自然语言处理（NLP）——分词统计itertools.chain—nltk工具
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文章目录
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 $12.40...
pattern = r"""(?x) # set flag to allow verbose regexps
(?:[A-Z]\\.)+ # abbreviations, e.g. U.S.A.
|\\d+(?:\\.\\d+)?%? # numbers, incl. currency and percentages
|\\w+(?:[-]\\w+)* # words w/ optional internal hyphens/apostrophe
|\\.\\.\\. # ellipsis
|(?:[.,;"?():-_`]) # special characters with meanings
"""
nltk.regexp_tokenize(text, pattern)
[That, U.S.A., poster-print, costs, 12.40, ...]
2.2 nltk提供了两种常用的接口：FreqDist 和 ConditionalFreqDist
FreqDist 使用
from nltk import *
import matplotlib.pyplot as plt
tem = [hello,world,hello,dear]
print(FreqDist(tem))
输出:
FreqDist(dear: 1, hello: 2, world: 1)
通过 plot(TopK,cumulative=True) 和 tabulate()
ConditionalFreqDist 使用
以一个配对链表作为输入，需要给分配的每个事件关联一个条件，
输入时类似于 (条件,事件) 的元组。
import nltk
from nltk.corpus import brown
cfd = nltk.ConditionalFreqDist((genre,word) \\
for genre in brown.categories()\\
for word in brown.words(categories=genre))
print("conditions are:",cfd.conditions()) #查看conditions
print(cfd[news])
print(cfd[news][could]) #类似字典查询
输出:
conditions are: [adventure, belles_lettres, editorial, fiction,
government, hobbies, humor, learned, lore, mystery,
news, religion, reviews, romance, science_fiction]
<FreqDist with 14394 samples and 100554 outcomes>
86
"""
尤其对于plot() 和 tabulate() 有了更多参数选择：
conditions：指定条件
samples： 迭代器类型，指定取值范围
cumulative：设置为True可以查看累积值
"""
cfd.tabulate(conditions=[news,romance],samples=[could,can])
cfd.tabulate(conditions=[news,romance],samples=[could,can],cumulative=True)
输出：
could can
news 86 93
romance 193 74
could can
news 86 179
romance 193 267
2.3 正则表达式及其应用
输入法联想提示（9宫格输入法）
import re
from nltk.corpus import words
#查找类似于hole和golf序列（4653）的单词。
wordlist = [w for w in words.words(en-basic) if w.islower()]
same = [w for w in wordlist if re.search(r^[ghi][mno][jlk][def]$,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)
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