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dc.contributor.authorHuang, Yanen
dc.contributor.authorMurakami, Akiraen
dc.contributor.authorAlexopoulou, Doraen
dc.contributor.authorKorhonen, Anna-Leenaen
dc.date.accessioned2018-05-15T12:27:59Z
dc.date.available2018-05-15T12:27:59Z
dc.identifier.issn1384-6655
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/275806
dc.description.abstractCurrent syntactic annotation of large-scale learner corpora mainly resorts to “standard parsers” trained on native language data. Understanding how these parsers perform on learner data is important for downstream research and application related to learner language. This study evaluates the performance of multiple standard probabilistic parsers on learner English. Our contributions are three-fold. Firstly, we demonstrate that the common practice of constructing a gold standard – by manually correcting the pre-annotation of a single parser – can introduce bias to parser evaluation. We propose an alternative annotation method which can control for the annotation bias. Secondly, we quantify the influence of learner errors on parsing errors, and identify the learner errors that impact on parsing most. Finally, we compare the performance of the parsers on learner English and native English. Our results have useful implications on how to select a standard parser for learner English.
dc.publisherJohn Benjamins Publishing Company
dc.titleDependency parsing of learner Englishen
dc.typeArticle
prism.endingPage54
prism.issueIdentifier1en
prism.publicationNameInternational Journal of Corpus Linguisticsen
prism.startingPage28
prism.volume23en
dc.identifier.doi10.17863/CAM.23072
dcterms.dateAccepted2018-02-21en
rioxxterms.versionofrecord10.1075/ijcl.16080.huaen
rioxxterms.versionAM*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-02-21en
dc.contributor.orcidHuang, Yan [0000-0002-6879-0446]
dc.identifier.eissn1569-9811
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2018-05-31en


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