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 *

776Men-1.1.0

774Men_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 *

424Mdeps

8.0Kmeta.json

35Mner

12Mpos

84Ktokenizer

300Mvocab

6.3Mwordnet

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.