Search Results for author: Javier Parapar

Found 7 papers, 2 papers with code

MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection

1 code implementation12 Jan 2024 Paloma Piot, Patricia Martín-Rodilla, Javier Parapar

Hate speech represents a pervasive and detrimental form of online discourse, often manifested through an array of slurs, from hateful tweets to defamatory posts.

Hate Speech Detection

Conversations in Galician: a Large Language Model for an Underrepresented Language

1 code implementation7 Nov 2023 Eliseo Bao, Anxo Pérez, Javier Parapar

Additionally, as a demonstration of the dataset utility, we fine-tuned LLaMA-7B to comprehend and respond in Galician, a language not originally supported by the model, by following the Alpaca format.

Language Modelling Large Language Model

Explainable Depression Symptom Detection in Social Media

no code implementations20 Oct 2023 Eliseo Bao Souto, Anxo Pérez, Javier Parapar

Recent research has pointed out the importance of using clinical markers, such as the use of symptoms, to improve trust in the computational models by health professionals.

In-Context Learning

DepreSym: A Depression Symptom Annotated Corpus and the Role of LLMs as Assessors of Psychological Markers

no code implementations21 Aug 2023 Anxo Pérez, Marcos Fernández-Pichel, Javier Parapar, David E. Losada

The eRisk initiative fosters research on this area and has recently proposed a new ranking task focused on developing search methods to find sentences related to depressive symptoms.

Depression Detection

How Discriminative Are Your Qrels? How To Study the Statistical Significance of Document Adjudication Methods

no code implementations18 Aug 2023 David Otero, Javier Parapar, Nicola Ferro

Researchers evaluate the quality of those methods by measuring the correlation between the known gold ranking of systems under the full collection and the observed ranking of systems under the lower-cost one.

Keyword Embeddings for Query Suggestion

no code implementations19 Jan 2023 Jorge Gabín, M. Eduardo Ares, Javier Parapar

Nowadays, search engine users commonly rely on query suggestions to improve their initial inputs.

Retrieval Sentence +2

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