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💫 Industrial-strength Natural Language Processing (NLP) in Python
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Matthew Honnibal 85485f5c2b Fix inconsistencies in generate_specials.py 
Re Issue #321, fix inconsistencies in the script that generates specials.json. The result still isn't so satisfying --- we need to revise this as we move to parse more morphologically rich languages.
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README.rst 

.. image:: https://travis-ci.org/spacy-io/spaCy.svg?branch=master
    :target: https://travis-ci.org/spacy-io/spaCy

==============================
spaCy: Industrial-strength NLP
==============================

spaCy is a library for advanced natural language processing in Python and Cython.

Documentation and details: https://spacy.io/

spaCy is built on the very latest research, but it isn't researchware.  It was
designed from day 1 to be used in real products. It's commercial open-source
software, released under the MIT license.


Features
--------

* Labelled dependency parsing (91.8% accuracy on OntoNotes 5)
* Named entity recognition (82.6% accuracy on OntoNotes 5)
* Part-of-speech tagging (97.1% accuracy on OntoNotes 5)
* Easy to use word vectors
* All strings mapped to integer IDs
* Export to numpy data arrays
* Alignment maintained to original string, ensuring easy mark up calculation
* Range of easy-to-use orthographic features.
* No pre-processing required. spaCy takes raw text as input, warts and newlines and all.

Top Peformance
--------------

* Fastest in the world: <50ms per document.  No faster system has ever been
  announced.
* Accuracy within 1% of the current state of the art on all tasks performed
  (parsing, named entity recognition, part-of-speech tagging).  The only more
  accurate systems are an order of magnitude slower or more.

Supports
--------

* CPython 2.6, 2.7, 3.3, 3.4, 3.5 (only 64 bit)
* OSX
* Linux
* Windows (Cygwin, MinGW, Visual Studio)