A Modular Part-of-Speech Tagger for Scottish Gaelic using spaCy

arXiv:2608.04808 · cs.CL · Submitted 2026-08-05 · Read on arXiv

Peter Stefan, Peter J Barclay, Alistair Lawson

cs.CL

Submitted: 2026-08-05

Comments: A revised version of this paper has been accepted for presentation at UKCI 2026 (https://ukci2026.coventry.ac.uk/home/) and will be published by Springer

Code: https://github.com/explosion/spaCy

Project page: https://spacy.io/api/tok2vec

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: Part-of-speech tagging for low-resource languages remains challenging due to limited annotated data, especially for linguistically complex languages.

Terminology

Abstract

Part-of-speech tagging for low-resource languages remains challenging due to limited annotated data, especially for linguistically complex languages. Gaidhlig (Scottish Gaelic) is a morphologically rich and endangered language with limited digital resources, making it suitable for examining a lightweight language processing approach. This paper describes using the modular spaCy Natural Language Processing framework to build part-of-speech taggers for Gaidhlig using the Annotated Reference Corpus of Scottish Gaelic. We train two models with minimal pre-processing and configuration: one using a fine-grained tagset and another using a reduced coarse-grained tagset. Both models are trained without external embeddings or pre-trained language models, using only supervised learning from the available corpus. The fine-grained model achieves 88.6% tagging accuracy, while the coarse-grained model achieves 93.7%. The results are comparable to those of the two previously published Gaidhlig taggers, indicating that simple, off-the-shelf language processing pipelines can demonstrate good performance in low-resource and morphologically complex linguistic settings.

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