Annotating Spelling Errors in German Texts Produced by Primary School Children

Laarmann-Quante, Ronja; Knichel, Lukas; Dipper, Stefanie; Betken, Carina

doi:10.18653/v1/w16-1705

Cited by 5 publications

(11 citation statements)

References 10 publications

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“…The annotations were carried out on 11 texts (866 target tokens) from the corpus described in [25], where children from grades 2-4 were asked to write down the story shown in a sequence of six pictures. The same texts were manually annotated by three humans for comparison (they were a subset of the annotation experiment reported in [1]). Table 1 lists the agreement figures 16 .…”

Section: Discussionmentioning

confidence: 99%

“…It graphically marks a syllable boundary between to vowels as in <Ruhe> [öu:@] ('quietness') which would be pronounced the same way without the <h> but its di-syllabic nature is more easily detected with the <h>. In summary, these principles serve as 1 asterisks mark an orthographically incorrect spelling Language Teaching, Learning and Technology 6-7 Sep 2016, San Francisco, USA reading aids: they allow to derive a word's pronunciation and prosodic structure from its spelling.…”

Section: Background and Motivationmentioning

confidence: 99%

“…We developed a fine-grained and multi-layered annotation scheme for (correctly-spelled and misspelled) German words that closely reflects these regularities and principles and also provides information about a word's general linguistic properties (e.g. phoneme, syllable and morpheme structure) [1]. It is supposed to allow for detailed analyses of the nature of spelling errors.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Each annotation according to our scheme consists of several layers. The annotations are stored in an XML-format called LearnerXML that we describe in [1]. They can be visualized in the Partitur-Editor of the tool EXMARaLDA 4 [12,13].…”

Section: Basic Property Annotations Of Target Wordsmentioning

confidence: 99%

“…In [1], we present a new annotation scheme for the analysis of orthographic properties of words and spelling errors in German primary school children's writings which is closely linked to the principles of the German writing system. It features multiple layers on which different properties of a word are coded which can be relevant for a detailed analysis of a given spelling error.…”

Section: Introductionmentioning

confidence: 99%

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Automating Multi-Level Annotations of Orthographic Properties of German Words and Children’s Spelling Errors

Laarmann-Quante¹

2016

Language Teaching, Learning and Technology (LTLT 2016)

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This paper presents the automatic annotation of orthographic properties of German words and spelling errors in texts of German primary school children according to a new multi-layered annotation scheme [1]. The scheme is closely linked to the principles of the German writing system and is supposed to allow the pursuit of new research questions concerning the relationship between spelling errors of competent and less competent spellers and the regularities of the German graphematic system. A novelty of the automatic annotation is that it takes an intended, correctly spelled word as input and applies a set of rules to generate a list of error candidates containing systematic spelling errors. As a further novelty, the annotation of additional word-and error-related properties is presented such as whether the spelling error changes the word's pronunciation and whether a spelling can be derived from a related word form. This gives rise to more detailed analyses of the errors but also allows us to develop an application for learners that generates automatic advice for the correct spelling. A first evaluation shows that the automatic annotation of the presented categories and features can come close to human annotations.

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Section: Discussionmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Section: Basic Property Annotations Of Target Wordsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automating Multi-Level Annotations of Orthographic Properties of German Words and Children’s Spelling Errors

Laarmann-Quante¹

2016

Language Teaching, Learning and Technology (LTLT 2016)

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Annotating Orthographic Target Hypotheses in a German L1 Learner Corpus

Laarmann-Quante

Ortmann

Ehlert

et al. 2017

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

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NLP applications for learners often rely on annotated learner corpora. Thereby, it is important that the annotations are both meaningful for the task, and consistent and reliable. We present a new longitudinal L1 learner corpus for German (handwritten texts collected in grade 2-4), which is transcribed and annotated with a target hypothesis that strictly only corrects orthographic errors, and is thereby tailored to research and tool development for orthographic issues in primary school. While for most corpora, transcription and target hypothesis are not evaluated, we conducted a detailed inter-annotator agreement study for both tasks. Although we achieved high agreement, our discussion of cases of disagreement shows that even with detailed guidelines, annotators differ here and there for different reasons, which should also be considered when working with transcriptions and target hypotheses of other corpora, especially if no explicit guidelines for their construction are known.

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The making of the Litkey Corpus, a richly annotated longitudinal corpus of German texts written by primary school children

Laarmann-Quante

Dipper

Belke

2019

Proceedings of the 13th Linguistic Annotation Workshop

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To date, corpus and computational linguistic work on written language acquisition has mostly dealt with second language learners who have usually already mastered orthography acquisition in their first language. In this paper, we present the Litkey Corpus, a richly-annotated longitudinal corpus of written texts produced by primary school children in Germany from grades 2 to 4. The paper focuses on the (semi-)automatic annotation procedure at various linguistic levels, which include POS tags, features of the wordinternal structure (phonemes, syllables, morphemes) and key orthographic features of the target words as well as a categorization of spelling errors. Comprehensive evaluations show that high accuracy was achieved on all levels, making the Litkey Corpus a useful resource for corpus-based research on literacy acquisition of German primary school children and for developing NLP tools for educational purposes. The corpus is freely available under https://www.linguistics.rub. de/litkeycorpus/.

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Annotating Spelling Errors in German Texts Produced by Primary School Children

Cited by 5 publications

References 10 publications

Automating Multi-Level Annotations of Orthographic Properties of German Words and Children’s Spelling Errors

Automating Multi-Level Annotations of Orthographic Properties of German Words and Children’s Spelling Errors

Annotating Orthographic Target Hypotheses in a German L1 Learner Corpus

The making of the Litkey Corpus, a richly annotated longitudinal corpus of German texts written by primary school children

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