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

spaCy is a library for industrial-strength text processing in Python and Cython.
Its core values are efficiency, accuracy and minimalism: you get a fast pipeline of
state-of-the-art components, a nice API, and no clutter.

spaCy is particularly good for feature extraction, because it pre-loads lexical
resources, maps strings to integer IDs, and supports output of numpy arrays:

    >>> from spacy.en import English
    >>> from spacy.en import attrs
    >>> nlp = English()
    >>> tokens = nlp(u'An example sentence', pos_tag=True, parse=True)
    >>> tokens.to_array((attrs.LEMMA, attrs.POS, attrs.SHAPE, attrs.CLUSTER))

spaCy also makes it easy to add in-line mark up. Let's say you want to mark all
adverbs in red:

    >>> from spacy.defs import ADVERB
    >>> color = lambda t: u'\033[91m' % t if t.pos == ADVERB else u'%s'
    >>> print u''.join(color(t) + unicode(t) for t in tokens)

Tokens.__iter__ produces a sequence of Token objects.  The Token.__unicode__
method --- invoked by unicode(t) --- pads each token with any whitespace that
followed it.  So, u''.join(unicode(t) for t in tokens) is guaranteed to restore
the original string.

spaCy is also very efficient --- much more efficient than any other language
processing tools available.  The table below compares the time to tokenize, POS
tag and parse 100m words of text; it also shows accuracy on the standard
evaluation, from the Wall Street Journal:


+----------+----------+---------------+----------+
| System   | Tokenize | POS Tag       |          |
+----------+----------+---------------+----------+
| spaCy    | 37s      | 98s           |          |
+----------+----------+---------------+----------+
| NLTK     | 626s     | 44,310s (12h) |          |
+----------+----------+---------------+----------+
| CoreNLP  | 420s     | 1,300s (22m)  |          |
+----------+----------+---------------+----------+
| ZPar     |          | ~1,500s       |          |
+----------+----------+---------------+----------+

spaCy completes its whole pipeline faster than some of the other libraries can
tokenize the text.  Its POS tag accuracy is as good as any system available.
For parsing, I chose an algorithm that sacrificed some accuracy, in favour of
efficiency.

I wrote spaCy so that startups and other small companies could take advantage
of the enormous progress being made by NLP academics.  Academia is competitive,
and what you're competing to do is write papers --- so it's very hard to write
software useful to non-academics. Seeing this gap, I resigned from my post-doc,
and wrote spaCy.

.. toctree::
    :hidden:
    :maxdepth: 3

    features.rst
    license_stories.rst