UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet

Abstract

[EN] This paper presents the contributions of the UPV-Symanto team, a collaboration between Symanto Research and the PRHLT Center, in the eRisk 2021 shared tasks on gambling addiction, self-harm detection and prediction of depression levels. We have used a variety of models and techniques, including Transformers, hierarchical attention networks with multiple linguistic features, a dedicated early alert decision mechanism, and temporal modelling of emotions. We trained the models using additional training data that we collected and annotated thanks to expert psychologists. Our emotions-over-time model obtained the best results for the depression severity task in terms of ACR (and second best according to ADODL). For the self-harm detection task, our Transformer-based model obtained the best absolute result in terms of ERDE5 and we ranked equal first in terms of speed and latency.The authors from Universitat Politècnica de València thank the EU-FEDER Comunitat Valenciana 2014-2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana. We would like to thank the two anonymous reviewers who helped us improve this paper.Basile, A.; Chinea-Ríos, M.; Uban, A.; Müller, T.; Rössler, L.; Yenikent, S.; Chulvi-Ferriols, MA.... (2021). UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet. CEUR. 908-927. http://hdl.handle.net/10251/19067090892