Inga Lill Sigga Mikkelsen

Also published as: Inga Lill Sigga Mikkelsen


2022

pdf bib
Reusing a Multi-lingual Setup to Bootstrap a Grammar Checker for a Very Low Resource Language without Data
Inga Lill Sigga Mikkelsen | Linda Wiechetek | Flammie A Pirinen
Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

Grammar checkers (GEC) are needed for digital language survival. Very low resource languages like Lule Sámi with less than 3,000 speakers need to hurry to build these tools, but do not have the big corpus data that are required for the construction of machine learning tools. We present a rule-based tool and a workflow where the work done for a related language can speed up the process. We use an existing grammar to infer rules for the new language, and we do not need a large gold corpus of annotated grammar errors, but a smaller corpus of regression tests is built while developing the tool. We present a test case for Lule Sámi reusing resources from North Sámi, show how we achieve a categorisation of the most frequent errors, and present a preliminary evaluation of the system. We hope this serves as an inspiration for small languages that need advanced tools in a limited amount of time, but do not have big data.

pdf bib
Unmasking the Myth of Effortless Big Data - Making an Open Source Multi-lingual Infrastructure and Building Language Resources from Scratch
Linda Wiechetek | Katri Hiovain-Asikainen | Inga Lill Sigga Mikkelsen | Sjur Moshagen | Flammie Pirinen | Trond Trosterud | Børre Gaup
Proceedings of the Thirteenth Language Resources and Evaluation Conference

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.