Uarizona at the CLEF eRisk 2017 pilot task: Linear and recurrent models for early depression detection

Farig Sadeque, Dongfang Xu, Steven Bethard

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations

Abstract

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.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume1866
StatePublished - 2017
Event18th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2017 - Dublin, Ireland
Duration: Sep 11 2017Sep 14 2017

ASJC Scopus subject areas

  • Computer Science(all)

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