Tagging a Morphologically Complex Language Using an Averaged Perceptron Tagger: The Case of Icelandic

Hrafn Lofsson
School of Computer Science, Reykjavik University, Iceland

Robert Östling
Department of Linguistics, Stockholm University, Sweden

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Ingår i: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013); May 22-24; 2013; Oslo University; Norway. NEALT Proceedings Series 16

Linköping Electronic Conference Proceedings 85:13, s. 105-119

NEALT Proceedings Series 16:13, s. 105-119

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Publicerad: 2013-05-17

ISBN: 978-91-7519-589-6

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


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.


Averaged Perceptron; Part-of-Speech Tagging; Morphological Database; Linguistic Features; Word Embeddings


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