NLTK库安装
pip install nltk
执行python并下载书籍:
[root@centos #] pythonPython 2.7.11 (default, Jan 22 , 08:29:18)[GCC 4.2.1 Compatible Apple LLVM 7.0.2 (clang-700.1.81)] on darwinType "help", "copyright", "credits" or "license" for more information.>>> import nltk>>> nltk.download()
选择book后点Download开始下载
下载完成以后再输入:
>>> from nltk.book import *
你会看到可以正常加载书籍如下:
*** Introductory Examples for the NLTK Book ***Loading text1, ..., text9 and sent1, ..., sent9Type the name of the text or sentence to view it.Type: 'texts()' or 'sents()' to list the materials.text1: Moby Dick by Herman Melville 1851text2: Sense and Sensibility by Jane Austen 1811text3: The Book of Genesistext4: Inaugural Address Corpustext5: Chat Corpustext6: Monty Python and the Holy Grailtext7: Wall Street Journaltext8: Personals Corpustext9: The Man Who Was Thursday by G . K . Chesterton 1908
这里面的text*都是一个一个的书籍节点,直接输入text1会输出书籍标题:
>>> text1<Text: Moby Dick by Herman Melville 1851>
搜索文本
执行
>>> text1.concordance("former")
会显示20个包含former的语句上下文
我们还可以搜索相关词,比如:
>>> text1.similar("ship")whale boat sea captain world way head time crew man other pequod linedeck body fishery air boats side voyage
输入了ship,查找了boat,都是近义词
我们还可以查看某个词在文章里出现的位置:
>>> text4.dispersion_plot(["citizens", "democracy", "freedom", "duties", "America"])
词统计
len(text1):返回总字数
set(text1):返回文本的所有词集合
len(set(text4)):返回文本总词数
text4.count("is"):返回“is”这个词出现的总次数
FreqDist(text1):统计文章的词频并按从大到小排序存到一个列表里
fdist1 = FreqDist(text1);fdist1.plot(50, cumulative=True):统计词频,并输出累计图像
纵轴表示累加了横轴里的词之后总词数是多少,这样看来,这些词加起来几乎达到了文章的总词数
fdist1.hapaxes():返回只出现一次的词
text4.collocations():频繁的双联词