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Neural Networks and Spelling Features for Native Language Identification
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.ORCID iD: 0000-0003-2815-395X
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.ORCID iD: 0000-0002-6027-4156
2017 (English)In: The Twelfth Workshop on Innovative Use of NLP for Building Educational Applications: Proceedings of the Workshop, Association for Computational Linguistics, 2017, p. 235-239Conference paper, Published paper (Refereed)
Abstract [en]

We present the RUG-SU team's submission at the Native Language Identification Shared Task 2017. We combine several approaches into an ensemble, based on spelling error features, a simple neural network using word representations, a deep residual network using word and character features, and a system based on a recurrent neural network. Our best system is an ensemble of neural networks, reaching an F1 score of 0.8323. Although our system is not the highest ranking one, we do outperform the baseline by far.

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2017. p. 235-239
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:su:diva-146825ISBN: 978-1-945626-85-2 (print)OAI: oai:DiVA.org:su-146825DiVA, id: diva2:1140589
Conference
EMNLP 2017: Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 7-11 September, 2017
Available from: 2017-09-12 Created: 2017-09-12 Last updated: 2019-03-26Bibliographically approved

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fulltext(210 kB)125 downloads
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Bjerva, JohannesGrigonyte, GintareÖstling, RobertPlank, Barbara
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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