We describe the process of conversion between the PoS tagging schemes of two languages, the Icelandic MIM-GOLD tagging scheme and the Faroese Sosialurin tagging scheme. These tagging schemes are functionally similar but use separate ways to encode fine-grained morphological information on tokenised text. As Faroese and Icelandic are lexically and grammatically similar, having a systematic method to convert between these two tagging schemes would be beneficial in the field of language technology, specifically in research on transfer learning between the two languages. As a product of our work, we present a provisional version of Icelandic corpora, prepared in the Faroese PoS tagging scheme, ready for use in cross-lingual NLP applications.

We describe the development of a dedicated, high-accuracy part-of-speech (PoS) tagging solution for Faroese, a North Germanic language with about 50,000 speakers. To achieve this, a state-of-the-art neural PoS tagger for Icelandic, ABLTagger, was trained on a 100,000 word PoS-tagged corpus for Faroese, standardised with methods previously applied to Icelandic corpora. This tagger was supplemented with a novel Experimental Database of Faroese Inflection (EDFM), which contains morphological information on 67,488 Faroese words with about one million inflectional forms. This approach produced a PoS-tagging model for Faroese which achieves a 91.40% overall accuracy when evaluated with 10-fold cross validation, which is currently the highest reported accuracy for a dedicated Faroese PoS-tagger. The tagging model, morphological database, proposed revised PoS tagset for Faroese as well as a revised and standardised PoS tagged corpus are all presented as products of this project and are made available for use in further research in Faroese language technology

The topic of this paper is a rule-based pipeline for converting constituency treebanks based on the Penn Treebank format to Universal Dependencies (UD). We describe an Icelandic constituency treebank, its annotation scheme and the UD scheme. The conversion is discussed, the methods used to deliver a fully automated UD corpus and complications involved. To show its applicability to corpora in different languages, we extend the pipeline and convert a Faroese constituency treebank to a UD corpus. The result is an open-source conversion tool, published under an Apache 2.0 license, applicable to a Penn-style treebank for conversion to a UD corpus, along with the two new UD corpora.

Spell checkers and other proofreading software are crucial tools for people with dyslexia and other reading disabilities. Most spell checkers automatically detect spelling mistakes by looking up individual words and seeing if they exist in the vocabulary. However, one of the biggest challenges of automatic spelling correction is how to deal with real-word errors, i.e. spelling mistakes which lead to a real but unintended word, such as when then is written in place of than. These errors account for 20% of all spelling mistakes made by people with dyslexia. As both words exist in the vocabulary, a simple dictionary lookup will not detect the mistake. The only way to disambiguate which word was actually intended is to look at the context in which the word appears. This problem is particularly apparent in languages with rich morphology where there is often minimal orthographic difference between grammatical items. In this paper, we present our novel confusion set corpus for Icelandic and discuss how it could be used for context-sensitive spelling correction. We have collected word pairs from seven different categories, chosen for their homophonous properties, along with sentence examples and frequency information from said pairs. We present a small-scale machine learning experiment using a decision tree binary classification which results range from 73% to 86% average accuracy with 10-fold cross validation. While not intended as a finalized result, the method shows potential and will be improved in future research.

Making the low-resource language, Icelandic, accessible and usable in Language Technology is a work in progress and is supported by the Icelandic government. Creating resources and suitable training data (e.g., a dependency treebank) is a fundamental part of that work. We describe work on a parallel Icelandic dependency treebank based on Universal Dependencies (UD). This is important because it is the first parallel treebank resource for the language and since several other languages already have a resource based on the same text. Two Icelandic treebanks based on phrase-structure grammar have been built and ongoing work aims to convert them to UD. Previously, limited work has been done on dependency grammar for Icelandic. The current project aims to ameliorate this situation by creating a small dependency treebank from scratch. Creating a treebank is a laborious task so the process was implemented in an accessible manner using freely available tools and resources. The parallel data in the UD project was chosen as a source because this would furthermore give us the first parallel treebank for Icelandic. The Icelandic parallel UD corpus will be published as part of UD version 2.6.

In this paper, we describe a new national language technology programme for Icelandic. The programme, which spans a period of five years, aims at making Icelandic usable in communication and interactions in the digital world, by developing accessible, open-source language resources and software. The research and development work within the programme is carried out by a consortium of universities, institutions, and private companies, with a strong emphasis on cooperation between academia and industries. Five core projects will be the main content of the programme: language resources, speech recognition, speech synthesis, machine translation, and spell and grammar checking. We also describe other national language technology programmes and give an overview over the history of language technology in Iceland.

This paper presents ongoing work that aims to improve machine parsing of Faroese using a combination of Faroese and Icelandic training data. We show that even if we only have a relatively small parsed corpus of one language, namely 53,000 words of Faroese, we can obtain better results by adding information about phrase structure from a closely related language which has a similar syntax. Our experiment uses the Berkeley parser. We demonstrate that the addition of Icelandic data without any other modification to the experimental setup results in an f-measure improvement from 75.44% to 78.05% in Faroese and an improvement in part-of-speech tagging accuracy from 88.86% to 90.40%.

We describe the background for and building of IcePaHC, a one million word parsed historical corpus of Icelandic which has just been finished. This corpus which is completely free and open contains fragments of 60 texts ranging from the late 12th century to the present. We describe the text selection and text collecting process and discuss the quality of the texts and their conversion to modern Icelandic spelling. We explain why we choose to use a phrase structure Penn style annotation scheme and briefly describe the syntactic anno-tation process. We also describe a spin-off project which is only in its beginning stages: a parsed historical corpus of Faroese. Finally, we advocate the importance of an open source policy as regards language resources.