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

Python

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