自然语言处理（NLP）——分词统计itertools.chain—nltk工具 _语言

别裁伪体亲风雅，转益多师是汝师。这篇文章主要讲述自然语言处理（NLP）——分词统计itertools.chain—nltk工具相关的知识，希望能为你提供帮助。

文章目录

??一、itertools.chain( *[ ] )??
??二、NLTK工具：条件频率分布、正则表达式、词干提取器和归并器。??

??2.1 nltk 分句—分词??

??Sentences Segment（分句）??
??Tokenize sentences （分词）??

??2.2 nltk提供了两种常用的接口：`FreqDist` 和 `ConditionalFreqDist`??

??`FreqDist` 使用??
??`ConditionalFreqDist` 使用??

??2.3 正则表达式及其应用??
??2.4 词干提取器和归并器??

??利用词干提取器实现索引文本(concordance)??

一、itertools.chain( *[ ] )

import itertools

a= itertools.chain([a,aa,aaa])
b= itertools.chain(*[a,aa,aaa])
print(list(a))
print(list(b))

输出：
[‘a’, ‘aa’, ‘aaa’]
[‘a’, ‘a’, ‘a’, ‘a’, ‘a’, ‘a’]

二、NLTK工具：条件频率分布、正则表达式、词干提取器和归并器。
2.1 nltk 分句—分词

??nltk.sent_tokenize(text)?? #对文本按照句子进行分割

??nltk.word_tokenize(sent)?? #对句子进行分词
NLTK进行词性标注 ??nltk.pos_tag(tokens)?? #tokens是句子分词后的结果，同样是句子级的标注
NLTK进行命名实体识别（NER） ??nltk.ne_chunk(tags)?? #tags是句子词性标注后的结果，同样是句子级

Sentences Segment（分句）

sent_tokenizer = nltk.data.load(tokenizers/punkt/english.pickle)

paragraph = "The first time I heard that song was in Hawaii on radio.
I was just a kid, and loved it very much! What a fantastic song!"

print(sent_tokenizer.tokenize(paragraph))
输出：
[The first time I heard that song was in Hawaii on radio.,
I was just a kid, and loved it very much!,
What a fantastic song!]

Tokenize sentences （分词）

from nltk.tokenize import WordPunctTokenizer

sentence = "Are you old enough to remember Michael Jackson attending
the Grammys with Brooke Shields and Webster sat on his lap during the show?"

print(WordPunctTokenizer().tokenize(sentence))
输出：
[Are, you, old, enough, to, remember, Michael, Jackson, attending,
the, Grammys, with, Brooke, Shields, and, Webster, sat, on, his,
lap, during, the, show, ?]

----------------------------------------------------

text = That U.S.A. poster-print costs $12.40...

pattern = r"""(?x)# set flag to allow verbose regexps
(?:[A-Z]\\.)+# abbreviations, e.g. U.S.A.
|\\d+(?:\\.\\d+)?%?# numbers, incl. currency and percentages
|\\w+(?:[-]\\w+)*# words w/ optional internal hyphens/apostrophe
|\\.\\.\\.# ellipsis
|(?:[.,; "?():-_`])# special characters with meanings
"""

nltk.regexp_tokenize(text, pattern)
[That, U.S.A., poster-print, costs, 12.40, ...]

2.2 nltk提供了两种常用的接口：??FreqDist??? 和 ??ConditionalFreqDist?? ??FreqDist?? 使用

from nltk import *
import matplotlib.pyplot as plt

tem = [hello,world,hello,dear]
print(FreqDist(tem))

输出:
FreqDist(dear: 1, hello: 2, world: 1)

通过 plot(TopK,cumulative=True) 和 tabulate()

??ConditionalFreqDist?? 使用以一个配对链表作为输入，需要给分配的每个事件关联一个条件，
输入时类似于 (条件,事件) 的元组。

import nltk
from nltk.corpus import brown

cfd = nltk.ConditionalFreqDist((genre,word) \\
for genre in brown.categories()\\
for word in brown.words(categories=genre))
print("conditions are:",cfd.conditions()) #查看conditions
print(cfd[news])
print(cfd[news][could])#类似字典查询

输出:
conditions are: [adventure, belles_lettres, editorial, fiction,
government, hobbies, humor, learned, lore, mystery,
news, religion, reviews, romance, science_fiction]
< FreqDist with 14394 samples and 100554 outcomes>
86

"""
尤其对于plot() 和 tabulate() 有了更多参数选择：
conditions：指定条件
samples：迭代器类型，指定取值范围
cumulative：设置为True可以查看累积值

"""

cfd.tabulate(conditions=[news,romance],samples=[could,can])
cfd.tabulate(conditions=[news,romance],samples=[could,can],cumulative=True)
输出：
couldcan
news8693
romance19374

couldcan
news86179
romance193267

2.3 正则表达式及其应用输入法联想提示（9宫格输入法）

import re
from nltk.corpus import words

#查找类似于hole和golf序列（4653）的单词。
wordlist = [w for w in words.words(en-basic) if w.islower()]
same = [w for w in wordlist if re.search(r^[ghi][mno][jlk][def]$,w)]
print(same)

寻找字符块 —查找两个或两个以上的元音序列，并且确定相对频率。

import nltk

wsj = sorted(set(nltk.corpus.treebank.words()))
fd = nltk.FreqDist(vs for word in wsj for vs in re.findall(r[aeiou]2,,word))
fd.items()

查找词干—apples和apple对比中，apple就是词干。写一个简单脚本来查询词干。

def stem(word):
for suffix in [ing,ly,ed,ious,ies,ive,es,s,ment]:
if word.endswith(suffix):
return word[:-len(suffix)]
return None

或者使用正则表达式，只需要一行：
re.findall(r^(.*?)(ing|ly|ed|ious|ies|ive|es|s|ment)$,word)

2.4 词干提取器和归并器nltk提供了??PorterStemmer??? 和 ??LancasterStemmer??? 两个词干提取器，
Porter比较好，可以处理lying这样的单词。

porter = nltk.PorterStemmer()
print(porter.stem(lying))
---------------------------------------
词性归并器：WordNetLemmatizer

wnl = nltk.WordNetLemmatizer()
print(wnl.lemmatize(women))

利用词干提取器实现索引文本(concordance)用到nltk.Index这个函数:??nltk.Index((word , i) for (i,word) in enumerate([a,b,a]))??

class IndexText:
def __init__(self,stemmer,text):
self._text = text
self._stemmer = stemmer
self._index = nltk.Index((self._stem(word),i) for (i,word) in enumerate(text))
def _stem(self,word):
return self._stemmer.stem(word).lower()
def concordance(self,word,width =40):
key = self._stem(word)
wc = width/4 #words of context
for i in self._index[key]:
lcontext =.join(self._text[int(i-wc):int(i)])
rcontext =.join(self._text[int(i):int(i+wc)])
ldisplay = %*s % (width,lcontext[-width:])
rdisplay = %-*s % (width,rcontext[:width])
print(ldisplay,rdisplay)

porter = nltk.PorterStemmer()#词干提取
grail = nltk.corpus.webtext.words(grail.txt)
text = IndexText(porter,grail)
text.concordance(lie)