Listar GI-IRlab-Congresos, conferencias, etc. por autor "Parapar, Javier"

Mostrando ítems 1-9 de 9

    • Building High-Quality Datasets for Information Retrieval Evaluation at a Reduced Cost 

      Otero, David; Valcarce, Daniel; Parapar, Javier; Barreiro, Álvaro (M D P I AG, 2019-08-01)
      [Abstract] Information Retrieval is not any more exclusively about document ranking. Continuously new tasks are proposed on this and sibling fields. With this proliferation of tasks, it becomes crucial to have a cheap way ...

    • 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 ...

    • Designing an Open Source Virtual Assistant 

      Pérez, Anxo; López-Otero, Paula; Parapar, Javier (MDPI AG, 2020-08-21)
      [Abstract] A chatbot is a type of agent that allows people to interact with an information repository using natural language. Nowadays, chatbots have been incorporated in the form of conversational assistants on the most ...

    • eRisk 2020: autolesiones y desafíos de la depresión 

      Losada, David E.; Crestani, Fabio; Parapar, Javier (Springer, 2020-04-08)
      [Abstract] This paper describes eRisk, the CLEF lab on early risk prediction on the Internet. eRisk started in 2017 as an attempt to set the experimental foundations of early risk detection. Over the last three editions ...

    • 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 ...

    • 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. ...

    • 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 ...

    • Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings 

      Landin, Alfonso; Parapar, Javier; Barreiro, Alvaro (Springer, 2020-04-08)
      [Abstract] Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of ...

    • Priors for Diversity and Novelty on Neural Recommender Systems 

      Landin, Alfonso; Valcarce, Daniel; Parapar, Javier; Barreiro, Álvaro (M D P I AG, 2019-07-31)
      [Abstract] PRIN is a neural based recommendation method that allows the incorporation of item prior information into the recommendation process. In this work we study how the system behaves in terms of novelty and diversity ...