Sadeque Farig, Xu Dongfang, Bethard Steven
School of Information, University of Arizona, 1103 E 2nd St, Tucson, AZ 85721.
CEUR Workshop Proc. 2017 Sep;1866. Epub 2017 Jul 13.
The 2017 CLEF eRisk pilot task focuses on automatically detecting depression as early as possible from a users' posts to Reddit. In this paper we present the techniques employed for the University of Arizona team's participation in this early risk detection shared task. We leveraged external information beyond the small training set, including a preexisting depression lexicon and concepts from the Unified Medical Language System as features. For prediction, we used both sequential (recurrent neural network) and non-sequential (support vector machine) models. Our models perform decently on the test data, and the recurrent neural models perform better than the non-sequential support vector machines while using the same feature sets.
2017年的CLEF eRisk试点任务聚焦于尽早从用户在Reddit上发布的帖子中自动检测抑郁症。在本文中，我们展示了亚利桑那大学团队参与这项早期风险检测共享任务所采用的技术。我们利用了小训练集之外的外部信息，包括一个已有的抑郁症词汇表和统一医学语言系统中的概念作为特征。对于预测，我们使用了序列模型（循环神经网络）和非序列模型（支持向量机）。我们的模型在测试数据上表现良好，并且在使用相同特征集的情况下，循环神经模型比非序列支持向量机表现得更好。
