The quality of the part-of-speech (PoS) annotation in a corpus is crucial for the development of PoS taggers. In this paper, we experiment with three complementary methods for automatically detecting errors in the PoS annotation for the Icelandic Frequency Dictionary corpus. The first two methods are language independent and we argue that the third method can be adapted to other morphologically complex languages. Once possible errors have been detected, we examine each error candidate and hand-correct the corresponding PoS tag if necessary. Overall, based on the three methods, we handcorrect the PoS tagging of 1,334 tokens (0.23% of the tokens) in the corpus. Furthermore, we re-evaluate existing state-ofthe-art PoS taggers on Icelandic text using the corrected corpus.

The Icelandic language is a morphologically complex language, for which a large tagset has been created. This paper describes the design of a linguistic rule-based system for part-of-speech tagging Icelandic text. The system contains two main components: a disambiguator, IceTagger, and an unknown word guesser, IceMorphy. IceTagger uses a small number of local elimination rules along with a global heuristics component. The heuristics guess the functional roles of the words in a sentence, mark prepositional phrases, and use the acquired knowledge to force feature agreement where appropriate. IceMorphy is used for guessing the tag profile for unknown words and for automatically filling tag profile gaps in the lexicon. Evaluation shows that IceTagger achieves 91.54% accuracy, a substantial improvement on the highest accuracy, 90.44%, obtained using three state-of-the-art data-driven taggers. Furthermore, the accuracy increases to 92.95% by using IceTagger along with two data-driven taggers in a simple voting scheme. The development time of the tagging system was only seven man-months, which can be considered a short development period for a linguistic rule-based system.

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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.