spaCy 是一个Python自然语言处理工具包，诞生于2014年年中，号称“Industrial-Strength Natural Language Processing in Python”，是具有工业级强度的Python NLP工具包。spaCy里大量使用了 Cython 来提高相关模块的性能，这个区别于学术性质更浓的Python NLTK，因此具有了业界应用的实际价值。
安装和编译 spaCy 比较方便，在ubuntu环境下，直接用pip安装即可：
sudo apt-get install build-essential python-dev git
sudo pip install -U spacy
不过安装完毕之后，需要下载相关的模型数据，以英文模型数据为例，可以用"all"参数下载所有的数据:
sudo python -m spacy.en.download all
或者可以分别下载相关的模型和用glove训练好的词向量数据：
# 这个过程下载英文tokenizer，词性标注，句法分析，命名实体识别相关的模型
python -m spacy.en.download parser
# 这个过程下载glove训练好的词向量数据
python -m spacy.en.download glove
下载好的数据放在spacy安装目录下的data里，以我的ubuntu为例：
textminer@textminer:/usr/local/lib/python2.7/dist-packages/spacy/data$ du -sh *
776M en-1.1.0
774M en_glove_cc_300_1m_vectors-1.0.0
进入到英文数据模型下：
textminer@textminer:/usr/local/lib/python2.7/dist-packages/spacy/data/en-1.1.0$ du -sh *
424M deps
8.0K meta.json
35M ner
12M pos
84K tokenizer
300M vocab
6.3M wordnet
可以用如下命令检查模型数据是否安装成功：
textminer@textminer:~$ python -c "import spacy; spacy.load('en'); print('OK')"
OK
也可以用pytest进行测试：
# 首先找到spacy的安装路径：
python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
/usr/local/lib/python2.7/dist-packages/spacy
# 再安装pytest:
sudo python -m pip install -U pytest
# 最后进行测试：
python -m pytest /usr/local/lib/python2.7/dist-packages/spacy --vectors --model --slow
============================= test session starts ==============================
platform linux2 -- Python 2.7.12, pytest-3.0.4, py-1.4.31, pluggy-0.4.0
rootdir: /usr/local/lib/python2.7/dist-packages/spacy, inifile:
collected 318 items
../../usr/local/lib/python2.7/dist-packages/spacy/tests/test_matcher.py ........
../../usr/local/lib/python2.7/dist-packages/spacy/tests/matcher/test_entity_id.py ....
../../usr/local/lib/python2.7/dist-packages/spacy/tests/matcher/test_matcher_bugfixes.py .....
......
../../usr/local/lib/python2.7/dist-packages/spacy/tests/vocab/test_vocab.py .......Xx
../../usr/local/lib/python2.7/dist-packages/spacy/tests/website/test_api.py x...............
../../usr/local/lib/python2.7/dist-packages/spacy/tests/website/test_home.py ............
============== 310 passed, 5 xfailed, 3 xpassed in 53.95 seconds ===============
现在可以快速测试一下spaCy的相关功能，我们以英文数据为例，spaCy目前主要支持英文和德文，对其他语言的支持正在陆续加入：
textminer@textminer:~$ ipython
Python 2.7.12 (default, Jul 1 2016, 15:12:24)
Type "copyright", "credits" or "license" for more information.
IPython 2.4.1 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import spacy
# 加载英文模型数据，稍许等待
In [2]: nlp = spacy.load('en')
Word tokenize功能，spaCy 1.2版本加了中文tokenize接口，基于Jieba中文分词:
In [3]: test_doc = nlp(u"it's word tokenize test for spacy")
In [4]: print(test_doc)
it's word tokenize test for spacy
In [5]: for token in test_doc:
print(token)
...:
it
's
word
tokenize
test
for
spacy
英文断句:
In [6]: test_doc = nlp(u'Natural language processing (NLP) deals with the application of computational models to text or speech data. Application areas within NLP include automatic (machine) translation between languages; dialogue systems, which allow a human to interact with a machine using natural language; and information extraction, where the goal is to transform unstructured text into structured (database) representations that can be searched and browsed in flexible ways. NLP technologies are having a dramatic impact on the way people interact with computers, on the way people interact with each other through the use of language, and on the way people access the vast amount of linguistic data now in electronic form. From a scientific viewpoint, NLP involves fundamental questions of how to structure formal models (for example statistical models) of natural language phenomena, and of how to design algorithms that implement these models.')
In [7]: for sent in test_doc.sents:
print(sent)
...:
Natural language processing (NLP) deals with the application of computational models to text or speech data.
Application areas within NLP include automatic (machine) translation between languages; dialogue systems, which allow a human to interact with a machine using natural language; and information extraction, where the goal is to transform unstructured text into structured (database) representations that can be searched and browsed in flexible ways.
NLP technologies are having a dramatic impact on the way people interact with computers, on the way people interact with each other through the use of language, and on the way people access the vast amount of linguistic data now in electronic form.
From a scientific viewpoint, NLP involves fundamental questions of how to structure formal models (for example statistical models) of natural language phenomena, and of how to design algorithms that implement these models.
词干化（Lemmatize):
In [8]: test_doc = nlp(u"you are best. it is lemmatize test for spacy. I love these books")
In [9]: for token in test_doc:
print(token, token.lemma_, token.lemma)
...:
(you, u'you', 472)
(are, u'be', 488)
(best, u'good', 556)
(., u'.', 419)
(it, u'it', 473)
(is, u'be', 488)
(lemmatize, u'lemmatize', 1510296)
(test, u'test', 1351)
(for, u'for', 480)
(spacy, u'spacy', 173783)
(., u'.', 419)
(I, u'i', 570)
(love, u'love', 644)
(these, u'these', 642)
(books, u'book', 1011)
词性标注(POS Tagging):
In [10]: for token in test_doc:
print(token, token.pos_, token.pos)
....:
(you, u'PRON', 92)
(are, u'VERB', 97)
(best, u'ADJ', 82)
(., u'PUNCT', 94)
(it, u'PRON', 92)
(is, u'VERB', 97)
(lemmatize, u'ADJ', 82)
(test, u'NOUN', 89)
(for, u'ADP', 83)
(spacy, u'NOUN', 89)
(., u'PUNCT', 94)
(I, u'PRON', 92)
(love, u'VERB', 97)
(these, u'DET', 87)
(books, u'NOUN', 89)
命名实体识别（NER）：
In [11]: test_doc = nlp(u"Rami Eid is studying at Stony Brook University in New York")
In [12]: for ent in test_doc.ents:
print(ent, ent.label_, ent.label)
....:
(Rami Eid, u'PERSON', 346)
(Stony Brook University, u'ORG', 349)
(New York, u'GPE', 350)
名词短语提取：
In [13]: test_doc = nlp(u'Natural language processing (NLP) deals with the application of computational models to text or speech data. Application areas within NLP include automatic (machine) translation between languages; dialogue systems, which allow a human to interact with a machine using natural language; and information extraction, where the goal is to transform unstructured text into structured (database) representations that can be searched and browsed in flexible ways. NLP technologies are having a dramatic impact on the way people interact with computers, on the way people interact with each other through the use of language, and on the way people access the vast amount of linguistic data now in electronic form. From a scientific viewpoint, NLP involves fundamental questions of how to structure formal models (for example statistical models) of natural language phenomena, and of how to design algorithms that implement these models.')
In [14]: for np in test_doc.noun_chunks:
print(np)
....:
Natural language processing
Natural language processing (NLP) deals
the application
computational models
text
speech
data
Application areas
NLP
automatic (machine) translation
languages
dialogue systems
a human
a machine
natural language
information extraction
the goal
unstructured text
structured (database) representations
flexible ways
NLP technologies
a dramatic impact
the way
people
computers
the way
people
the use
language
the way
people
the vast amount
linguistic data
electronic form
a scientific viewpoint
NLP
fundamental questions
formal models
example
natural language phenomena
algorithms
these models
基于词向量计算两个单词的相似度：
In [15]: test_doc = nlp(u"Apples and oranges are similar. Boots and hippos aren't.")
In [16]: apples = test_doc[0]
In [17]: print(apples)
Apples
In [18]: oranges = test_doc[2]
In [19]: print(oranges)
oranges
In [20]: boots = test_doc[6]
In [21]: print(boots)
Boots
In [22]: hippos = test_doc[8]
In [23]: print(hippos)
hippos
In [24]: apples.similarity(oranges)
Out[24]: 0.77809414836023805
In [25]: boots.similarity(hippos)
Out[25]: 0.038474555379008429
当然，spaCy还包括句法分析的相关功能等。另外值得关注的是 spaCy 从1.0版本起，加入了对深度学习工具的支持，例如 Tensorflow 和 Keras 等，这方面具体可以参考官方文档给出的一个对情感分析（Sentiment Analysis）模型进行分析的例子：Hooking a deep learning model into spaCy.
参考：
spaCy官方文档
Getting Started with spaCy
注：原创文章，转载请注明出处及保留链接“我爱自然语言处理”：https://www.52nlp.cn