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* Another redraft of index.rst
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Matthew Honnibal
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@ -11,30 +11,57 @@ spaCy is a library for industrial-strength text processing in Python and Cython.
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It features extremely efficient, up-to-date algorithms, and a rethink of how those
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algorithms should be accessed.
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Most text-processing libraries give you APIs that look like this:
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A typical text-processing API looks something like this:
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>>> import nltk
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>>> nltk.pos_tag(nltk.word_tokenize('''Some string of language.'''))
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[('Some', 'DT'), ('string', 'VBG'), ('of', 'IN'), ('language', 'NN'), ('.', '.')]
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A list of strings is good for poking around, or for printing the annotation to
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evaluate it. But to actually *use* the output, you have to jump through some
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hoops. If you're doing some machine learning, all the strings have to be
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mapped to integers, and you have to save and load the mapping at training and
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runtime. If you want to display mark-up based on the annotation, you have to
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realign the tokens to your original string.
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This API often leaves you with a lot of busy-work. If you're doing some machine
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learning or information extraction, all the strings have to be mapped to integers,
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and you have to save and load the mapping at training and runtime. If you want
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to display mark-up based on the annotation, you have to realign the tokens to your
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original string.
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With spaCy, you should never have to do any string processing at all:
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I've been writing NLP systems for almost ten years now, so I've done these
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things dozens of times. When designing spaCy, I thought carefully about how to
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make the right thing easy.
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We begin by initializing a global vocabulary store:
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>>> from spacy.en import EN
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>>> from spacy.en import feature_names as fn
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>>> tokens = EN.tokenize('''Some string of language.''')
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>>> tokens.to_array((fn.WORD, fn.SUFFIX, fn.CLUSTER, fn.POS, fn.LEMMA))
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>>> EN.load()
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A range of excellent features are pre-computed for you, and by default the
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words are part-of-speech tagged and lemmatized. We do this by default because
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even with these extra processes, spaCy is still several times faster than
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most tokenizers:
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The vocabulary reads in a data file with all sorts of pre-computed lexical
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features. You can load anything you like here, but by default I give you:
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* String IDs for the word's string, its prefix, suffix and "shape";
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* Length (in unicode code-points)
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* A cluster ID, representing distributional similarity;
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* A cluster ID, representing its typical POS tag distribution;
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* Good-turing smoothed unigram probability;
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* 64 boolean features, for assorted orthographic and distributional features.
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With so many features pre-computed, you usually don't have to do any string
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processing at all. You give spaCy your string, and tell it to give you either
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a numpy array, or a counts dictionary:
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>>> from spacy.en import feature_names as fn
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>>> tokens = EN.tokenize(u'''Some string of language.''')
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>>> tokens.to_array((fn.WORD, fn.SUFFIX, fn.CLUSTER))
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...
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>>> tokens.count_by(fn.WORD)
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If you do need strings, you can simply iterate over the Tokens object:
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>>> for token in tokens:
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...
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I mostly use this for debugging and testing.
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spaCy returns these rich Tokens objects much faster than most other tokenizers
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can give you a list of strings --- in fact, spaCy's POS tagger is *4 times
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faster* than CoreNLP's tokenizer:
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+----------+----------+---------------+----------+
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| System | Tokenize | POS Tag | |
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@ -48,157 +75,7 @@ most tokenizers:
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| ZPar | | ~1,500s | |
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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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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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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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