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.

翻译：在社会媒体上发现个人健康,对于补充现有的健康监测系统至关重要,然而,在大规模检测健康的数据说明是一项具有挑战性的任务,这项研究采用多任务学习框架,利用相关任务中现有的附加说明的数据,改进在主要任务中的绩效,发现社交媒体文本中提到的个人健康经验,具体地说,我们的重点是将情感信息作为辅助任务,将情感信息纳入我们的目标任务中,我们的方法大大改进了广泛的个人健康发现任务,而不是一个最先进的基准。