Detailed Error Annotation for Morphologically Rich Languages: Latvian Use Case
Roberts Darģis, Ilze Auziņa, Kristīne Levāne-Petrova, Inga Kaija · Frontiers in artificial intelligence and applications · 2020
This paper presents a detailed error annotation for morphologically rich languages. The described approach is used to create Latvian Language Learner corpus (LaVA) which is part of a currently ongoing project Development of Learner corpus of Latvian: methods, tools and applications. There is no need for an advanced multi-token error annotation schema, because error annotated texts are written by beginner level (A1 and A2) who use simple syntactic structures. This schema focuses on in-depth categorization of spelling and word formation errors. The annotation schema will work best for languages with relatively free word order and rich morphology.