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mbt Documentation


memory-based tagger-generator and tagger for natural language processing
MBT is a memory-based tagger-generator and tagger in one. The tagger-generator
part can generate a sequence tagger on the basis of a training set of tagged
sequences; the tagger part can tag new sequences. MBT can, for instance, be
used to generate part-of-speech taggers or chunkers for natural language
processing. Features:
* Tagger generation: tagged text in, tagger out,
* Optional feedback loop: feed previous tag decision back to input of next
* Easily customizable feature representation; can incorporate user-provided
* Automatic generation of separate sub-taggers for known words and unknown
* Can make use of full algorithmic parameters of TiMBL.
MBT is a product of the ILK Research Group (Tilburg University, The
Netherlands) and the CLiPS Research Centre (University of Antwerp, Belgium).
If you do scientific research in natural language processing, MBT will
likely be of use to you.
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