spaCy/docs/source/index.rst

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

.. note:: `Version 0.40`_ released, with better parsing, better parse-tree
  navigation, and major bug fixes.

.. _Version 0.40: updates.html

.. _Issue Tracker: https://github.com/honnibal/spaCy/issues

`spaCy`_ is a new library for text processing in Python and Cython.
I wrote it because I think small companies are terrible at
natural language processing (NLP).  Or rather:
small companies are using terrible NLP technology.

.. _spaCy: https://github.com/honnibal/spaCy/

To do great NLP, you have to know a little about linguistics, a lot
about machine learning, and almost everything about the latest research.
The people who fit this description seldom join small companies.
Most are broke --- they've just finished grad school.
If they don't want to stay in academia, they join Google, IBM, etc.

The net result is that outside of the tech giants, commercial NLP has changed
little in the last ten years.  In academia, it's changed entirely.  Amazing
improvements in quality. Orders of magnitude faster.  But the
academic code is always GPL, undocumented, unuseable, or all three.  You could
implement the ideas yourself, but the papers are hard to read, and training
data is exorbitantly expensive.  So what are you left with?  A common answer is
NLTK, which was written primarily as an educational resource.  Nothing past the
tokenizer is suitable for production use.

I used to think that the NLP community just needed to do more to communicate
its findings to software engineers.  So I wrote two blog posts, explaining
`how to write a part-of-speech tagger`_ and `parser`_.  Both were very well received,
and there's been a bit of interest in `my research software`_ --- even though
it's entirely undocumented, and mostly unuseable to anyone but me.

.. _`my research software`: https://github.com/syllog1sm/redshift/tree/develop

.. _`how to write a part-of-speech tagger`: https://honnibal.wordpress.com/2013/09/11/a-good-part-of-speechpos-tagger-in-about-200-lines-of-python/

.. _`parser`: https://honnibal.wordpress.com/2013/12/18/a-simple-fast-algorithm-for-natural-language-dependency-parsing/

So six months ago I quit my post-doc, and I've been working day and night on
spaCy since.  I'm now pleased to announce an alpha release.

If you're a small company doing NLP, I think spaCy will seem like a minor miracle.
It's by far the fastest NLP software ever released.
The full processing pipeline completes in 7ms per document, including accurate
tagging and parsing.  All strings are mapped to integer IDs, tokens are linked
to embedded word representations, and a range of useful features are pre-calculated
and cached.

If none of that made any sense to you, here's the gist of it.  Computers don't
understand text. This is unfortunate, because that's what the web almost entirely
consists of.  We want to recommend people text based on other text they liked.
We want to shorten text to display it on a mobile screen.  We want to aggregate
it, link it, filter it, categorise it, generate it and correct it.

spaCy provides a library of utility functions that help programmers build such
products.  It's commercial open source software: you can either use it under
the AGPL, or you can `buy a commercial license`_ for a one-time fee. 

.. _buy a commercial license: license.html

Example functionality
---------------------

Let's say you're developing a proofreading tool, or possibly an IDE for
writers.  You're convinced by Stephen King's advice that `adverbs are not your
friend <http://www.brainpickings.org/2013/03/13/stephen-king-on-adverbs/>`_, so
you want to **highlight all adverbs**.  We'll use one of the examples he finds
particularly egregious:

    >>> import spacy.en
    >>> from spacy.parts_of_speech import ADV
    >>> # Load the pipeline, and call it with some text.
    >>> nlp = spacy.en.English()
    >>> tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’", tag=True, parse=False)
    >>> print u''.join(tok.string.upper() if tok.pos == ADV else tok.string for tok in tokens)
    u‘Give it BACK,’ he pleaded ABJECTLY, ‘it’s mine.’


Easy enough --- but the problem is that we've also highlighted "back".
While "back" is undoubtedly an adverb, we probably don't want to highlight it.
If what we're trying to do is flag dubious stylistic choices, we'll need to
refine our logic.  It turns out only a certain type of adverb is of interest to
us.

There are lots of ways we might do this, depending on just what words
we want to flag.  The simplest way to exclude adverbs like "back" and "not"
is by word frequency: these words are much more common than the prototypical
manner adverbs that the style guides are worried about.

The :py:attr:`Lexeme.prob` and :py:attr:`Token.prob` attribute gives a
log probability estimate of the word:

   >>> nlp.vocab[u'back'].prob
   -7.403977394104004
   >>> nlp.vocab[u'not'].prob
   -5.407193660736084
   >>> nlp.vocab[u'quietly'].prob
   -11.07155704498291

(The probability estimate is based on counts from a 3 billion word corpus,
smoothed using the `Simple Good-Turing`_ method.)

.. _`Simple Good-Turing`: http://www.d.umn.edu/~tpederse/Courses/CS8761-FALL02/Code/sgt-gale.pdf

So we can easily exclude the N most frequent words in English from our adverb
marker.  Let's try N=1000 for now:

    >>> import spacy.en
    >>> from spacy.parts_of_speech import ADV
    >>> nlp = spacy.en.English()
    >>> # Find log probability of Nth most frequent word
    >>> probs = [lex.prob for lex in nlp.vocab]
    >>> probs.sort()
    >>> is_adverb = lambda tok: tok.pos == ADV and tok.prob < probs[-1000]
    >>> tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’")
    >>> print u''.join(tok.string.upper() if is_adverb(tok) else tok.string for tok in tokens)
    ‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’

There are lots of other ways we could refine the logic, depending on just what
words we want to flag.  Let's say we wanted to only flag adverbs that modified words
similar to "pleaded".  This is easy to do, as spaCy loads a vector-space
representation for every word (by default, the vectors produced by
`Levy and Goldberg (2014)`_).  Naturally, the vector is provided as a numpy
array:

    >>> pleaded = tokens[7]
    >>> pleaded.repvec.shape
    (300,)
    >>> pleaded.repvec[:5]
    array([ 0.04229792,  0.07459262,  0.00820188, -0.02181299,  0.07519238], dtype=float32)

.. _Levy and Goldberg (2014): https://levyomer.wordpress.com/2014/04/25/dependency-based-word-embeddings/

We want to sort the words in our vocabulary by their similarity to "pleaded".
There are lots of ways to measure the similarity of two vectors.  We'll use the
cosine metric:

    >>> from numpy import dot
    >>> from numpy.linalg import norm
 
    >>> cosine = lambda v1, v2: dot(v1, v2) / (norm(v1) * norm(v2))
    >>> words = [w for w in nlp.vocab if w.lower]
    >>> words.sort(key=lambda w: cosine(w.repvec, pleaded.repvec))
    >>> words.reverse()
    >>> print('1-20', ', '.join(w.orth_ for w in words[0:20]))
    1-20 pleaded, pled, plead, confessed, interceded, pleads, testified, conspired, motioned, demurred, countersued, remonstrated, begged, apologised, consented, acquiesced, petitioned, quarreled, appealed, pleading
    >>> print('50-60', ', '.join(w.orth_ for w in words[50:60]))
    50-60 counselled, bragged, backtracked, caucused, refiled, dueled, mused, dissented, yearned, confesses
    >>> print('100-110', ', '.join(w.orth_ for w in words[100:110]))
    100-110 cabled, ducked, sentenced, perjured, absconded, bargained, overstayed, clerked, confided, sympathizes
    >>> print('1000-1010', ', '.join(w.orth_ for w in words[1000:1010]))
    1000-1010 scorned, baled, righted, requested, swindled, posited, firebombed, slimed, deferred, sagged
    >>> print('50000-50010', ', '.join(w.orth_ for w in words[50000:50010]))
    50000-50010, fb, ford, systems, puck, anglers, ik, tabloid, dirty, rims, artists

As you can see, the similarity model that these vectors give us is excellent
--- we're still getting meaningful results at 1000 words, off a single
prototype!  The only problem is that the list really contains two clusters of
words: one associated with the legal meaning of "pleaded", and one for the more
general sense.  Sorting out these clusters is an area of active research.


A simple work-around is to average the vectors of several words, and use that
as our target:

    >>> say_verbs = ['pleaded', 'confessed', 'remonstrated', 'begged', 'bragged', 'confided', 'requested']
    >>> say_vector = sum(nlp.vocab[verb].repvec for verb in say_verbs) / len(say_verbs)
    >>> words.sort(key=lambda w: cosine(w.repvec * say_vector))
    >>> words.reverse()
    >>> print('1-20', ', '.join(w.orth_ for w in words[0:20]))
    1-20 bragged, remonstrated, enquired, demurred, sighed, mused, intimated, retorted, entreated, motioned, ranted, confided, countersued, gestured, implored, interceded, muttered, marvelled, bickered, despaired
    >>> print('50-60', ', '.join(w.orth_ for w in words[50:60]))
    50-60 flaunted, quarrelled, ingratiated, vouched, agonized, apologised, lunched, joked, chafed, schemed
    >>> print('1000-1010', ', '.join(w.orth_ for w in words[1000:1010]))
    1000-1010 hoarded, waded, ensnared, clamoring, abided, deploring, shriveled, endeared, rethought, berate

These definitely look like words that King might scold a writer for attaching
adverbs to.  Recall that our original adverb highlighting function looked like
this:

    >>> import spacy.en
    >>> from spacy.parts_of_speech import ADV
    >>> # Load the pipeline, and call it with some text.
    >>> nlp = spacy.en.English()
    >>> tokens = nlp("‘Give it back,’ he pleaded abjectly, ‘it’s mine.’",
                     tag=True, parse=False)
    >>> print(''.join(tok.string.upper() if tok.pos == ADV else tok.string for tok in tokens))
    ‘Give it BACK,’ he pleaded ABJECTLY, ‘it’s mine.’



We wanted to refine the logic so that only adverbs modifying evocative verbs 
of communication, like "pleaded", were highlighted.  We've now built a vector that
represents that type of word, so now we can highlight adverbs based on very
subtle logic, honing in on adverbs that seem the most stylistically
problematic, given our starting assumptions:

    >>> import numpy
    >>> from numpy import dot
    >>> from numpy.linalg import norm
    >>> import spacy.en
    >>> from spacy.parts_of_speech import ADV, VERB
    >>> cosine = lambda v1, v2: dot(v1, v2) / (norm(v1), norm(v2))
    >>> def is_bad_adverb(token, target_verb, tol):
    ...   if token.pos != ADV 
    ...     return False
    ...   elif token.head.pos != VERB:
    ...     return False
    ...   elif cosine(token.head.repvec, target_verb) < tol:
    ...     return False
    ...   else:
    ...     return True


This example was somewhat contrived --- and, truth be told, I've never really
bought the idea that adverbs were a grave stylistic sin.  But hopefully it got
the message across: the state-of-the-art NLP technologies are very powerful.
spaCy gives you easy and efficient access to them, which lets you build all
sorts of use products and features that were previously impossible.


Speed Comparison
----------------

**Set up**: 100,000 plain-text documents were streamed from an SQLite3
database, and processed with an NLP library, to one of three levels of detail
--- tokenization, tagging, or parsing.  The tasks are additive: to parse the
text you have to tokenize and tag it.  The  pre-processing was not subtracted
from the times --- I report the time required for the pipeline to complete.
I report mean times per document, in milliseconds. 

**Hardware**: Intel i7-3770 (2012)

.. table:: Efficiency comparison. Lower is better. 

  +--------------+---------------------------+--------------------------------+
  |              | Absolute (ms per doc)     | Relative (to spaCy)            |
  +--------------+----------+--------+-------+----------+---------+-----------+
  | System       | Tokenize | Tag    | Parse | Tokenize | Tag     | Parse     |
  +--------------+----------+--------+-------+----------+---------+-----------+
  | spaCy        | 0.2ms    | 1ms    | 7ms   | 1x       | 1x      | 1x        |
  +--------------+----------+--------+-------+----------+---------+-----------+
  | CoreNLP      | 2ms      | 10ms   | 49ms  | 10x      | 10x     | 7x        |
  +--------------+----------+--------+-------+----------+---------+-----------+
  | ZPar         | 1ms      | 8ms    | 850ms | 5x       | 8x      | 121x      |
  +--------------+----------+--------+-------+----------+---------+-----------+
  | NLTK         | 4ms      | 443ms  | n/a   | 20x      | 443x    |  n/a      |
  +--------------+----------+--------+-------+----------+---------+-----------+


Efficiency is a major concern for NLP applications.  It is very common to hear
people say that they cannot afford more detailed processing, because their
datasets are too large.  This is a bad position to be in.  If you can't apply
detailed processing, you generally have to cobble together various heuristics.
This normally takes a few iterations, and what you come up with will usually be
brittle and difficult to reason about.

spaCy's parser is faster than most taggers, and its tokenizer is fast enough
for any workload.  And the tokenizer doesn't just give you a list
of strings.  A spaCy token is a pointer to a Lexeme struct, from which you can
access a wide range of pre-computed features, including embedded word
representations.

.. I wrote spaCy because I think existing commercial NLP engines are crap.
  Alchemy API are a typical example.  Check out this part of their terms of
  service:
  publish or perform any benchmark or performance tests or analysis relating to
  the Service or the use thereof without express authorization from AlchemyAPI;

.. Did you get that? You're not allowed to evaluate how well their system works,
  unless you're granted a special exception.  Their system must be pretty
  terrible to motivate such an embarrassing restriction.
  They must know this makes them look bad, but they apparently believe allowing
  you to evaluate their product would make them look even worse!

.. spaCy is based on science, not alchemy.  It's open source, and I am happy to
  clarify any detail of the algorithms I've implemented.
  It's evaluated against the current best published systems, following the standard
  methodologies.  These evaluations show that it performs extremely well.  

Accuracy Comparison
-------------------

.. table:: Accuracy comparison, on the standard benchmark data from the Wall Street Journal.

  +--------------+----------+------------+
  | System       | POS acc. | Parse acc. |
  +--------------+----------+------------+
  | spaCy        | 97.2     | 92.4       |
  +--------------+----------+------------+
  | CoreNLP      | 96.9     | 92.2       | 
  +--------------+----------+------------+
  | ZPar         | 97.3     | 92.9       |
  +--------------+----------+------------+
  | Redshift     | 97.3     | 93.5       |
  +--------------+----------+------------+
  | NLTK         | 94.3     | n/a        |
  +--------------+----------+------------+

.. See `Benchmarks`_ for details.

The table above compares spaCy to some of the current state-of-the-art systems,
on the standard evaluation from the Wall Street Journal, given gold-standard
sentence boundaries and tokenization.  I'm in the process of completing a more
realistic evaluation on web text.


spaCy's parser offers a better speed/accuracy trade-off than any published
system: its accuracy is within 1% of the current state-of-the-art, and it's
seven times faster than the 2014 CoreNLP neural network parser, which is the
previous fastest parser that I'm aware of.


.. toctree::
    :maxdepth: 3

    index.rst
    quickstart.rst
    api.rst
    howworks.rst
    license.rst 
    updates.rst