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* Tweak overview docs

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Matthew Honnibal 2014-09-07 21:29:41 +02:00
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docs/guide/overview.rst
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@ -4,8 +4,7 @@ Overview
What and Why
------------
spaCy is a lightning-fast, full-cream NLP tokenizer, tightly coupled to a
global vocabulary store.
spaCy is a lightning-fast, full-cream NLP tokenizer and lexicon.
Most tokenizers give you a sequence of strings. That's barbaric.
Giving you strings invites you to compute on every *token*, when what
@ -13,33 +12,30 @@ you should be doing is computing on every *type*. Remember
`Zipf's law <http://en.wikipedia.org/wiki/Zipf's_law>`_: you'll
see exponentially fewer types than tokens.
Instead of strings, spacy gives you Lexeme IDs, from which you can access
an excellent set of pre-computed orthographic and distributional features:
Instead of strings, spaCy gives you references to Lexeme objects, from which you
can access an excellent set of pre-computed orthographic and distributional features:
::
>>> from spacy import en
>>> apples, are, nt, oranges, dots = en.tokenize(u"Apples aren't oranges...")
>>> en.is_lower(apples)
False
>>> en.prob_of(are) >= en.prob_of(oranges)
>>> apples, are, nt, oranges, dots = en.EN.tokenize(u"Apples aren't oranges...")
>>> are.prob >= oranges.prob
True
>>> en.can_tag(are, en.NOUN)
>>> apples.check_flag(en.IS_TITLE)
True
>>> apples.check_flag(en.OFT_TITLE)
False
>>> en.is_often_titled(apples)
>>> are.check_flag(en.CAN_NOUN)
False
Accessing these properties is essentially free: the Lexeme IDs are actually
memory addresses that point to structs --- so the only cost is the Python
function call overhead. If you call the accessor functions from Cython,
there's no overhead at all.
spaCy makes it easy to write very efficient NLP applications, because your feature
functions have to do almost no work: almost every lexical property you'll want
is pre-computed for you. See the tutorial for an example POS tagger.
Benchmark
---------
Because it exploits Zipf's law, spaCy is much more efficient than
regular-expression based tokenizers. See Algorithm and Implementation Details
for an explanation of how this works.
The tokenizer itself is also very efficient:
+--------+-------+--------------+--------------+
| System | Time | Words/second | Speed Factor |