Mii *eai leat gal vuollánan – Vi *ha neimen ikke gitt opp

• Published in: Nordlyd
• Main Authors: Linda Wiechetek, Flammie Pirinen, Børre Gaup, Chiara Argese, Thomas Omma
• Format: Article in Journal/Newspaper
• Language: English; Norwegian
• Published: Septentrio Academic Publishing 2022
• Subjects: Sámi language; grammar checking; neural networks; nlp; rule-based; agreement; Language. Linguistic theory. Comparative grammar; P101-410; Sámi
• Online Access: https://doi.org/10.7557/12.6346; https://doaj.org/article/1063afdd01b14ca0b9106b2ea873221b

Machine learning is the dominating paradigm in natural language processing nowadays. It requires vast amounts of manually annotated or synthetically generated text data. In the GiellaLT infrastructure, on the other hand, we have worked with rule-based methods, where the linguistis have full control over the development the tools. In this article we uncover the myth of machine learning being cheaper than a rule- based approach by showing how much work there is behind data generation, either via corpus annotation or creating tools that automatically mark-up the corpus. Earlier we have shown that the correction of grammatical errors, in particular compound errors, benefit from hybrid methods. Agreement errors, on the other other hand, are to a higher degree dependent on the larger grammatical context. Our experiments show that machine learning methods for this error type, even when supplemented by rule-based methods generating massive data, can not compete with the state-of-the-art rule-based approach.