Error annotation systems

Lüdeling, Anke; Hirschmann, Hagen

doi:10.1017/cbo9781139649414.007

Cited by 26 publications

(16 citation statements)

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“…Thus corpus hits chart early and subsequent success much better than they do errors on the road to acquisition. Research in error tagging and annotation is clearly a priority (Lüdeling & Hirschmann, ).…”

Section: L1 Acquisitionmentioning

confidence: 99%

“…A significant issue with searching learner language is that one can find instances where the learner has produced the target form, but it is much harder to find instances where they have produced some nontargetlike variant. Dealing with learner language with NLP techniques poses considerable challenges, although significant developments are being made (Meurers, ), likewise there are important developments in human annotation of error (Dickinson & Lee ; Lüdeling & Hirschmann, ).…”

Section: L2 Acquisitionmentioning

confidence: 99%

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Cognition, Corpora, and Computing: Triangulating Research in Usage‐Based Language Learning

Ellis

2016

Language Learning

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Usage‐based approaches explore how we learn language from our experience of language. Related research thus involves the analysis of the usage from which learners learn and of learner usage as it develops. This program involves considerable data recording, transcription, and analysis, using a variety of corpus and computational techniques, many of them specially devised for learner language. This article surveys relevant developments across the psychology of learning, first and second language acquisition, psycholinguistics, corpus linguistics, and computational linguistics and identifies challenges and future priorities relating to the following issues: (1) analyzing the distributional characteristics of linguistic constructions and their meanings in large collections of language that are representative of the language that learners experience, (2) the longitudinal analysis of learner language, and (3) Natural Language Processing analyses of the dimensions of language complexity.

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Section: L1 Acquisitionmentioning

confidence: 99%

Section: L2 Acquisitionmentioning

confidence: 99%

Cognition, Corpora, and Computing: Triangulating Research in Usage‐Based Language Learning

Ellis

2016

Language Learning

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“…In many cases, learner errors are also marked, using different guidelines for the identification and interpretation of errors (cf. Lüdeling & Hirschmann, ; van Rooij, ; among many others).…”

Section: Usage Data: Learner Corporamentioning

confidence: 98%

“…Learner language does not consist only of grammatical utterances, which poses conceptual and methodological challenges that range from defining an error to the assignment of categories or structures. This has been discussed extensively (see, e.g., Díaz‐Negrillo, Meurers, Valera, & Wunsch, ; Lüdeling & Hirschmann, ; Reznicek et al., ; van Rooij, ). We base our analysis on the explicit formulation of a TH.…”

Section: Usage Data: Learner Corporamentioning

confidence: 99%

Linguistic Models, Acquisition Theories, and Learner Corpora: Morphological Productivity in SLA Research Exemplified by Complex Verbs in German

2017

Self Cite

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The present study analyzes morphological productivity for complex verbs in second language acquisition by analyzing a corpus of German as a Foreign Language (GFL). It shows that advanced learners of GFL use prefix and particle verbs relatively frequently and productively but less so than native speakers do and discusses these findings in the light of different linguistic models and acquisition theories. It argues that corpus data must be evaluated against good models and that it is necessary to make the categorization decisions available as annotations.

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“…Especially corpora annotated with linguistic information provide a rich source for research on language acquisition and allow users to identify and quantify properties of learner language. As argued in Lüdeling and Hirschmann (2015), it is especially important to specify a target hypothesis for each error, which makes explicit the annotator's assumption or interpretation of what the learner intended to write. The number of corpora of written texts by young L1 speakers is rather limited.…”

Section: Learner Corporamentioning

confidence: 99%

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

et al. 2019

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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 ). 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.

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Error annotation systems

Cited by 26 publications

References 0 publications

Cognition, Corpora, and Computing: Triangulating Research in Usage‐Based Language Learning

Cognition, Corpora, and Computing: Triangulating Research in Usage‐Based Language Learning

Linguistic Models, Acquisition Theories, and Learner Corpora: Morphological Productivity in SLA Research Exemplified by Complex Verbs in German

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

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