Application of stacked methods to part-of-speech tagging of polish

Abstract

We compare the accuracy of several single and combination part-of-speech tagging methods applied to Polish and evaluated on the modified corpus of Frequency Dictionary of Contemporary Polish (m-FDCP). Three well known combination methods (weighted voting, distributed voting, and stacked) are analyzed, as well as two new weighted voting methods: MorphCatPrecision and AmbClassPrecision methods are proposed. The MorphCatPrecision method achieves the highest accuracy among all considered weighted voting methods. The best combination method achieves 11.9% error reduction with respect to the best baseline tagger. We report also the statistical significance of the difference in accuracy between various methods measured by means of the McNemar test. Selection of the best algorithms was conducted on a multiprocessor supercomuter due to the high time and memory requirements of most of these algorithms. © 2010 Springer-Verlag Berlin Heidelberg.