New spaCy-based POS tagger achieves high accuracy for Scottish Gaelic

Researchers have developed a part-of-speech tagger for Scottish Gaelic, an endangered and morphologically complex language, using the spaCy Natural Language Processing framework. Two models were trained with minimal pre-processing and configuration: one with a fine-grained tagset achieving 88.6% accuracy and another with a coarse-grained tagset reaching 93.7% accuracy. These results demonstrate that straightforward, off-the-shelf NLP pipelines can achieve strong performance even with limited annotated data and complex linguistic structures. AI

IMPACT Demonstrates effective NLP techniques for low-resource languages, potentially aiding in the preservation and digital accessibility of endangered languages.

RANK_REASON Academic paper detailing a new NLP model for a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]

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• Annotated Reference Corpus of Scottish Gaelic
• arXiv
• Scottish Gaelic
• spaCy

• paper
• other

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