Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet
| Autor: | Jérôme Azé, Waleed Ragheb, Sandra Bringay, Maximilien Servajean |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Clef Variation (linguistics) Mood 020204 information systems 0202 electrical engineering electronic engineering information engineering The Internet Word2vec Risk detection Language model Artificial intelligence Architecture business computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030285760 CLEF |
| Popis: | 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 Microelectronique 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. |
| Databáze: | OpenAIRE |
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