SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
This work addresses a domain-specific problem for mental health monitoring, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the early detection of pathological gambling signs using NLP, achieving an F1 score of 0.126 and ranking seventh out of 49 submissions, with the highest recall and early detection metrics.
This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, one of the proposed tasks has been addressed: Task 2 on the early detection of signs of pathological gambling. The approach presented in Task 2 is based on pre-trained models from Transformers architecture with comprehensive preprocessing data and data balancing techniques. Moreover, we integrate Long-short Term Memory (LSTM) architecture with automodels from Transformers. In this Task, our team has been ranked in seventh position, with an F1 score of 0.126, out of 49 participant submissions and achieves the highest values in recall metrics and metrics related to early detection.