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2019 | OriginalPaper | Chapter

Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet

Authors : Waleed Ragheb, Jérôme Azé, Sandra Bringay, Maximilien Servajean

Published in: Experimental IR Meets Multilinguality, Multimodality, and Interaction

Publisher: Springer International Publishing

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Abstract

Early risk detection can be useful in different areas, particularly those related to health and safety. Two tasks are proposed at CLEF eRisk-2018 for predicting mental disorder using users posts on Reddit. Depression and anorexia disorders must be detected as early as possible. In this paper, we extend the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier) in both tasks. The proposed model addresses this problem by modeling the temporal mood variation detected from user posts. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-the-art text vectorizations and deep language models. The proposed models perform comparably to other contributions while further experiments shows that attentive based deep language models outperformed the shallow learning text vectorizations.

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Title: Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet
Authors: Waleed Ragheb
Jérôme Azé
Sandra Bringay
Maximilien Servajean
Publisher: Springer International Publishing
Book: Experimental IR Meets Multilinguality, Multimodality, and Interaction
Print ISBN: 978-3-030-28576-0

Electronic ISBN: 978-3-030-28577-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-28577-7_21

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