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71 lines
2.1 KiB
ReStructuredText
71 lines
2.1 KiB
ReStructuredText
Overview
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========
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What and Why
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------------
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spaCy is a lightning-fast, full-cream NLP tokenizer and lexicon.
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Most tokenizers give you a sequence of strings. That's barbaric.
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Giving you strings invites you to compute on every *token*, when what
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you should be doing is computing on every *type*. Remember
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`Zipf's law <http://en.wikipedia.org/wiki/Zipf's_law>`_: you'll
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see exponentially fewer types than tokens.
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Instead of strings, spaCy gives you references to Lexeme objects, from which you
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can access an excellent set of pre-computed orthographic and distributional features:
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::
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>>> from spacy import en
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>>> apples, are, nt, oranges, dots = en.EN.tokenize(u"Apples aren't oranges...")
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>>> are.prob >= oranges.prob
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True
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>>> apples.check_flag(en.IS_TITLE)
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True
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>>> apples.check_flag(en.OFT_TITLE)
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False
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>>> are.check_flag(en.CAN_NOUN)
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False
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spaCy makes it easy to write efficient NLP applications, because your feature
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functions have to do almost no work: almost every lexical property you'll want
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is pre-computed for you. See the tutorial for an example POS tagger.
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Benchmark
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---------
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The tokenizer itself is also efficient:
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+--------+-------+--------------+--------------+
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| System | Time | Words/second | Speed Factor |
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+--------+-------+--------------+--------------+
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| NLTK | 6m4s | 89,000 | 1.00 |
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+--------+-------+--------------+--------------+
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| spaCy | 9.5s | 3,093,000 | 38.30 |
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+--------+-------+--------------+--------------+
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The comparison refers to 30 million words from the English Gigaword, on
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a Maxbook Air. For context, calling string.split() on the data completes in
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about 5s.
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Pros and Cons
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-------------
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Pros:
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- All tokens come with indices into the original string
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- Full unicode support
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- Extendable to other languages
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- Batch operations computed efficiently in Cython
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- Cython API
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- numpy interoperability
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Cons:
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- It's new (released September 2014)
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- Security concerns, from memory management
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- Higher memory usage (up to 1gb)
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- More conceptually complicated
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- Tokenization rules expressed in code, not as data
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