Listar Information Retrieval Lab (IRlab) por autor "Parapar, Javier"
Mostrando ítems 1-13 de 13
-
Automatic depression score estimation with word embedding models
Pérez, Anxo; Parapar, Javier; Barreiro, Álvaro (Elsevier, 2022)[Abstract]: Depression is one of the most common mental health illnesses. The biggest obstacle lies in an efficient and early detection of the disorder. Self-report questionnaires are the instruments used by medical experts ... -
Building Cultural Heritage Reference Collections from Social Media through Pooling Strategies: The Case of 2020’s Tensions Over Race and Heritage
Otero, David; Martín-Rodilla, Patricia; Parapar, Javier (2021)[Abstract] Social networks constitute a valuable source for documenting heritage constitution processes or obtaining a real-time snapshot of a cultural heritage research topic. Many heritage researchers use social networks ... -
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 ... -
How Discriminative Are Your Qrels? How To Study the Statistical Significance of Document Adjudication Methods
Otero, David David; Parapar, Javier; Ferro, Nicola (Association for Computing Machinery, 2023-10)[Abstract]: Creating test collections for offline retrieval evaluation requires human effort to judge documents' relevance. This expensive activity motivated much work in developing methods for constructing benchmarks with ... -
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 ... -
Using score distributions to compare statistical significance tests for information retrieval evaluation
Parapar, Javier; Losada, David E.; Presedo-Quindimil, Manuel-Antonio; Barreiro, Álvaro (Willey, 2019-01-11)[Abstract] Statistical significance tests can provide evidence that the observed difference in performance between two methods is not due to chance. In Information Retrieval, some studies have examined the validity and ...