Long term language technology infrastructures are critical for continued maintenance of language technology based software that is used to support the use of languages in digital world. In Nordic area we have languages ranging from well-resourced national majority languages like Norwegian, Swedish and Finnish as well as minoritised, unresourced and indigenous languages like Sámi languages. We present an infrastructure that has been build in over 20 years time that supports building language technology and tools for most of the Nordic languages as well as many of the languages all over the world, with focus on Sámi and other indigenous, minoritised and unresourced languages. We show that one common infrastructure can be used to build tools from keyboards and spell-checkers to machine translators, grammar checkers and text-to-speech as well as automatic speech recognition.

Machine learning (ML) approaches have dominated NLP during the last two decades. From machine translation and speech technology, ML tools are now also in use for spellchecking and grammar checking, with a blurry distinction between the two. We unmask the myth of effortless big data by illuminating the efforts and time that lay behind building a multi-purpose corpus with regard to collecting, mark-up and building from scratch. We also discuss what kind of language technology minority languages actually need, and to what extent the dominating paradigm has been able to deliver these tools. In this context we present our alternative to corpus-based language technology, which is knowledge-based language technology, and we show how this approach can provide language technology solutions for languages being outside the reach of machine learning procedures. We present a stable and mature infrastructure (GiellaLT) containing more than hundred languages and building a number of language technology tools that are useful for language communities.

We investigate both rule-based and machine learning methods for the task of compound error correction and evaluate their efficiency for North Sámi, a low resource language. The lack of error-free data needed for a neural approach is a challenge to the development of these tools, which is not shared by bigger languages. In order to compensate for that, we used a rule-based grammar checker to remove erroneous sentences and insert compound errors by splitting correct compounds. We describe how we set up the error detection rules, and how we train a bi-RNN based neural network. The precision of the rule-based model tested on a corpus with real errors (81.0%) is slightly better than the neural model (79.4%). The rule-based model is also more flexible with regard to fixing specific errors requested by the user community. However, the neural model has a better recall (98%). The results suggest that an approach that combines the advantages of both models would be desirable in the future. Our tools and data sets are open-source and freely available on GitHub and Zenodo.