Marcos Martínez Galindo — accepted AI-conference papers

2025

OpenBioNER: Lightweight Open-Domain Biomedical Named Entity Recognition Through Entity Type Description

Alessio Cocchieri, Giacomo Frisoni, Marcos Martínez Galindo, Gianluca Moro, Giuseppe Tagliavini, Francesco Candoli

NAACL 2025findings

Biomedical Named Entity Recognition (BioNER) faces significant challenges in real-world applications due to limited annotated data and the constant emergence of new entity types, making zero-shot learning capabilities crucial. While Large Language Models (LLMs) possess extensive domain knowledge nec…

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2025

ZeroNER: Fueling Zero-Shot Named Entity Recognition via Entity Type Descriptions

Alessio Cocchieri, Marcos Martínez Galindo, Giacomo Frisoni, Gianluca Moro, Claudio Sartori, Giuseppe Tagliavini

ACL 2025finding

What happens when a named entity recognition (NER) system encounters entities it has never seen before? In practical applications, models must generalize to unseen entity types where labeled training data is either unavailable or severely limited—a challenge that demands zero-shot learning capabilit…

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2024

Description Boosting for Zero-Shot Entity and Relation Classification

Gabriele Picco, Leopold Fuchs, Marcos Martínez Galindo, Alberto Purpura, Vanessa López, Hoang Thanh Lam

ACL 2024findings

Zero-shot entity and relation classification models leverage available external information of unseen classes – e.g., textual descriptions – to annotate input text data. Thanks to the minimum data requirement, Zero-Shot Learning (ZSL) methods have high value in practice, especially in applications w…

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