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CLPsych 2015 Shared Task: Depression and PTSD on Twitter

机译:CLPsych 2015共享任务:Twitter上的抑郁症和PTSD

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This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures.
机译:本文概述了计算语言学和临床心理学(CLPsych)2015共享和未共享的任务。这些任务旨在对社交媒体中与心理健康相关的语言建模的各种方法进行逐个比较。用于这些任务的数据来自Twitter用户,这些用户陈述了抑郁症或创伤后应激障碍(PTSD)的诊断以及人口统计学上匹配的社区控制。这项未共享的任务是于2014年11月在约翰·霍普金斯大学举行的一次黑客马拉松,以探索数据,并且这项共享任务是远程进行的,每个参与团队都为保留的一组测试用户提交了分数。共同的任务包括三个二进制分类实验:(1)抑郁与对照,(2)PTSD与对照,以及(3)抑郁与PTSD。主要通过分类器的平均精度对分类器进行比较,尽管与此同时使用了许多其他指标,以便对绩效指标进行更细微的解释。

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