at #SMM4H-HeaRD 2025: Insomnia Detection and Food Safety Event Extraction with Domain-Aware Transformers
- 采用RoBERTa等编码器模型，结合GPT-4增强数据
- 在食品安全事件抽取中达F1 0.958，排名第一
- 适用于医疗文本与新闻事件的精准提取任务
本文介绍了我们在SMM4H-HeaRD 2025共享任务中的系统表现，涵盖任务4（子任务1、2a、2b）和任务5（子任务1、2）。任务4聚焦于临床笔记中失眠相关表述的识别，任务5则针对新闻文章中的食品安全事件抽取。我们参与了所有子任务，并报告关键结果，尤其在任务5子任务1中，系统在测试集上取得F1分数0.958，获得第一名。为达成此效果，我们采用基于编码器的模型（如RoBERTa），并利用GPT-4进行数据增强。本文详细阐述了我们的方法，包括预处理流程、模型架构及各子任务的针对性调整。
原文摘要 · Abstract (English)
This paper presents our system for the SMM4H-HeaRD 2025 shared tasks, specifically Task 4 (Subtasks 1, 2a, and 2b) and Task 5 (Subtasks 1 and 2). Task 4 focused on detecting mentions of insomnia in clinical notes, while Task 5 addressed the extraction of food safety events from news articles. We participated in all subtasks and report key findings across them, with particular emphasis on Task 5 Subtask 1, where our system achieved strong performance-securing first place with an F1 score of 0.958 on the test set. To attain this result, we employed encoder-based models (e.g., RoBERTa), alongside GPT-4 for data augmentation. This paper outlines our approach, including preprocessing, model architecture, and subtask-specific adaptations
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