SINAI at eRisk@CLEF 2022: Approaching Early Detection of Gambling and Eating Disorders with Natural Language Processing
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912591841853440 |
|---|---|
| author | Marmol-Romero, Alba Maria Jimenez-Zafra, Salud Maria Plaza-del-Arco, Flor Miriam Molina-Gonzalez, M. Dolores Martin-Valdivia, Maria-Teresa Montejo-Raez, Arturo |
| author_facet | Marmol-Romero, Alba Maria Jimenez-Zafra, Salud Maria Plaza-del-Arco, Flor Miriam Molina-Gonzalez, M. Dolores Martin-Valdivia, Maria-Teresa Montejo-Raez, Arturo |
| contents | This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, two of the proposed tasks have been addressed: i) Task 1 on the early detection of signs of pathological gambling, and ii) Task 3 on measuring the severity of the signs of eating disorders. The approach presented in Task 1 is based on the use of sentence embeddings from Transformers with features related to volumetry, lexical diversity, complexity metrics, and emotion-related scores, while the approach for Task 3 is based on text similarity estimation using contextualized word embeddings from Transformers. In Task 1, our team has been ranked in second position, with an F1 score of 0.808, out of 41 participant submissions. In Task 3, our team also placed second out of a total of 3 participating teams. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14806 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SINAI at eRisk@CLEF 2022: Approaching Early Detection of Gambling and Eating Disorders with Natural Language Processing Marmol-Romero, Alba Maria Jimenez-Zafra, Salud Maria Plaza-del-Arco, Flor Miriam Molina-Gonzalez, M. Dolores Martin-Valdivia, Maria-Teresa Montejo-Raez, Arturo Computation and Language This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, two of the proposed tasks have been addressed: i) Task 1 on the early detection of signs of pathological gambling, and ii) Task 3 on measuring the severity of the signs of eating disorders. The approach presented in Task 1 is based on the use of sentence embeddings from Transformers with features related to volumetry, lexical diversity, complexity metrics, and emotion-related scores, while the approach for Task 3 is based on text similarity estimation using contextualized word embeddings from Transformers. In Task 1, our team has been ranked in second position, with an F1 score of 0.808, out of 41 participant submissions. In Task 3, our team also placed second out of a total of 3 participating teams. |
| title | SINAI at eRisk@CLEF 2022: Approaching Early Detection of Gambling and Eating Disorders with Natural Language Processing |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.14806 |