activity
20232026
most citedKeyword Embeddings for Query Suggestion

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR2026

Lost in the Evidence? Reproducing Document Position and Context Size Effects in RAG

Jorge Gabín, Anxo Perez, Javier Parapar

Retrieval-Augmented Generation (RAG) systems rely on retrieved documents being concatenated into a model's input context, making both document ordering and context size critical ye…

cs.CL2025

Who Is the Story About? Protagonist Entity Recognition in News

Jorge Gabín, M. Eduardo Ares, Javier Parapar

News articles often reference numerous organizations, but traditional Named Entity Recognition (NER) treats all mentions equally, obscuring which entities genuinely drive the narra…

cs.IR2024

Beyond Questions: Leveraging ColBERT for Keyphrase Search

Jorge Gabín, Javier Parapar, Craig Macdonald

While question-like queries are gaining popularity and search engines' users increasingly adopt them, keyphrase search has traditionally been the cornerstone of web search. This qu…

cs.IR2024

Enhancing Automatic Keyphrase Labelling with Text-to-Text Transfer Transformer (T5) Architecture: A Framework for Keyphrase Generation and Filtering

Jorge Gabín, M. Eduardo Ares, Javier Parapar

Automatic keyphrase labelling stands for the ability of models to retrieve words or short phrases that adequately describe documents' content. Previous work has put much effort int…

cs.IR2023★ 1 cited

Keyword Embeddings for Query Suggestion

Jorge Gabín, M. Eduardo Ares, Javier Parapar

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 corr…