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Error Coding of Second-Language Learner Texts Based on Mostly Automatic Alignment of Parallel Corpora
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.ORCID iD: 0000-0003-4040-3544
2018 (English)In: CLARIN Annual Conference 2018: Proceedings / [ed] Inguna Skadina, Maria Eskevich, 2018, p. 181-184Conference paper, Published paper (Refereed)
Abstract [en]

Error coding of second-language learner text, that is, detecting, correcting and annotating errors, is a cumbersome task which in turn requires interpretation of the text to decide what the errors are. This paper describes a system with which the annotator corrects the learner text by editing it prior to the actual error annotation. During the editing, the system automatically generates a parallel corpus of the learner and corrected texts. Based on this, the work of the annotator consists of three independent tasks that are otherwise often conflated in error coding: correcting the learner text, repairing inconsistent alignments, and performing the actual error annotation.

Place, publisher, year, edition, pages
2018. p. 181-184
Keywords [en]
Second language learning, error coding, annotation, parallel corpora
National Category
General Language Studies and Linguistics Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:su:diva-162705OAI: oai:DiVA.org:su-162705DiVA, id: diva2:1268990
Conference
CLARIN Annual Conference 2018, Pisa, Italy, 8–10 October, 2018
Projects
SweLL project
Funder
Riksbankens Jubileumsfond, N16-0464:1Available from: 2018-12-07 Created: 2018-12-07 Last updated: 2022-02-26Bibliographically approved

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Authority records

Wirén, MatsVolodina, Elena

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf