The Litkey Corpus: A richly annotated longitudinal corpus of German texts written by primary school children

Abstract

Compared to early language development, later changes to the language system during orthography and literacy acquisition have not yet been researched in detail. We present a longitudinal corpus of texts on short picture stories written by German primary school children between grades 2 and 4 and grades 3 and 4. It includes 1,922 texts with 212,505 tokens (6,364 types) from 251 children. For each text, rich metadata is available, including age, grade and linguistic background (at least 60% of the children were multilingual). To our knowledge, our corpus is the largest longitudinal corpus of written texts by children at primary school age. Each word is included in its original spelling as well as in a normalized form (target hypothesis), specifying the intended word form, which we corrected for orthographic but not grammatical errors. Original and target word forms are aligned characterwise and the target word forms are enriched with phonological, syllabic, and morphological information. Additionally, for each target word form, we established key lexical variables, e.g., word frequency or summed bigram frequency, as specified in childLex. Where applicable, we also specify key features of German orthography (e.g., consonant doubling, vowellengthening <h>). Taken together, this information allows for a detailed assessment of the properties of words that tend to increase the likelihood of spelling errors. The corpus is available in different formats-as tab-delimited annotated token and type based lists, in an XML format, and via the corpus search tool ANNIS.

Can we utilize Large Language Models (LLMs) to generate useful linguistic corpora? A case study of the word frequency effect in young German readers

“…We compared LLM-based word frequencies to word frequencies from ChildLex (Schroeder et al, 2015), Litkey (Laarmann-Quante et al, 2019), DWDS (Heister et al, 2011), SubtLEX, and Google Books (Brysbaert et al, 2016). We used reading performance measures from DeveL (Schröter & Schroeder, 2017).…”

Evaluating Automatic Spelling Correction Tools on German Primary School Children’s Misspellings

“…We base our study on the Litkey Corpus (Laarmann-Quante et al, 2019), which is a freely-available longitudinal corpus consisting of 1,922 German texts written by 251 primary school children between the second half of grade 2 and the end of grade 4 (= end of primary school). Every few months, at ten testing points in total, the same children were asked to write down a story that was shown in a sequence of six pictures.…”

“…The aim and contribution of this paper is an evaluation of six existing spelling correction tools on misspellings of German primary school children taken from the Litkey Corpus (Laarmann-Quante et al, 2019). We examine how well the existing approaches perform in order to be used e.g.…”