SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing

Fuente: arXiv
Salvato in:
Dettagli Bibliografici
Autori principali: Marmol-Romero, Alba Maria, Plaza-del-Arco, Flor Miriam, Montejo-Raez, Arturo
Natura: Preprint
Pubblicazione: 2025
Soggetti:
Accesso online:
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
_version_ 1866915501060390912
author Marmol-Romero, Alba Maria
Plaza-del-Arco, Flor Miriam
Montejo-Raez, Arturo
author_facet Marmol-Romero, Alba Maria
Plaza-del-Arco, Flor Miriam
Montejo-Raez, Arturo
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2509_14797
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
Marmol-Romero, Alba Maria
Plaza-del-Arco, Flor Miriam
Montejo-Raez, Arturo
Computation and Language
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.
title SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
topic Computation and Language
url https://arxiv.org/abs/2509.14797