Few-shot domain adaptation for named-entity recognition via joint constrained k-means and subspace selection
Ayoub Hammal, Benno Uthayasooriyar, Caio Corro
Abstract
Named-entity recognition (NER) is a task that typically requires large annotated datasets, which limits its applicability across domains with varying entity definitions. This paper addresses few-shot NER, aiming to transfer knowledge to new domains with minimal supervision. Unlike previous approaches that rely solely on limited annotated data, we propose a weakly-supervised algorithm that combines small labeled datasets with large amounts of unlabeled data. Our method extends the k-means algorithm with label supervision, cluster size constraints, and domain-specific discriminative subspace selection. This unified framework achieves state-of-the-art results in few-shot NER, demonstrating its effectiveness in leveraging unlabeled data and adapting to domain-specific challenges.
BibTeX
@inproceedings{hammal-etal-2025-shot,
title = "Few-shot domain adaptation for named-entity recognition via joint constrained k-means and subspace selection",
author = "Hammal, Ayoub and
Uthayasooriyar, Benno and
Corro, Caio",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.662/",
pages = "9902--9916"
}