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Add python-zpar to Python NLP section.

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Nitin Madnani 2015-07-27 16:11:18 -04:00
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@ -603,6 +603,7 @@ on MNIST digits[DEEP LEARNING]
* [PyNLPl](https://github.com/proycon/pynlpl) - Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for [FoLiA](https://proycon.github.io/folia), but also ARPA language models, Moses phrasetables, GIZA++ alignments. * [PyNLPl](https://github.com/proycon/pynlpl) - Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for [FoLiA](https://proycon.github.io/folia), but also ARPA language models, Moses phrasetables, GIZA++ alignments.
* [python-ucto](https://github.com/proycon/python-ucto) - Python binding to ucto (a unicode-aware rule-based tokenizer for various languages) * [python-ucto](https://github.com/proycon/python-ucto) - Python binding to ucto (a unicode-aware rule-based tokenizer for various languages)
* [python-frog](https://github.com/proycon/python-frog) - Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER) * [python-frog](https://github.com/proycon/python-frog) - Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER)
* [python-zpar](https://github.com/EducationalTestingService/python-zpar) - Python bindings for [ZPar](https://github.com/frcchang/zpar), a statistical part-of-speech-tagger, constiuency parser, and dependency parser for English.
* [colibri-core](https://github.com/proycon/colibri-core) - Python binding to C++ library for extracting and working with with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way. * [colibri-core](https://github.com/proycon/colibri-core) - Python binding to C++ library for extracting and working with with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
* [spaCy](https://github.com/honnibal/spaCy/) - Industrial strength NLP with Python and Cython. * [spaCy](https://github.com/honnibal/spaCy/) - Industrial strength NLP with Python and Cython.
* [PyStanfordDependencies](https://github.com/dmcc/PyStanfordDependencies) - Python interface for converting Penn Treebank trees to Stanford Dependencies. * [PyStanfordDependencies](https://github.com/dmcc/PyStanfordDependencies) - Python interface for converting Penn Treebank trees to Stanford Dependencies.