spaCy是一个NLP工具包用于完成NLP领域的很多任务比如词性标注，命名实体识别，依存句法分析，归一化，停用词，判断是否词语等，支持Unix/Linux,macOS/os X和Windows操作系统，可以通过pip,conda方式安装。
pip:
pip install -U spacy
conda:
conda install -c conda-forge spacy
| NAME | LANGUAGE | TYPE |
|---|---|---|
| en_core_web_sm | English | Vocabulary, syntax, entities |
| en_core_web_md | English | Vocabulary, syntax, entities, vectors |
| en_core_web_lg | English | Vocabulary, syntax, entities, vectors |
| en_vectors_web_lg | English | Word vectors |
| de_core_news_sm | German | Vocabulary, syntax, entities |
| es_core_news_sm | Spanish | Vocabulary, syntax, entities |
| es_core_news_md | Spanish | Vocabulary, syntax, entities, vectors |
| pt_core_news_sm | Portuguese | Vocabulary, syntax, entities |
| fr_core_news_sm | French | Vocabulary, syntax, entities |
| fr_core_news_md | French | Vocabulary, syntax, entities, vectors |
| it_core_news_sm | Italian | Vocabulary, syntax, entities |
| nl_core_news_sm | Dutch | Vocabulary, syntax, entities |
| xx_ent_wiki_sm | Multi-language | Named entities |
语言模型的安装：
#命令行
python -m spacy download xx #xx表示上面表格中的的NAME
#pip
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_md-1.2.0/en_core_web_md-1.2.0.tar.gz
#本地文件
pip install /Users/you/en_core_web_md-1.2.0.tar.gz
安装语言模型之后就可以在代码中使用了：
import spacy
nlp = spacy.load('en') # load model with shortcut link "en"
nlp = spacy.load('en_core_web_sm') # load model package "en_core_web_sm"
nlp = spacy.load('/path/to/en_core_web_sm') # load package from a directory
doc = nlp(u'This is a sentence.')
安装中如果遇到问题参考链接
词性标注：
doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
for token in doc:
print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
token.shape_, token.is_alpha, token.is_stop)
依存树：
doc = nlp(u'Autonomous cars shift insurance liability toward manufacturers')
for token in doc:
print(token.text, token.dep_, token.head.text, token.head.pos_,
[child for child in token.children])
doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
for ent in doc.ents:
print(ent.text, ent.start_char, ent.end_char, ent.label_)
标签化:
for token in doc:
print(token.text)