🤖 AI Summary
This study addresses part-of-speech tagging for Scottish Gaelic—a low-resource, morphologically complex language—under conditions of extreme data scarcity and without access to external linguistic resources. Building upon the spaCy framework, the authors develop a lightweight, modular supervised tagger that relies solely on a reference corpus of Scottish Gaelic, training separate models for fine-grained and coarse-grained tagsets without leveraging pretrained language models or external embeddings. Experimental results demonstrate that the fine-grained and coarse-grained models achieve accuracies of 88.6% and 93.7%, respectively, matching the performance of current state-of-the-art systems. These findings underscore the effectiveness and practicality of off-the-shelf NLP pipelines for tackling morphologically rich, low-resource languages when appropriately adapted within a supervised learning paradigm.
📝 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.