Error annotation: a review and faceted taxonomy.
Classification of errors in language use plays a crucial role in language learning & teaching, error analysis studies, and language technology development. However, there is no standard and inclusive error classification method agreed upon among different disciplines, which causes repetition of simi...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 3385 - 3410 |
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| Autores principales: | , , , |
| Formato: | Literature Review |
| Publicado: |
Springer Nature
Sep2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909054&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909054 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909054 10.1007/s10579-024-09794-0 ppf: 3385 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.4MB tig: atl: Error annotation: a review and faceted taxonomy. aug: au: Eryiğit, Gülşen Golynskaia, Anna Sayar, Elif Türker, Tolgahan affil: https://ror.org/059636586 Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey https://ror.org/059636586 Turkish Teaching Application and Research Center, Istanbul Technical University, Istanbul, Turkey Department of Education, Yunus Emre Institute, Ankara, Turkey su: Taxonomy Machine translating Natural language processing Corpora Foreign language education sug: subj: Taxonomy Machine translating Natural language processing Corpora Foreign language education keyword: Communication and Culture Linguistics Error classification Language Learner corpus ab: Classification of errors in language use plays a crucial role in language learning & teaching, error analysis studies, and language technology development. However, there is no standard and inclusive error classification method agreed upon among different disciplines, which causes repetition of similar efforts and a barrier in front of a common understanding in the field. This article brings a new and holistic perspective to error classifications and annotation schemes across different fields (i.e., learner corpora research, error analysis, grammar error correction, and machine translation), all serving the same purpose but employing different methods and approaches. The article first reviews previous error annotation efforts from different fields for nineteen languages with different characteristics, including the morphologically rich ones that pose diverse challenges for language technologies. It then introduces a faceted taxonomy for errors in language use, comprising multidimensional and hierarchical facets that can be utilized to create both fine- and coarse-grained error annotation schemes depending on specific requirements. We believe that the proposed taxonomy based on the principles of universality and diversity will address the emerging need for a common framework in error annotation. pubtype: Academic Journal doctype: Literature Review src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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