Abstract
In this summary, we discuss our approach to the CLPsych Shared Task and its initial results. For our predictions in each task, we used a recursive partitioning algorithm (decision trees) to select from our set of features, which were primarily dictionary scores and counts of individual words. We focused primarily on Task A, which aimed to predict suicide risk, as rated by a team of expert clinicians (Shing et al., 2018), based on language used in SuicideWatch posts on Reddit. Category-level findings highlight the potential importance of social and moral language categories. Word-level correlates of risk levels underline the value of fine-grained data-driven approaches, revealing both theory-consistent and potentially novel correlates of suicide risk that may motivate future research.- Anthology ID:
- W19-3025
- Volume:
- Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Kate Niederhoffer, Kristy Hollingshead, Philip Resnik, Rebecca Resnik, Kate Loveys
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 188–194
- Language:
- URL:
- https://aclanthology.org/W19-3025
- DOI:
- 10.18653/v1/W19-3025
- Cite (ACL):
- Micah Iserman, Taleen Nalabandian, and Molly Ireland. 2019. Dictionaries and Decision Trees for the 2019 CLPsych Shared Task. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pages 188–194, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Dictionaries and Decision Trees for the 2019 CLPsych Shared Task (Iserman et al., CLPsych 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W19-3025.pdf