mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-30 20:06:30 +03:00
72 lines
2.1 KiB
ReStructuredText
72 lines
2.1 KiB
ReStructuredText
Overview
|
|
========
|
|
|
|
What and Why
|
|
------------
|
|
|
|
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
|
|
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 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.EN.tokenize(u"Apples aren't oranges...")
|
|
>>> are.prob >= oranges.prob
|
|
True
|
|
>>> apples.check_flag(en.IS_TITLE)
|
|
True
|
|
>>> apples.check_flag(en.OFT_TITLE)
|
|
False
|
|
>>> are.check_flag(en.CAN_NOUN)
|
|
False
|
|
|
|
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
|
|
---------
|
|
|
|
The tokenizer itself is also very efficient:
|
|
|
|
+--------+-------+--------------+--------------+
|
|
| System | Time | Words/second | Speed Factor |
|
|
+--------+-------+--------------+--------------+
|
|
| NLTK | 6m4s | 89,000 | 1.00 |
|
|
+--------+-------+--------------+--------------+
|
|
| spaCy | 9.5s | 3,093,000 | 38.30 |
|
|
+--------+-------+--------------+--------------+
|
|
|
|
The comparison refers to 30 million words from the English Gigaword, on
|
|
a Maxbook Air. For context, calling string.split() on the data completes in
|
|
about 5s.
|
|
|
|
Pros and Cons
|
|
-------------
|
|
|
|
Pros:
|
|
|
|
- All tokens come with indices into the original string
|
|
- Full unicode support
|
|
- Extensible to other languages
|
|
- Batch operations computed efficiently in Cython
|
|
- Cython API
|
|
- numpy interoperability
|
|
|
|
Cons:
|
|
|
|
- It's new (released September 2014)
|
|
- Security concerns, from memory management
|
|
- Higher memory usage (up to 1gb)
|
|
- More conceptually complicated
|
|
- Tokenization rules expressed in code, not as data
|
|
|