Abstract
In this article we report the results for five POS taggers, i.e., the Mate tagger, the Hunpos tagger, RFTagger, theOpenNLP tagger, andNLTKUnigramtagger, tested on the data of the Ancient Greek Dependency Treebank. This is done in order to find the most efficient POS tagger to use for pre-annotation of new treebank data. A corrected 1-run 10-fold cross validation t test shows that the Mate tagger outperforms all the other taggers, with an accuracy score of 88%.
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© 2016 G. G. A. Celano et al.
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