Abstract
The KoKo Corpus is an error-annotated learner corpus of L1 German speakers. It has been created with the aim to investigate and describe the writing skills of German-speaking secondary-school pupils at the end of their school career by analysing authentic texts produced in classrooms. The corpus consists of 1503 argumentative essays which contain manually performed transcription annotations and linguistic error annotations. Transcription annotations reflect surface features of the text, such as the graphical arrangement and self-corrections. All texts are error annotated on the orthographic level (including punctuation errors) and a selection contains error annotations on the grammatical level (i.e. ANNIS sub-corpus KoKo_4_gram, n=597) and on the lexical level (i.e. ANNIS sub-corpus KoKo_4_lex, n=980). The corpus building process was guided by two goals: 1. describe writing skills at the transition from secondary school to university, 2. determine external factors that may influence the distribution of writing skills, such as the region, sociolinguistic (gender, age), socio-economic, and language-related biographical factors (L1, preferred variety of German, reading and writing habits, etc.). The pupils were selected from three different German-speaking areas: - North Tyrol (Austria), South Tyrol (Italy), and Thuringia (Germany). Classes were sampled randomly, using the size of the cities in which the schools were located (small vs. medium vs. big) and the type of school (providing general education vs. education specific to a particular profession) as strata for the sampling. Since data were collected during regular courses, the typical formation of secondary-school classes in the three regions is represented in the whole corpus. Most of the participants are German native speakers (n=1319, 82.7%). Person-related metadata provides information about: - writer’s L1 - writer’s gender - type of school the essay comes from - location of the school the essay comes from - grade attended at data collection In addition, the corpus is automatically annotated, including tokenisation, sentence splitting, POS-tagging and lemmatization.