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.
|CEUR Workshop Proceedings
|Published - 2017
|18th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2017 - Dublin, Ireland
Duration: Sep 11 2017 → Sep 14 2017
ASJC Scopus subject areas
- General Computer Science