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RDRPOSTagger is a robust and easy-to-use toolkit for POS and morphological tagging. It employs an error-driven approach to automatically construct tagging rules in the form of a binary tree.

  • RDRPOSTagger obtains very fast tagging speed and achieves a competitive accuracy in comparison to the state-of-the-art results. See experimental results including performance speed and tagging accuracy for 13 languages in our AI Communications article.

  • RDRPOSTagger now supports pre-trained UPOS, XPOS and morphological tagging models for about 80 languages. See folder Models for more details.

The general architecture and experimental results of RDRPOSTagger can be found in our following papers:

Please CITE either the EACL or the AICom paper whenever RDRPOSTagger is used to produce published results or incorporated into other software.

Current release (41MB .zip file containing about 330 pre-trained tagging models) is available to download at:

Find more information about RDRPOSTagger at:

In addition, you might want to try my neural network-based toolkit jPTDP for joint POS tagging and dependency parsing.

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