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关于python:NLTK简单入门和数据清洗

jiezi

NLTK 历史悠久的英文分词工具 

# 导入分词模块
from nltk.tokenize import word_tokenize
from nltk.text import Text

input='''There were a sensitivity and a beauty to her that have nothing to do with looks. She was one to be listened to, whose words were so easy to take to heart.'''
tokens=word_tokenize(input)
# 打印前 5 个词
print(tokens[:5])
# 将单词对立转换成小写 There 和 there 应该算同一个词
tokens=[w.lower() for w in tokens]

# 创立一个 Text 对象
t=Text(tokens)

# 统计某个词的呈现的次数
t.count('beauty')

# 计算某个词呈现的地位

t.index('beauty')

# 呈现最多的前 8 个词画一个图
# 须要装置 matplotlib pip install matplotlib
t.plot(8)

['There', 'were', 'a', 'sensitivity', 'and']

停用词 

from nltk.corpus import stopwords

# 打印出所有的停用词反对的语言，咱们应用 english

stopwords.fileids()
['arabic',
 'azerbaijani',
 'danish',
 'dutch',
 'english',
 'finnish',
 'french',
 'german',
 'greek',
 'hungarian',
 'indonesian',
 'italian',
 'kazakh',
 'nepali',
 'norwegian',
 'portuguese',
 'romanian',
 'russian',
 'spanish',
 'swedish',
 'turkish']




# 打印所有的停用词
stopwords.raw('english').replace('\n',' ')
"i me my myself we our ours ourselves you you're you've you'll you'd your yours yourself yourselves he him his himself she she's her hers herself it it's its itself they them their theirs themselves what which who whom this that that'll these those am is are was were be been being have has had having do does did doing a an the and but if or because as until while of at by for with about against between into through during before after above below to from up down in out on off over under again further then once here there when where why how all any both each few more most other some such no nor not only own same so than too very s t can will just don don't should should've now d ll m o re ve y ain aren aren't couldn couldn't didn didn't doesn doesn't hadn hadn't hasn hasn't haven haven't isn isn't ma mightn mightn't mustn mustn't needn needn't shan shan't shouldn shouldn't wasn wasn't weren weren't won won't wouldn wouldn't"




# 过滤停用词

tokens=set(tokens)

filtered=[w for w in tokens if(w not in stopwords.words('english'))]

print(filtered)
['nothing', 'sensitivity', ',', 'one', 'beauty', 'words', 'heart', 'looks', 'take', 'whose', '.', 'listened', 'easy']

词性标注 

# 第一次须要下载相应的组件 nltk.download()
from nltk import pos_tag
pos_tag(filtered)
[('nothing', 'NN'),
 ('sensitivity', 'NN'),
 (',', ','),
 ('one', 'CD'),
 ('beauty', 'NN'),
 ('words', 'NNS'),
 ('heart', 'NN'),
 ('looks', 'VBZ'),
 ('take', 'VB'),
 ('whose', 'WP$'),
 ('.', '.'),
 ('listened', 'VBN'),
 ('easy', 'JJ')]
POS Tag 指代
CC 并列连词
CD 基数词
DT 限定符
EX 存在词
FW 外来词
IN 介词或隶属连词
JJ 形容词
JJR 比较级的形容词
JJS 最高级的形容词
LS 列表项标记
MD 情态动词
NN 名词复数
NNS 名词复数
NNP 专有名词
PDT 前置限定词
POS 所有格结尾
PRP 人称代词
PRP$ 所有格代词
RB 副词
RBR 副词比较级
RBS 副词最高级
RP 小品词
UH 感叹词
VB 动词原型
VBD 动词过来式
VBG 动名词或当初分词
VBN 动词过去分词
VBP 非第三人称复数的当初时
VBZ 第三人称复数的当初时
WDT 以 wh 结尾的限定词

分块 

from nltk.chunk import RegexpParser
sentence = [('the','DT'),('little','JJ'),('yellow','JJ'),('dog','NN'),('died','VBD')]
grammer = "MY_NP: {<DT>?<JJ>*<NN>}"
cp = nltk.RegexpParser(grammer) #生成规定
result = cp.parse(sentence) #进行分块
print(result)

result.draw() #调用 matplotlib 库画进去 
(S (MY_NP the/DT little/JJ yellow/JJ dog/NN) died/VBD)



An exception has occurred, use %tb to see the full traceback.


SystemExit: 0

命名实体辨认 

# 第一次须要下载相应的组件 nltk.download()
from nltk import ne_chunk

input = "Edison went to Tsinghua University today."

print(ne_chunk(pos_tag(word_tokenize(input))))
showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml
(S
  (PERSON Edison/NNP)
  went/VBD
  to/TO
  (ORGANIZATION Tsinghua/NNP University/NNP)
  today/NN
  ./.)

数据荡涤 

import re
from nltk.corpus import stopwords
# 输出数据
s = 'RT @Amila #Test\nTom\'s newly listed Co  &amp; Mary\'s unlisted     Group to supply tech for nlTK.\nh $TSLA $AAPL https:// t.co/x34afsfQsh'

# 去掉 html 标签
s=re.sub(r'&\w*;|@\w*|#\w*','',s)

# 去掉一些价值符号
s=re.sub(r'\$\w*','',s)

# 去掉超链接
s=re.sub(r'https?:\/\/.*\/\w*','',s)

# 去掉一些专有名词 \b 为单词的边界
s=re.sub(r'\b\w{1,2}\b','',s)

# 去掉多余的空格
s=re.sub(r'\s\s+','',s)

# 分词
tokens=word_tokenize(s)

# 去掉停用词
tokens=[w for w in tokens if(w not in stopwords.words('english'))]

# 最初的后果
print(' '.join(tokens))

Tom 'newly listedMary' unlistedGroupsupply tech nlTK .

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