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Tagging a Morphologically Complex Language Using an Averaged Perceptron Tagger: The Case of Icelandic
Reykjaviks universitet, Island.
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.ORCID iD: 0000-0002-6027-4156
2013 (English)In: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013), Linköping University Electronic Press, Linköpings universitet, 2013, 105-119 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we experiment with using Stagger, an open-source implementation of an Averaged Perceptron tagger, to tag Icelandic, a morphologically complex language. By adding languagespecific linguistic features and using IceMorphy, an unknown word guesser, we obtain state-of- the-art tagging accuracy of 92.82%. Furthermore, by adding data from a morphological database, and word embeddings induced from an unannotated corpus, the accuracy increases to 93.84%. This is equivalent to an error reduction of 5.5%, compared to the previously best tagger for Icelandic, consisting of linguistic rules and a Hidden Markov Model.

Place, publisher, year, edition, pages
Linköping University Electronic Press, Linköpings universitet, 2013. 105-119 p.
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3740
Keyword [en]
part of speech tagging, pos tagging, icelandic
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:su:diva-90304OAI: oai:DiVA.org:su-90304DiVA: diva2:624559
Conference
19th Nordic Conference of Computational Linguistics (NODALIDA 2013)
Available from: 2013-06-01 Created: 2013-06-01 Last updated: 2014-04-28Bibliographically approved

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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