In online forums like Reddit, users share their experiences with medical
conditions and treatments, including making claims, asking questions, and
discussing the effects of treatments on their health. Building systems to
understand this information can effectively monitor the spread of
misinformation and verify user claims. The Task-8 of the 2023 International
Workshop on Semantic Evaluation focused on medical applications, specifically
extracting patient experience- and medical condition-related entities from user
posts on social media. The Reddit Health Online Talk (RedHot) corpus contains
posts from medical condition-related subreddits with annotations characterizing
the patient experience and medical conditions. In Subtask-1, patient experience
is characterized by personal experience, questions, and claims. In Subtask-2,
medical conditions are characterized by population, intervention, and outcome.
For the automatic extraction of patient experiences and medical condition
information, as a part of the challenge, we proposed language-model-based
extraction systems that ranked $3^{rd}$ on both subtasks' leaderboards. In this
work, we describe our approach and, in addition, explore the automatic
extraction of this information using domain-specific language models and the
inclusion of external knowledge.

利用基于语言模型的提取系统以及外部知识，从社交媒体用户帖子中自动提取与医疗状况相关的患者经历和医疗状况信息，可用于有效监测医疗谣言的传播和验证用户主张。

MasonNLP+ 在 SemEval-2023 任务 8 中的应用：基于知识增强预训练语言模型从社交媒体中提取医疗问题、经验和主张

MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions,  Experiences and Claims from Social Media using Knowledge-Augmented  Pre-trained Language Models