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* Impove index docs
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Matthew Honnibal
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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: Text-processing for products
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===================================
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==============================
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spaCy: Industrial-strength NLP
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==============================
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spaCy is a library for industrial-strength text processing in Python and Cython.
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Its core values are efficiency, accuracy and minimalism: you get a fast pipeline of
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state-of-the-art components, a nice API, and no clutter:
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It is commercial open source software, with a dual (AGPL or commercial)
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license.
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>>> from spacy.en import English
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>>> nlp = English()
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>>> tokens = nlp(u'An example sentence', tag=True, parse=True)
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>>> for token in tokens:
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... print token.lemma, token.pos, bin(token.cluster)
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an DT Xx 0b111011110
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example NN xxxx 0b111110001
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sentence NN xxxx 0b1101111110010
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If you're a small company doing NLP, spaCy might seem like a minor miracle.
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It's by far the fastest NLP software available. The full processing pipeline
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completes in 7ms, including state-of-the-art part-of-speech tagging and
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dependency parsing. All strings are mapped to integer IDs, tokens
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are linked to word vectors and other lexical resources, and a range of useful
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features are pre-calculated and cached.
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spaCy is particularly good for feature extraction, because it pre-loads lexical
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resources, maps strings to integer IDs, and supports output of numpy arrays:
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If none of that made any sense to you, here's the gist of it. Computers don't
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understand text. This is unfortunate, because that's what the web almost entirely
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consists of. We want to recommend people text based on other text they liked.
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We want to shorten text to display it on a mobile screen. We want to aggregate
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it, link it, filter it, categorise it, generate it and correct it.
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>>> from spacy.en import attrs
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>>> tokens.to_array((attrs.LEMMA, attrs.POS, attrs.SHAPE, attrs.CLUSTER))
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array([[ 1265, 14, 76, 478],
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[ 1545, 24, 262, 497],
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[ 3385, 24, 262, 14309]])
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spaCy provides a set of utility functions that help programmers build such
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products. It's an NLP engine, analogous to the 3d engines commonly licensed
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for game development.
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spaCy also makes it easy to add in-line mark up. Let's say you're convinced by
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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 mark
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them in red. We'll use one of the examples he finds particularly egregious:
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Example functionality
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---------------------
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>>> tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’")
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>>> red = lambda string: u'\033[91m{0}\033[0m'.format(string)
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>>> red = lambda string: unicode(string).upper() # TODO -- make red work on website...
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>>> print u''.join(red(t) if t.is_adverb else unicode(t) for t in tokens)
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Let's say you're developing a proofreading tool, or possibly an IDE for
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writers. You're convinced by Stephen King's advice that `adverbs are not your
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friend <http://www.brainpickings.org/2013/03/13/stephen-king-on-adverbs/>`_, so
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you want to **mark adverbs in red**. We'll use one of the examples he finds
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particularly egregious:
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>>> import spacy.en
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>>> from spacy.enums import ADVERB
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>>> # Load the pipeline, and call it with some text.
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>>> nlp = spacy.en.English()
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>>> tokens = nlp("‘Give it back,’ he pleaded abjectly, ‘it’s mine.’",
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tag=True, parse=True)
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>>> output = ''
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>>> for tok in tokens:
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... # Token.string preserves whitespace, making it easy to
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... # reconstruct the original string.
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... output += tok.string.upper() if tok.is_pos(ADVERB) else tok.string
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>>> print(output)
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‘Give it BACK,’ he pleaded ABJECTLY, ‘it’s mine.’
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Easy --- except, "back" isn't the sort of word we're looking for, even though
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it's undeniably an adverb. Let's search refine the logic a little, and only
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highlight adverbs that modify verbs:
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Easy enough --- but the problem is that we've also highlighted "back", when probably
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we only wanted to highlight "abjectly". This is undoubtedly an adverb, but it's
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not the sort of adverb King is talking about. This is a persistent problem when
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dealing with linguistic categories: the prototypical examples, the ones whic
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spring to your mind, are often not the most common cases.
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>>> print u''.join(red(t) if t.is_adverb and t.head.is_verb else unicode(t) for t in tokens)
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There are lots of ways we might refine our logic, depending on just what words
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we want to flag. The simplest way to filter out adverbs like "back" and "not"
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is by word frequency: these words are much more common than the manner adverbs
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the style guides are worried about.
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The prob attribute of a Lexeme or Token object gives a log probability estimate
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of the word, based on smoothed counts from a 3bn word corpus:
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>>> nlp.vocab[u'back'].prob
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-7.403977394104004
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>>> nlp.vocab[u'not'].prob
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-5.407193660736084
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>>> nlp.vocab[u'quietly'].prob
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-11.07155704498291
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So we can easily exclude the N most frequent words in English from our adverb
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marker. Let's try N=1000 for now:
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>>> import spacy.en
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>>> from spacy.enums import ADVERB
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>>> nlp = spacy.en.English()
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>>> # Find log probability of Nth most frequent word
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>>> probs = [lex.prob for lex in nlp.vocab]
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>>> is_adverb = lambda tok: tok.is_pos(ADVERB) and tok.prob < probs[-1000]
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>>> tokens = nlp("‘Give it back,’ he pleaded abjectly, ‘it’s mine.’",
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tag=True, parse=True)
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>>> print(''.join(tok.string.upper() if is_adverb(tok) else tok.string))
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‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’
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spaCy is also very efficient --- much more efficient than any other language
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processing tools available. The table below compares the time to tokenize, POS
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tag and parse a document (amortized over 100k samples). It also shows accuracy
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on the standard evaluation, from the Wall Street Journal:
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There are lots of ways to refine the logic, depending on just what words we
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want to flag. Let's define this narrowly, and only flag adverbs applied to
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verbs of communication or perception:
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+----------+----------+---------+----------+----------+------------+
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| System | Tokenize | POS Tag | Parse | POS Acc. | Parse Acc. |
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+----------+----------+---------+----------+----------+------------+
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| spaCy | 0.37ms | 0.98ms | 10ms | 97.3% | 92.4% |
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+----------+----------+---------+----------+----------+------------+
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| NLTK | 6.2ms | 443ms | n/a | 94.0% | n/a |
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+----------+----------+---------+----------+----------+------------+
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| CoreNLP | 4.2ms | 13ms | todo | 96.97% | 92.2% |
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+----------+----------+---------+----------+----------+------------+
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| ZPar | n/a | 15ms | 850ms | 97.3% | 92.9% |
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+----------+----------+---------+----------+----------+------------+
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>>> from spacy.enums import VERB, WN_V_COMMUNICATION, WN_V_COGNITION
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>>> def is_say_verb(tok):
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... return tok.is_pos(VERB) and (tok.check_flag(WN_V_COMMUNICATION) or
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tok.check_flag(WN_V_COGNITION))
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>>> print(''.join(tok.string.upper() if is_adverb(tok) and is_say_verb(tok.head)
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else tok.string))
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‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’
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(The CoreNLP results refer to their recently published shift-reduce neural
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network parser.)
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The two flags refer to the 45 top-level categories in the WordNet ontology.
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spaCy stores membership in these categories as a bit set, because
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words can have multiple senses. We only need one 64
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bit flag variable per word in the vocabulary, so this useful data requires only
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2.4mb of memory.
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I wrote spaCy so that startups and other small companies could take advantage
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of the enormous progress being made by NLP academics. Academia is competitive,
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and what you're competing to do is write papers --- so it's very hard to write
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software useful to non-academics. Seeing this gap, I resigned from my post-doc,
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and wrote spaCy.
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spaCy packs all sorts of other goodies into its lexicon.
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Words are mapped to one these rich lexical types immediately, during
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tokenization --- and spaCy's tokenizer is *fast*.
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Efficiency
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----------
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.. table:: Efficiency comparison. See `Benchmarks`_ for details.
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+--------------+---------------------------+--------------------------------+
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| | Absolute (ms per doc) | Relative (to spaCy) |
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+--------------+----------+--------+-------+----------+---------+-----------+
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| System | Tokenize | Tag | Parse | Tokenize | Tag | Parse |
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+--------------+----------+--------+-------+----------+---------+-----------+
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| spaCy | 0.2ms | 1ms | 7ms | 1x | 1x | 1x |
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+--------------+----------+--------+-------+----------+---------+-----------+
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| CoreNLP | 2ms | 10ms | 49ms | 10x | 10x | 7x |
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+--------------+----------+--------+-------+----------+---------+-----------+
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| ZPar | 1ms | 8ms | 850ms | 5x | 8x | 121x |
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+--------------+----------+--------+-------+----------+---------+-----------+
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| NLTK | 4ms | 443ms | n/a | 20x | 443x | n/a |
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+--------------+----------+--------+-------+----------+---------+-----------+
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Efficiency is a major concern for NLP applications. It is very common to hear
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people say that they cannot afford more detailed processing, because their
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datasets are too large. This is a bad position to be in. If you can't apply
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detailed processing, you generally have to cobble together various heuristics.
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This normally takes a few iterations, and what you come up with will usually be
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brittle and difficult to reason about.
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spaCy's parser is faster than most taggers, and its tokenizer is fast enough
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for truly web-scale processing. And the tokenizer doesn't just give you a list
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of strings. A spaCy token is a pointer to a Lexeme struct, from which you can
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access a wide range of pre-computed features.
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.. I wrote spaCy because I think existing commercial NLP engines are crap.
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Alchemy API are a typical example. Check out this part of their terms of
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service:
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publish or perform any benchmark or performance tests or analysis relating to
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the Service or the use thereof without express authorization from AlchemyAPI;
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.. Did you get that? You're not allowed to evaluate how well their system works,
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unless you're granted a special exception. Their system must be pretty
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terrible to motivate such an embarrassing restriction.
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They must know this makes them look bad, but they apparently believe allowing
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you to evaluate their product would make them look even worse!
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.. spaCy is based on science, not alchemy. It's open source, and I am happy to
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clarify any detail of the algorithms I've implemented.
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It's evaluated against the current best published systems, following the standard
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methodologies. These evaluations show that it performs extremely well.
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Accuracy
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--------
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.. table:: Accuracy comparison, on the standard benchmark data from the Wall Street Journal. See `Benchmarks`_ for details.
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+--------------+----------+------------+
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| System | POS acc. | Parse acc. |
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+--------------+----------+------------+
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| spaCy | 97.2 | 92.4 |
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+--------------+----------+------------+
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| CoreNLP | 96.9 | 92.2 |
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+--------------+----------+------------+
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| ZPar | 97.3 | 92.9 |
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+--------------+----------+------------+
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| NLTK | 94.3 | n/a |
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+--------------+----------+------------+
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spaCy is dual-licensed: you can either use it under the GPL, or pay a one-time
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fee of $5000 for a commercial license. I think this is excellent value:
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you'll find NLTK etc much more expensive, because what you save on license
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cost, you'll lose many times over in lost productivity. $5000 does not buy you
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much developer time.
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.. toctree::
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:hidden:
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:maxdepth: 3
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license.rst
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quickstart.rst
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features.rst
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license_stories.rst
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api.rst
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