This paper explores the use of various linguistic features to develop models for automatic classification of language proficiency on the CEFR scale for Icelandic, a low-resourced and morphologically complex language. We train two classifiers to assess skill level of learner texts. One is used as a baseline and takes in the original unaltered text written by a learner and uses predominantly surface features to assess the level. The other uses both surface and other morphological and lexical features, as well as context vectors from transformer (IceBERT). It takes in both the original and corrected versions of the text and takes into account errors/deviation of the original texts compared to the corrected versions. Both classifiers show promising results, with baseline models achieving between 62.2-67.1% accuracy and dual-version between 75-80.3%.

Error corpora are useful for many tasks, in particular for developing spell and grammar checking software and teaching material and tools. We present and compare three specialized Icelandic error corpora; the Icelandic L2 Error Corpus, the Icelandic Dyslexia Error Corpus, and the Icelandic Child Language Error Corpus. Each corpus contains texts written by speakers of a particular group; L2 speakers of Icelandic, people with dyslexia, and children aged 10 to 15. The corpora shed light on errors made by these groups and their frequencies, and all errors are manually labeled according to an annotation scheme. The corpora vary in size, consisting of errors ranging from 7,817 to 24,948, and are published under a CC BY 4.0 license. In this paper, we describe the corpora and their annotation scheme, and draw comparisons between their errors and their frequencies.