Papers by Marcos Martínez Galindo
OpenBioNER: Lightweight Open-Domain Biomedical Named Entity Recognition Through Entity Type Description (2025.findings-naacl)
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Alessio Cocchieri, Giacomo Frisoni, Marcos Martínez Galindo, Gianluca Moro, Giuseppe Tagliavini, Francesco Candoli
| Challenge: | Biomedical Named Entity Recognition (BioNER) is a computationally expensive and limited tool . specialized 7B NER LLMs and GPT-4o can't match textual spans with entity types . |
| Approach: | They propose a lightweight BERT-based cross-encoder architecture that can identify any biomedical entity using only its description. |
| Outcome: | The proposed system outperforms existing models that match textual spans with entity types rather than descriptions on biomedical benchmarks. |
ZeroNER: Fueling Zero-Shot Named Entity Recognition via Entity Type Descriptions (2025.findings-acl)
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Alessio Cocchieri, Marcos Martínez Galindo, Giacomo Frisoni, Gianluca Moro, Claudio Sartori, Giuseppe Tagliavini
| Challenge: | Existing zero-shot learning methods rely on entity type names for generalization . current solutions require large datasets and prioritize a handful of commonly occurring types . |
| Approach: | They propose a description-driven framework that enhances hard zero-shot NER in low-resource settings. |
| Outcome: | The proposed framework outperforms existing models by up to 16% in the F1 score . it also surpasses baseline models that use type names alone . |
Description Boosting for Zero-Shot Entity and Relation Classification (2024.findings-acl)
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Gabriele Picco, Leopold Fuchs, Marcos Martínez Galindo, Alberto Purpura, Vanessa López, Hoang Thanh Lam
| Challenge: | Named Entity Recognition and Relation Extraction (RE) methods are expensive and require domain experts for data acquisition and labeling. |
| Approach: | They propose a strategy for generating variations of an initial description, a heuristic for ranking them and an ensemble method capable of boosting the predictions of zero-shot models. |
| Outcome: | The proposed method outperforms existing approaches and achieves new SOTA results on four different entity and relation classification datasets. |