Python: NLTK几个入门函数

安装了

nltk

以及对应的一些英文语料之后,开始按照

python自然语言处理学习

来学习, 有几个入门级别的函数,记录如下:

text.concordance(word)

这个函数就是用来搜索单词

word

在

text

中出现多的情况,包括出现的那一行,重点强调上下文,实例如下:

In [1]: from nltk.book import *
*** Introductory Examples for the NLTK Book ***
Loading text1, ..., text9 and sent1, ..., sent9
Type the name of the text or sentence to view it.
Type: 'texts()' or 'sents()' to list the materials.
text1: Moby Dick by Herman Melville 1851
text2: Sense and Sensibility by Jane Austen 1811
text3: The Book of Genesis
text4: Inaugural Address Corpus
text5: Chat Corpus
text6: Monty Python and the Holy Grail
text7: Wall Street Journal
text8: Personals Corpus
text9: The Man Who Was Thursday by G . K . Chesterton 1908

In [2]: text1.concordance('monstrous')
Displaying 11 of 11 matches:
ong the former , one was of a most monstrous size . ... This came towards us ,
ON OF THE PSALMS . " Touching that monstrous bulk of the whale or ork we have r
ll over with a heathenish array of monstrous clubs and spears . Some were thick
d as you gazed , and wondered what monstrous cannibal and savage could ever hav
that has survived the flood ; most monstrous and most mountainous ! That Himmal
they might scout at Moby Dick as a monstrous fable , or still worse and more de
th of Radney .'" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l
ing Scenes . In connexion with the monstrous pictures of whales , I am strongly
ere to enter upon those still more monstrous stories of them which are to be fo
ght have been rummaged out of this monstrous cabinet there is no telling . But
of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u

从输出格式中可以看出来,

concordance

将要查询的单词,基本显示在一列,这样容易观察其上下文.

text.similar(word)

这个函数的作用则是根据

word

的上下文的单词的情况,来查找具有相似的上下文的单词. 比如monstrous 在上面可以看到,有这样的用法:

most monstrous size

the monstrous pictures

this monstrous cabinet

等等,

similar()

函数会在文本中 搜索具有类似结构的其他单词, 不过貌似这个函数只会考虑一些简单的指标,来作为相似度,比如上下文的词性,更多的完整匹配, 不会涉及到语义.

可以看看下面的例子:

In [3]: text1.similar('monstrous')
imperial subtly impalpable pitiable curious abundant perilous
trustworthy untoward singular lamentable few determined maddens
horrible tyrannical lazy mystifying christian exasperate

In [4]: text2.similar('monstrous')
very exceedingly so heartily a great good amazingly as sweet
remarkably extremely vast

In [5]: text3.similar('monstrous')
No matches#表示无匹配

这个可以看出的是,

text1

和

text2

对同一个单词

monstrous

的不同使用风格.

text.common_contexts([word1,word2…])

这个函数跟

simailar()

有点类似,也是在根据上下文搜索的.

不同的是,这个函数是用来搜索 共用 参数中的列表中的所有单词,的上下文.即: word1,word2 相同的上下文.看例子:

In [8]: text1.similar('monstrous')
imperial subtly impalpable pitiable curious abundant perilous
trustworthy untoward singular lamentable few determined maddens
horrible tyrannical lazy mystifying christian exasperate

In [9]: text1.common_contexts(['monstrous','subtly'])
most_and

In [10]: text1.common_contexts(['monstrous','imperial'])
most_and

In [11]: text1.common_contexts(['monstrous','few'])
the_pictures#表示:the monstrous pictures, 和 the few pictures 都存在与text1中. 上面同理.

text.dispersion_plot([word1, word2,])

这个函数是用离散图 表示 语料中word 出现的位置序列表示.

text1.dispersion_plot(['monstrous','few'])

效果如下:



其中横坐标表示文本的单词位置.纵坐标表示查询的单词, 坐标里面的就是,单词出现的位置.就是 单词的分布情况

基本函数

In [8]: len(text9)#统计单词量:以单词和标点符号为单位
Out[8]: 69213

In [9]: text9.count('the')#统计频数
Out[9]: 3291