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Matthew Honnibal 2014-12-09 16:08:01 +11:00
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@ -10,14 +10,27 @@ spaCy NLP Tokenizer and Lexicon
spaCy is a library for industrial-strength NLP in Python and Cython. spaCy's
take on NLP is that it's mostly about feature extraction --- that's the part
that's specific to NLP, so that's what an NLP library should focus on.
It should tell you what the current best-practice is, and help you do exactly
that, quickly and efficiently.
Best-practice is to **use lots of large lexicons**. Let's say you hit the word
*belieber* in production. What will your system know about this word? A bad
system will only know things about the words in its training corpus, which
probably consists of texts written before Justin Bieber was even born.
It doesn't have to be like that.
spaCy also believes that for NLP, **efficiency is critical**. If you're
running batch jobs, you probably have an enormous amount of data; if you're
serving requests one-by-one, you want lower latency and fewer servers. Even if
you're doing exploratory research on relatively small samples, you should still
value efficiency, because it means you can run more experiments.
Depending on the task, spaCy is between 10 and 200 times faster than NLTK,
often with much better accuracy. See Benchmarks for details, and
Why is spaCy so fast? for a discussion of the algorithms and implementation
that makes this possible.
+---------+----------+-------------+----------+
| System | Tokenize | --> Counts | --> Stem |
+---------+----------+-------------+----------+
| spaCy | 1m42s | 1m59s | 1m59s |
+---------+----------+-------------+----------+
| NLTK | 20m2s | 28m24s | 52m28 |
+---------+----------+-------------+----------+
Times for 100m words of text.
Unique Lexicon-centric design
@ -25,15 +38,14 @@ Unique Lexicon-centric design
spaCy helps you build models that generalise better, by making it easy to use
more robust features. Instead of a list of strings, the tokenizer returns
references to rich lexical types. Its tokenizer returns sequence of references
to rich lexical types. Features which ask about the word's Brown cluster, its
typical part-of-speech tag, how it's usually cased etc require no extra effort:
references to rich lexical types. Features which ask about the word's Brown cluster,
its typical part-of-speech tag, how it's usually cased etc require no extra effort:
>>> from spacy.en import EN
>>> from spacy.feature_names import *
>>> feats = (
SIC, # ID of the original word form
NORM, # ID of the normalized word form
STEM, # ID of the stemmed word form
CLUSTER, # ID of the word's Brown cluster
IS_TITLE, # Was the word title-cased?
POS_TYPE # A cluster ID describing what POS tags the word is usually assigned
@ -113,14 +125,6 @@ all to the special tokenization rules.
spaCy's tokenizer is also incredibly efficient:
+--------+---------------+--------------+
| System | Tokens/second | Speed Factor |
+--------+---------------+--------------+
| NLTK | 89 000 | 1.00 |
+--------+---------------+--------------+
| spaCy | 3 093 000 | 38.30 |
+--------+---------------+--------------+
spaCy can create an inverted index of the 1.8 billion word Gigaword corpus,
in under half an hour --- on a Macbook Air. See the `inverted
index tutorial`_.