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Task Effects on Linguistic Complexity and Accuracy: A Large-Scale Learner Corpus Analysis Employing Natural Language Processing Techniques

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cam.issuedOnline2017-06-16
dc.contributor.authorAlexopoulou, T
dc.contributor.authorMichel, M
dc.contributor.authorMurakami, A
dc.contributor.authorMeurers, D
dc.date.accessioned2017-02-03T09:12:28Z
dc.date.available2017-02-03T09:12:28Z
dc.date.issued2017-03-20
dc.description.abstractLarge-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: How does the prompt and input of a task and its functional requirements influence task-based linguistic performance? This question is vital for making large-scale task-based corpora fruitful for second language acquisition research. We explore the issue through an analysis of selected tasks in EFCAMDAT and the complexity and accuracy of the language they elicit.
dc.description.sponsorshipOur research was supported as part of the LEAD Graduate School & Research Network [GSC1028], a project of the Excellence Initiative of the German federal and state governments, and by grants ANR-11-LABX-0036 (BLRI) and ANR-11-IDEX-0001-02 (A*MIDEX). We also gratefully acknowledge the support of EF Education First through the sponsorship of the EF Research Lab for Applied Language Learning at the University of Cambridge.
dc.identifier.doi10.17863/CAM.7512
dc.identifier.eissn1467-9922
dc.identifier.issn0023-8333
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/262262
dc.language.isoeng
dc.publisherWiley
dc.publisher.urlhttps://onlinelibrary.wiley.com/doi/full/10.1111/lang.12232
dc.subjectlearner corpus
dc.subjecttask complexity
dc.subjectcomplexity
dc.subjectaccuracy
dc.subjectfluency (CAF)
dc.subjectNLP
dc.subjectTBLT
dc.titleTask Effects on Linguistic Complexity and Accuracy: A Large-Scale Learner Corpus Analysis Employing Natural Language Processing Techniques
dc.typeArticle
dcterms.dateAccepted2016-12-17
prism.endingPage208
prism.issueIdentifierS1
prism.publicationDate2017
prism.publicationNameLanguage Learning
prism.startingPage180
prism.volume67
rioxxterms.licenseref.startdate2017-03-20
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionAM
rioxxterms.versionofrecord10.1111/lang.12232

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