Tootfinder

SINAI at eRisk@CLEF 2022: Approaching Early Detection of Gambling and Eating Disorders with Natural Language Processing
Alba Maria Marmol-Romero, Salud Maria Jimenez-Zafra, Flor Miriam Plaza-del-Arco, M. Dolores Molina-Gonzalez, Maria-Teresa Martin-Valdivia, Arturo Montejo-Raez
https://arxiv.org/abs/2509.14806

SINAI at eRisk@CLEF 2022: Approaching Early Detection of Gambling and Eating Disorders with Natural Language Processing
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…

DataLens: Enhancing Dataset Discovery via Network Topologies
Ana\"is Ollagnier (CRISAM, CNRS, MARIANNE), Aline Menin (WIMMICS, Laboratoire I3S - SPARKS)
https://arxiv.org/abs/2507.23515

DataLens: Enhancing Dataset Discovery via Network Topologies
The rapid growth of publicly available textual resources, such as lexicons and domain-specific corpora, presents challenges in efficiently identifying relevant resources. While repositories are emerging, they often lack advanced search and exploration features. Most search methods rely on keyword queries and metadata filtering, which require prior knowledge and fail to reveal connections between resources. To address this, we present DataLens, a web-based platform that combines faceted search w…