A Comparative Evaluation of Statistical Part-of-Speech Taggers for Russian
Rinat Gareev, Vladimir Vladimirovich Ivanov · Communications in computer and information science · 2015
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.