Inter and Intra Document Attention for Depression Risk Assessment
Computation and Language
2019-07-02 v1 Machine Learning
Abstract
We take interest in the early assessment of risk for depression in social media users. We focus on the eRisk 2018 dataset, which represents users as a sequence of their written online contributions. We implement four RNN-based systems to classify the users. We explore several aggregations methods to combine predictions on individual posts. Our best model reads through all writings of a user in parallel but uses an attention mechanism to prioritize the most important ones at each timestep.
Cite
@article{arxiv.1907.00462,
title = {Inter and Intra Document Attention for Depression Risk Assessment},
author = {Diego Maupomé and Marc Queudot and Marie-Jean Meurs},
journal= {arXiv preprint arXiv:1907.00462},
year = {2019}
}
Comments
9 pages, in Proceedings of The 32nd Canadian Conference on Artificial Intelligence (Canadian AI 2019)