A Comparative Evaluation of Statistical Part-of-Speech Taggers for Russian

  • Rinat GareevEmail author
  • Vladimir Ivanov
Part of the Communications in Computer and Information Science book series (CCIS, volume 505)


Part-of-speech (POS) tagging is an essential step in many text processing applications. Quite a few works focus on solving this task for Russian; their results are not directly comparable due to the lack of shared datasets and tools. We propose a POS tagging evaluation framework for Russian that comprises existing third-party resources available for researchers. We applied the framework to compare several implementations of statistical classifiers: HunPos, Stanford POS tagger, OpenNLP implementation of MaxEnt Markov Model, and our own re-implementation of Tiered Conditional Random Fields. The best tagger that was trained on a corpus with less than one million words achieved an accuracy above 93 % .We expect that the evaluation framework will facilitate future studies and improvements on POS tagging for Russian.



This work was financially supported by the Russian Science Foundation (grant 15-11-10019).


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Authors and Affiliations

  1. 1.Kazan Federal UniversityKazanRussia

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