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Learning with learner corpora: Using the TLE for native language identification
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
2017 (English)In: Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition, 2017, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

This study investigates the usefulness of the Treebank of Learner English (TLE) when applied to the task of Native Language Identification (NLI). The TLE is effectively a parallel corpus of Standard/Learner English, as there are two versions; one based on original learner essays, and the other an error-corrected version. We use the corpus to explore how useful a parser trained on ungrammatical relations is compared to a parser trained on grammatical relations, when used as features for a native language classification task. While parsing results are much better when trained on grammatical relations, native language classification is slightly better using a parser trained on the original treebank containing ungrammatical relations.

Place, publisher, year, edition, pages
2017. p. 1-7
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:uu:diva-338549OAI: oai:DiVA.org:uu-338549DiVA, id: diva2:1172493
Conference
The joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition
Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2018-01-13Bibliographically approved

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fulltext(76 kB)17 downloads
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http://www.ep.liu.se/ecp/134/001/ecp17134001.pdf

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Stymne, Sara
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CiteExportLink to record
Permanent link

Direct link
Cite
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