spaCy使用

官方文档
https://spacy.io/usage

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。

加载模型

# 导入工具包和英文模型
# python -m spacy download en 用管理员身份打开CMD

import spacy
nlp = spacy.load('en')

  

 

  
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文本处理

doc = nlp('Weather is good, very windy and sunny. We have no classes in the afternoon.')
# 分词
for token in doc: print (token)
OUT:
 Weather
is
good
,
very
windy
and
sunny
.
We
have
no
classes
in
the
afternoon
---------------------------------
#分句
for sent in doc.sents: print (sent)
OUT：
Weather is good, very windy and sunny.
We have no classes in the afternoon. 
  

 

  
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词性 参考 https://www.winwaed.com/blog/2011/11/08/part-of-speech-tags/

for token in doc: print ('{}-{}'.format(token,token.pos_))
OUT:
Weather-PROPN
is-VERB
good-ADJ
,-PUNCT
very-ADV
windy-ADJ
and-CCONJ
sunny-ADJ
.-PUNCT
We-PRON
have-VERB
no-DET
classes-NOUN
in-ADP
the-DET
afternoon-NOUN
.-PUNCT 

  

 

  
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命名体识别

doc_2 = nlp("I went to Paris where I met my old friend Jack from uni.")
for ent in doc_2.ents: print ('{}-{}'.format(ent,ent.label_))
OUT:
Paris-GPE
Jack-PERSON 
----
from spacy import displacy
doc = nlp('I went to Paris where I met my old friend Jack from uni.')
displacy.render(doc,style='ent',jupyter=True)

  

 

  
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练习 ： 找到书中所有人物名字

def read_file(file_name): with open(file_name, 'r') as file: return file.read()
# 加载文本数据
text = read_file('./data/pride_and_prejudice.txt')
processed_text = nlp(text)
sentences = [s for s in processed_text.sents]
print (len(sentences))
OUT:
6469

  

 

  
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一共有6469个句子

from collections import Counter,defaultdict
def find_person(doc): c = Counter() for ent in processed_text.ents: if ent.label_ == 'PERSON': c[ent.lemma_]+=1 return c.most_common(10)
print (find_person(processed_text))
OUT:
[('elizabeth', 604), ('darcy', 276), ('jane', 274), ('bennet', 233), ('bingley', 189), ('collins', 179), ('wickham', 170), ('gardiner', 95), ('lizzy', 94), ('lady catherine', 77)]

  

 

  
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搞定

文章来源: maoli.blog.csdn.net，作者：刘润森！，版权归原作者所有，如需转载，请联系作者。

原文链接：maoli.blog.csdn.net/article/details/88931036