Predicting Psychological Health from Childhood Essays with Convolutional Neural Networks for the CLPsych 2018 Shared Task (Team UKNLP)

Anthony Rios, Tung Tran, and Ramakanth Kavuluru

In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic (CLPsych 2018)

Abstract:

This paper describes the systems we developed for tasks A and B of the 2018 CLPsych shared task. The first task (task A) focuses on predicting behavioral health scores at age 11 using childhood essays. The second task (task B) asks participants to predict future psychological distress at ages 23, 33, 42, and 50 using the age 11 essays. We propose two convolutional neural network based methods that map each task to a regression problem. Among seven teams we ranked third on task A with disattenuated Pearson correlation (DPC) score of 0.5587. Likewise, we ranked third on task B with an average DPC score of 0.3062.

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@InProceedings{W18-0611,
  author = "Rios, Anthony and Tran, Tung and Kavuluru, Ramakanth",
  title = "Predicting Psychological Health from Childhood Essays with Convolutional Neural Networks for the CLPsych 2018 Shared Task (Team UKNLP)",
  booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  pages = "107--112",
  location = "New Orleans, LA",
  url = "http://aclweb.org/anthology/W18-0611"
}