RDRPOSTagger is a robust, easy-to-use and language-independent toolkit for POS and morphological tagging. It employs an error-driven approach to automatically construct tagging rules in the form of a binary tree. The main properties of RDRPOSTagger are as follows:
- RDRPOSTagger obtains fast performance in both learning and tagging
process. For example, RDRPOSTagger achieved tagging speeds of 5K and 90K
English word tokens/second computed for single threaded implementations in
Python and Java respectively, using a computer with Core2Duo 2.4GHz.
- RDRPOSTagger achieves a very competitive accuracy in comparison to the
state-of-the-art results. Please see experimental results including
training time, tagging speed and tagging accuracy for 13 languages in the
following paper:
A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-Of-Speech Tagging <http://content.iospress.com/articles/ai-communications/aic698>. *AI Communications*, to appear. [CameraReadyVersion.pdf] <http://arxiv.org/abs/1412.4021>
- RDRPOSTagger supports pre-trained POS and morphological tagging models
for 13 languages including Bulgarian, Czech, Dutch, English, French,
German, Hindi, Italian, Portuguese, Spanish, Swedish, Thai and Vietnamese.
Please find more information about RDRPOSTagger at its website: http://rdrpostagger.sourceforge.net
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