声明：转载请注明出处，谢谢：https://www.jianshu.com/p/904c1833c2fe
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知乎：https://www.zhihu.com/people/yuquanle/columns
公众号：StudyForAI
CSDN地址：http://blog.csdn.net/m0_37306360

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jieba词性标注（part of specch)

安装：pip install jieba

国内源安装更快：pip install jieba -i https://pypi.tuna.tsinghua.edu.cn/simple

先导包：jieba.posseg.dt 为默认词性标注分词器

标注句子分词后每个词的词性，采用和 ictclas 兼容的标记法。

jieba貌似不能处理英文，后面会介绍处理英文的

import jieba.posseg as pseg
words = pseg.cut("我爱自然语言处理技术！")
for word, pos in words:
    print(word, pos)

我 r
爱 v
自然语言 l
处理 v
技术 n
！ x

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SnowNLP词性标注

安装：pip install snownlp

国内源安装：pip install snownlp -i https://pypi.tuna.tsinghua.edu.cn/simple

使用snownlp进行词性标注

from snownlp import SnowNLP
model = SnowNLP(u'我爱自然语言处理技术！')
for word, pos in model.tags:
    print(word, pos)

我 r
爱 v
自然 n
语言 n
处理 vn
技术 n
！ w

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THULAC词性标注

安装：pip install thulac

国内源安装：pip install thulac -i https://pypi.tuna.tsinghua.edu.cn/simple

使用thulac进行词性标注

import thulac
thulac_model = thulac.thulac()
wordseg = thulac_model.cut("我爱自然语言处理技术！")
print(wordseg)

Model loaded succeed
[['我', 'r'], ['爱', 'v'], ['自然', 'n'], ['语言', 'n'], ['处理', 'v'], ['技术', 'n'], ['！', 'w']]

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Stanford CoreNLP分词

安装：pip install stanfordcorenlp

国内源安装：pip install stanfordcorenlp -i https://pypi.tuna.tsinghua.edu.cn/simple

使用stanfordcorenlp进行词性标注

同时支持英文和中文的词性标注

from stanfordcorenlp import StanfordCoreNLP
zh_model = StanfordCoreNLP(r'stanford-corenlp-full-2018-02-27', lang='zh')
s_zh = '我爱自然语言处理技术！'
word_pos_zh = zh_model.pos_tag(s_zh)
print(word_pos_zh)

[('我爱', 'NN'), ('自然', 'AD'), ('语言', 'NN'), ('处理', 'VV'), ('技术', 'NN'), ('！', 'PU')]

eng_model = StanfordCoreNLP(r'stanford-corenlp-full-2018-02-27')
s_eng = 'I love natural language processing technology!'
word_pos_eng = eng_model.pos_tag(s_eng)
print(word_pos_eng)

[('I', 'PRP'), ('love', 'VBP'), ('natural', 'JJ'), ('language', 'NN'), ('processing', 'NN'), ('technology', 'NN'), ('!', '.')]

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Hanlp词性标注

安装：pip install pyhanlp

国内源安装：pip install pyhanlp -i https://pypi.tuna.tsinghua.edu.cn/simple

使用pyhanlp进行词性标注

from pyhanlp import *
s = '我爱自然语言处理技术！'
word_seg = HanLP.segment(s)
for term in word_seg:
    print(term.word, term.nature)

我 rr
爱 v
自然语言处理 nz
技术 n
！ w

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NLTK词性标注

安装：pip install nltk

国内源安装：pip install nltk -i https://pypi.tuna.tsinghua.edu.cn/simple

nltk只能处理英文

import nltk
s = 'I love natural language processing technology!'
s = nltk.word_tokenize(s)
s_pos = nltk.pos_tag(s)
print(s_pos)

[('I', 'PRP'), ('love', 'VBP'), ('natural', 'JJ'), ('language', 'NN'), ('processing', 'NN'), ('technology', 'NN'), ('!', '.')]

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spaCy词性标注

安装：pip install spaCy

国内源安装：pip install spaCy -i https://pypi.tuna.tsinghua.edu.cn/simple

下载不了模型，需要python -m spacy download en。
The easiest solution is to re-run the command as admin(意思是用用户管理权限打开CMD下载即可)

import spacy 

eng_model = spacy.load('en')
s = 'I love natural language processing technology!'

词性标注

s_token = eng_model(s)
for token in s_token:
    print(token, token.pos_, token.pos)

I PRON 94
love VERB 99
natural ADJ 83
language NOUN 91
processing NOUN 91
technology NOUN 91
! PUNCT 96