• Multiple-Choice Question Answering Models for Automatic Depression Severity Estimation 

  Gabín Brenlla, Jorge Juan; 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 ...

• aspBEEF: Explaining Predictions Through Optimal Clustering 

  Cabalar, Pedro; Martín, Rodrigo; Muñiz, Brais; Pérez, Gilberto (MDPI AG, 2020-08-28)

  [Abstract] In this paper we introduce aspBEEF, a tool for generating explanations for the outcome of an arbitrary machine learning classifier. This is done using Grover’s et al. framework known as Balanced English ...

• 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 ...

• 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 ...

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