Michelle Morales


2019

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An Investigation of Deep Learning Systems for Suicide Risk Assessment
Michelle Morales | Prajjalita Dey | Thomas Theisen | Danny Belitz | Natalia Chernova
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

This work presents the systems explored as part of the CLPsych 2019 Shared Task. More specifically, this work explores the promise of deep learning systems for suicide risk assessment.

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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
Anastassia Loukina | Michelle Morales | Rohit Kumar
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

2018

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A Linguistically-Informed Fusion Approach for Multimodal Depression Detection
Michelle Morales | Stefan Scherer | Rivka Levitan
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Automated depression detection is inherently a multimodal problem. Therefore, it is critical that researchers investigate fusion techniques for multimodal design. This paper presents the first-ever comprehensive study of fusion techniques for depression detection. In addition, we present novel linguistically-motivated fusion techniques, which we find outperform existing approaches.

2017

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A Cross-modal Review of Indicators for Depression Detection Systems
Michelle Morales | Stefan Scherer | Rivka Levitan
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality

Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. This paper surveys these efforts by presenting the first cross-modal review of depression detection systems and discusses best practices and most promising approaches to this task.