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227 lines
9.5 KiB
ReStructuredText
.. spaCy documentation master file, created by
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sphinx-quickstart on Tue Aug 19 16:27:38 2014.
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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================================
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spaCy NLP Tokenizer and Lexicon
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================================
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spaCy is a library for industrial-strength NLP in Python and Cython. spaCy's
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take on NLP is that it's mostly about feature extraction --- that's the part
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that's specific to NLP, so that's what an NLP library should focus on.
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It should tell you what the current best-practice is, and help you do exactly
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that, quickly and efficiently.
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Best-practice is to **use lots of large lexicons**. Let's say you hit the word
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*belieber* in production. What will your system know about this word? A bad
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system will only know things about the words in its training corpus, which
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probably consists of texts written before Justin Bieber was even born.
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It doesn't have to be like that.
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Unique Lexicon-centric design
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=============================
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spaCy helps you build models that generalise better, by making it easy to use
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more robust features. Instead of a list of strings, the tokenizer returns
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references to rich lexical types. Its tokenizer returns sequence of references
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to rich lexical types. Features which ask about the word's Brown cluster, its
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typical part-of-speech tag, how it's usually cased etc require no extra effort:
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>>> from spacy.en import EN
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>>> from spacy.feature_names import *
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>>> feats = (
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SIC, # ID of the original word form
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NORM, # ID of the normalized word form
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CLUSTER, # ID of the word's Brown cluster
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IS_TITLE, # Was the word title-cased?
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POS_TYPE # A cluster ID describing what POS tags the word is usually assigned
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)
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>>> tokens = EN.tokenize(u'Split words, punctuation, emoticons etc.! ^_^')
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>>> tokens.to_array(feats)[:5]
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array([[ 1, 2, 3, 4],
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[...],
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[...],
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[...]])
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spaCy is designed to **make the right thing easy**, where the right thing is to:
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* **Use rich distributional and orthographic features**. Without these, your model
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will be very brittle and domain dependent.
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* **Compute features per type, not per token**. Because of Zipf's law, you can
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expect this to be exponentially more efficient.
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* **Minimize string processing**, and instead compute with arrays of ID ints.
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For the current list of lexical features, see `Lexical Features`_.
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.. _lexical features: features.html
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Tokenization done right
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=======================
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Most tokenizers rely on complicated regular expressions. Often, they leave you
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with no way to align the tokens back to the original string --- a vital feature
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if you want to display some mark-up, such as spelling correction. The regular
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expressions also interact, making it hard to accommodate special cases.
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spaCy introduces a **novel tokenization algorithm** that's much faster and much
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more flexible:
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.. code-block:: python
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def tokenize(string, prefixes={}, suffixes={}, specials={}):
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'''Sketch of spaCy's tokenization algorithm.'''
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tokens = []
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cache = {}
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for chunk in string.split():
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# Because of Zipf's law, the cache serves the majority of "chunks".
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if chunk in cache:
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tokens.extend(cache[chunl])
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continue
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key = chunk
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subtokens = []
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# Process a chunk by splitting off prefixes e.g. ( " { and suffixes e.g. , . :
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# If we split one off, check whether we're left with a special-case,
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# e.g. contractions (can't, won't, etc), emoticons, abbreviations, etc.
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# This makes the tokenization easy to update and customize.
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while chunk:
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prefix, chunk = _consume_prefix(chunk, prefixes)
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if prefix:
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subtokens.append(prefix)
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if chunk in specials:
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subtokens.extend(specials[chunk])
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break
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suffix, chunk = _consume_suffix(chunk, suffixes)
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if suffix:
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subtokens.append(suffix)
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if chunk in specials:
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subtokens.extend(specials[chunk])
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break
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cache[key] = subtokens
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Your data is going to have its own quirks, so it's really useful to have
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a tokenizer you can easily control. To see the limitations of the standard
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regex-based approach, check out `CMU's recent work on tokenizing tweets <http://www.ark.cs.cmu.edu/TweetNLP/>`_. Despite a lot of careful attention, they can't handle all of their
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known emoticons correctly --- doing so would interfere with the way they
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process other punctuation. This isn't a problem for spaCy: we just add them
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all to the special tokenization rules.
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spaCy's tokenizer is also incredibly efficient:
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+--------+---------------+--------------+
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| System | Tokens/second | Speed Factor |
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+--------+---------------+--------------+
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| NLTK | 89 000 | 1.00 |
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+--------+---------------+--------------+
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| spaCy | 3 093 000 | 38.30 |
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+--------+---------------+--------------+
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spaCy can create an inverted index of the 1.8 billion word Gigaword corpus,
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in under half an hour --- on a Macbook Air. See the `inverted
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index tutorial`_.
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.. _inverted index tutorial: index_tutorial.html
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Comparison with NLTK
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====================
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`NLTK <http://nltk.org>`_ provides interfaces to a wide-variety of NLP
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tools and resources, and its own implementations of a few algorithms. It comes
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with comprehensive documentation, and a book introducing concepts in NLP. For
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these reasons, it's very widely known. However, if you're trying to make money
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or do cutting-edge research, NLTK is not a good choice.
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The `list of stuff in NLTK <http://www.nltk.org/py-modindex.html>`_ looks impressive,
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but almost none of it is useful for real work. You're not going to make any money,
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or do top research, by using the NLTK chat bots, theorem provers, toy CCG implementation,
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etc. Most of NLTK is there to assist in the explanation ideas in computational
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linguistics, at roughly an undergraduate level.
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But it also claims to support serious work, by wrapping external tools.
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In a pretty well known essay, Joel Spolsky discusses the pain of dealing with
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`leaky abstractions <http://www.joelonsoftware.com/articles/LeakyAbstractions.html>`_.
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An abstraction tells you to not care about implementation
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details, but sometimes the implementation matters after all. When it
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does, you have to waste time revising your assumptions.
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NLTK's wrappers call external tools via subprocesses, and wrap this up so
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that it looks like a native API. This abstraction leaks *a lot*. The system
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calls impose far more overhead than a normal Python function call, which makes
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the most natural way to program against the API infeasible.
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Case study: POS tagging
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-----------------------
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Here's a quick comparison of the following POS taggers:
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* **Stanford (CLI)**: The Stanford POS tagger, invoked once as a batch process
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from the command-line;
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* **nltk.tag.stanford**: The Stanford tagger, invoked document-by-document via
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NLTK's wrapper;
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* **nltk.pos_tag**: NLTK's own POS tagger, invoked document-by-document.
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* **spacy.en.pos_tag**: spaCy's POS tagger, invoked document-by-document.
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+-------------------+-------------+--------+
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| System | Speed (w/s) | % Acc. |
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+-------------------+-------------+--------+
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| spaCy | 107,000 | 96.7 |
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+-------------------+-------------+--------+
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| Stanford (CLI) | 8,000 | 96.7 |
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+-------------------+-------------+--------+
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| nltk.pos_tag | 543 | 94.0 |
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+-------------------+-------------+--------+
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| nltk.tag.stanford | 209 | 96.7 |
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+-------------------+-------------+--------+
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Experimental details TODO. Three things are apparent from this comparison:
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1. The native NLTK tagger, nltk.pos_tag, is both slow and inaccurate;
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2. Calling the Stanford tagger document-by-document via NLTK is **40x** slower
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than invoking the model once as a batch process, via the command-line;
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3. spaCy is over 10x faster than the Stanford tagger, even when called
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**sentence-by-sentence**.
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The problem is that NLTK simply wraps the command-line
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interfaces of these tools, so communication is via a subprocess. NLTK does not
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even hold open a pipe for you --- the model is reloaded, again and again.
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To use the wrapper effectively, you should batch up your text as much as possible.
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This probably isn't how you would like to structure your pipeline, and you
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might not be able to batch up much text at all, e.g. if serving a single
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request means processing a single document.
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Technically, NLTK does give you Python functions to access lots of different
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systems --- but, you can't use them as you would expect to use a normal Python
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function. The abstraction leaks.
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Here's the bottom-line: the Stanford tools are written in Java, so using them
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from Python sucks. You shouldn't settle for this. It's a problem that springs
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purely from the tooling, rather than the domain.
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Summary
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-------
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NLTK is a well-known Python library for NLP, but for the important bits, you
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don't get actual Python modules. You get wrappers which throw to external
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tools, via subprocesses. This is not at all the same thing.
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spaCy is implemented in Cython, just like numpy, scikit-learn, lxml and other
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high-performance Python libraries. So you get a native Python API, but the
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performance you expect from a program written in C.
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.. toctree::
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:hidden:
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:maxdepth: 3
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features.rst
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license_stories.rst
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