Dictionaries and Decision Trees for the 2019 CLPsych Shared Task

Micah Iserman, Taleen Nalabandian, Molly Ireland


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
Venues:
CLPsych | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
188–194
Language:
URL:
https://www.aclweb.org/anthology/W19-3025
DOI:
10.18653/v1/W19-3025
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3025.pdf