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@INCOLLECTION{Burdisso_SPRINGER_2022,
         author = {Burdisso, Sergio and Cagnina, Leticia and Errecalde, Marcelo and Montes-y-G{\'{o}}mez, Manuel},
       projects = {Idiap},
          title = {Two Simple and Domain-independent Approaches for Early Detection of Anorexia},
      booktitle = {Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project},
        edition = {202},
           year = {2022},
          pages = {159-182},
      publisher = {Springer International Publishing},
           isbn = {978-3-031-04431-1},
            url = {https://doi.org/10.1007/978-3-031-04431-1_7},
            doi = {10.1007/978-3-031-04431-1_7},
       abstract = {In this chapter, we describe the participation of our research team in the eRisk addressing the two editions of the early anorexia detection task. We used two domain-independent approaches to address this task. The first approach is based on a temporal-aware document representation, whereas the second one consists of a simple, interpretable, and novel text classification model specially designed for addressing early risk detection scenarios. Regarding the obtained results, in the first edition, we achieved the best ERDE5 value among all participant models using the first approach, whereas with the second one, the best precision (0.91). Besides, using the latter approach, in the second edition, we were able to achieve the best values for both ERDE5 and ERDE50, and also promising results in terms of the ranking-based metrics, obtaining the best values, consistently, across all four rankings.},
            pdf = {https://publications.idiap.ch/attachments/papers/2023/Burdisso_SPRINGER_2022.pdf}
}