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Tagging Icelandic text: an experiment with integrations and combinations of taggers

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Abstract

We use integrations and combinations of taggers to improve the tagging accuracy of Icelandic text. The accuracy of the best performing integrated tagger, which consists of our linguistic rule-based tagger for initial disambiguation and a trigram tagger for full disambiguation, is 91.80%. Combining five different taggers, using simple voting, results in 93.34% accuracy. By adding two linguistically motivated rules to the combined tagger, we obtain an accuracy of 93.48%. This method reduces the error rate by 20.5%, with respect to the best performing tagger in the combination pool.

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Abbreviations

DDT:

data-driven taggers

HMM:

Hidden Markov model

IFD:

Icelandic frequency dictionary

LMR:

linguistically motivated rules

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Acknowledgements

Thanks to the Institute of Lexicography at the University of Iceland, for providing access to the IFD corpus, and Professor Y. Wilks for valuable comments and suggestions in the preparation of this paper.

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Authors and Affiliations

  1. Department of Computer Science, University of Sheffield, 211 Regent Court, Portobello Street, S1 4DP, Sheffield, UK

    Hrafn Loftsson

  2. Department of Computer Science, Reykjavik University, Kringlan 1, 103, Reykjavik, Iceland

    Hrafn Loftsson

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  1. Hrafn Loftsson
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Correspondence to Hrafn Loftsson.

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Loftsson, H. Tagging Icelandic text: an experiment with integrations and combinations of taggers. Lang Resources & Evaluation 40, 175–181 (2006). https://doi.org/10.1007/s10579-006-9013-5

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  • DOI: https://doi.org/10.1007/s10579-006-9013-5

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