论文标题
个人健康的多任务学习在社交媒体上提及检测
Multi-task Learning for Personal Health Mention Detection on Social Media
论文作者
论文摘要
在社交媒体上检测个人健康提及对于补充现有的健康监视系统至关重要。但是，注释数据以大规模检测健康提及是一项艰巨的任务。本研究采用多任务学习框架来利用相关任务中可用的带注释的数据，以提高主要任务的绩效，以检测社交媒体文本中提到的个人健康经历。具体而言，我们专注于将情感信息作为辅助任务纳入目标任务。与强大的最新基准相比，我们的方法大大改善了广泛的个人健康提及的检测任务。
Detecting personal health mentions on social media is essential to complement existing health surveillance systems. However, annotating data for detecting health mentions at a large scale is a challenging task. This research employs a multitask learning framework to leverage available annotated data from a related task to improve the performance on the main task to detect personal health experiences mentioned in social media texts. Specifically, we focus on incorporating emotional information into our target task by using emotion detection as an auxiliary task. Our approach significantly improves a wide range of personal health mention detection tasks compared to a strong state-of-the-art baseline.
