Mostrando ítems 6-10 de 18

    • Delving into the Depths: Evaluating Depression Severity through BDI-biased Summaries 

      Aragón, Mario Ezra; Parapar, Javier; Losada, David E. (Association for Computational Linguistics, 2024-03)
      [Abstract]: Depression is a global concern suffered by millions of people, significantly impacting their thoughts and behavior. Over the years, heightened awareness, spurred by health campaigns and other initiatives, has ...
    • Keyword Embeddings for Query Suggestion 

      Gabín, Jorge; Ares, M. Eduardo; Parapar, Javier (Springer, Cham, 2023-04)
      [Abstract]: Nowadays, search engine users commonly rely on query suggestions to improve their initial inputs. Current systems are very good at recommending lexical adaptations or spelling corrections to users’ queries. ...
    • Experimental Analysis of the Relevance of Features and Effects on Gender Classification Models for Social Media Author Profiling 

      Piot, Paloma; Martín-Rodilla, Patricia; Parapar, Javier (SCITEPRESS, 2021)
      [Abstract] Automatic user profiling from social networks has become a popular task due to its commercial applications (targeted advertising, market studies...). Automatic profiling models infer demographic characteristics of ...
    • Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation 

      Gabín, Jorge; Pérez, Anxo; Parapar, Javier (MDPI, 2021)
      [Abstract] Depression is one of the most prevalent mental health diseases. Although there are effective treatments, the main problem relies on providing early and effective risk detection. Medical experts use self-reporting ...
    • A System for Explainable Answer Set Programming 

      Cabalar, Pedro; Fandinno, Jorge; Muñiz, Brais (Open Publishing Association, 2020-09-19)
      [Abstract] We present xclingo, a tool for generating explanations from ASP programs annotated with text and labels. These annotations allow tracing the application of rules or the atoms derived by them. The input of ...