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:
Dat Quoc Nguyen, Dai Quoc Nguyen, Dang Duc Pham and Son Bao Pham. RDRPOSTagger: A Ripple Down Rules-based Part-Of-Speech Tagger. In Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, pp. 17-20, 2014. [.PDF] [.bib]
Dat Quoc Nguyen, Dai Quoc Nguyen, Dang Duc Pham and Son Bao Pham. A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-Of-Speech Tagging. AI Communications (AICom), vol. 29, no. 3, pp. 409-422, 2016. [.PDF] [.bib]
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: https://github.com/datquocnguyen/RDRPOSTagger/archive/master.zip
Find more information about RDRPOSTagger at: http://rdrpostagger.sourceforge.net/
In addition, you might want to try my neural network-based toolkit jPTDP for joint POS tagging and dependency parsing.