spaCy/docs/source/index.rst

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================================
spaCy NLP Tokenizer and Lexicon
================================

spaCy is a library for industrial-strength NLP in Python and Cython.  It
assumes that NLP is mostly about solving machine learning problems, and that
solving these problems is mostly about feature extraction.  So, spaCy helps you
do feature extraction --- it helps you represent a linguistic context as
a vector of numbers.  It's also a great way to create an inverted index,
particularly if you want to index documents on fancier properties.

For commercial users, a trial license costs $0, with a one-time license fee of
$1,000 to use spaCy in production.  For non-commercial users, a GPL license is
available.  To quickly get the gist of the license terms, check out the license
user stories.


Unique Lexicon-centric design
=============================

spaCy takes care of all string-processing, efficiently and accurately.  This
makes a night-and-day difference to your feature extraction code.
Instead of a list of strings, spaCy's tokenizer gives you references to feature-rich
lexeme objects:

    >>> from spacy.en import EN
    >>> from spacy.feature_names import SIC, NORM, SHAPE, ASCIIED, PREFIX, SUFFIX, \
            LENGTH, CLUSTER, POS_TYPE, SENSE_TYPE, \
            IS_ALPHA, IS_ASCII, IS_DIGIT, IS_PUNCT, IS_SPACE, IS_TITLE, IS_UPPER, \
            LIKE_URL, LIKE_NUMBER
    >>> feats = (
            SIC, # ID of the original word form
            NORM, # ID of the normalized word form
            CLUSTER, # ID of the word's Brown cluster
            IS_TITLE, # Was the word title-cased?
            POS_TYPE # A cluster ID describing what POS tags the word is usually assigned
        )
    >>> tokens = EN.tokenize(u'Split words, punctuation, emoticons etc.! ^_^')
    >>> tokens.to_strings()
    [u'Split', u'words', u',', u'punctuation', u',', u'emoticons', u'etc.', u'!', u'^_^']
    >>> tokens.to_array(feats)[:5]
        array([[    1,  2,  3,  4],
               [...],
               [...],
               [...]])


spaCy is designed to **make the right thing easy**, where the right thing is to:

* **Use rich distributional and orthographic features**. Without these, your model
  will be very brittle and domain dependent.

* **Compute features per type, not per token**. Because of Zipf's law, you can
  expect this to be exponentially more efficient.

* **Minimize string processing**, and instead compute with arrays of ID ints.
  

Comparison with NLTK
====================

`NLTK <http://nltk.org>`_ provides interfaces to a wide-variety of NLP
tools and resources, and its own implementations of a few algorithms.  It comes
with comprehensive documentation, and a book introducing concepts in NLP.  For
these reasons, it's very widely known.  However, if you're trying to make money
or do cutting-edge research, NLTK is not a good choice.

The `list of stuff in NLTK <http://www.nltk.org/py-modindex.html>`_ looks impressive,
but almost none of it is useful for real work.  You're not going to make any money,
or do top research, by using the NLTK chat bots, theorem provers, toy CCG implementation,
etc.  Most of NLTK is there to assist in the explanation ideas in computational
linguistics, at roughly an undergraduate level.
But it also claims to support serious work, by wrapping external tools.

In a pretty well known essay, Joel Spolsky discusses the pain of dealing with 
`leaky abstractions <http://www.joelonsoftware.com/articles/LeakyAbstractions.html>`_.
An abstraction tells you to not care about implementation
details, but sometimes the implementation matters after all. When it
does, you have to waste time revising your assumptions.

NLTK's wrappers call external tools via subprocesses, and wrap this up so
that it looks like a native API.  This abstraction leaks *a lot*.  The system
calls impose far more overhead than a normal Python function call, which makes
the most natural way to program against the API infeasible. 


Case study: POS tagging
-----------------------

Here's a quick comparison of the following POS taggers:

* **Stanford (CLI)**: The Stanford POS tagger, invoked once as a batch process
  from the command-line;
* **nltk.tag.stanford**: The Stanford tagger, invoked document-by-document via
  NLTK's wrapper;
* **nltk.pos_tag**: NLTK's own POS tagger, invoked document-by-document.
* **spacy.en.pos_tag**: spaCy's POS tagger, invoked document-by-document.


+-------------------+-------------+--------+
| System            | Speed (w/s) | % Acc. |
+-------------------+-------------+--------+
| spaCy             | 107,000     | 96.7   |
+-------------------+-------------+--------+
| Stanford (CLI)    | 8,000       | 96.7   |
+-------------------+-------------+--------+
| nltk.pos_tag      | 543         | 94.0   |
+-------------------+-------------+--------+
| nltk.tag.stanford | 209         | 96.7   |
+-------------------+-------------+--------+

Experimental details here.  Three things are apparent from this comparison:

1. The native NLTK tagger, nltk.pos_tag, is both slow and inaccurate;

2. Calling the Stanford tagger document-by-document via NLTK is **40x** slower
   than invoking the model once as a batch process, via the command-line;

3. spaCy is over 10x faster than the Stanford tagger, even when called
   **sentence-by-sentence**.

The problem is that NLTK simply wraps the command-line
interfaces of these tools, so communication is via a subprocess.  NLTK does not
even hold open a pipe for you --- the model is reloaded, again and again.

To use the wrapper effectively, you should batch up your text as much as possible.
This probably isn't how you would like to structure your pipeline, and you
might not be able to batch up much text at all, e.g. if serving a single
request means processing a single document.
Technically, NLTK does give you Python functions to access lots of different
systems --- but, you can't use them as you would expect to use a normal Python
function.  The abstraction leaks.

Here's the bottom-line: the Stanford tools are written in Java, so using them
from Python sucks.  You shouldn't settle for this.  It's a problem that springs
purely from the tooling, rather than the domain.

Summary
-------

NLTK is a well-known Python library for NLP, but for the important bits, you
don't get actual Python modules.  You get wrappers which throw to external
tools, via subprocesses.  This is not at all the same thing.

spaCy is implemented in Cython, just like numpy, scikit-learn, lxml and other
high-performance Python libraries.  So you get a native Python API, but the
performance you expect from a program written in C.


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

    features.rst