学习Python自然语言处理，记录一下学习笔记。

运用Python进行自然语言处理需要用到nltk库，关于nltk库的安装，我使用的pip方式。

pip nltk

或者下载whl文件进行安装。(推荐pip方式，简单又适用)。

安装完成后就可以使用该库了，但是还需要下载学习所需要的数据。启动ipython，键入下面两行代码：

>>>importnltk>>>nltk.download()

就会出现下面的一个界面：

选择book，选择好文件夹，(我选择的是E:\nltk_data)。下载数据。

下载完成后，可以验证一下下载成功与否：

>>> 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

如果出现上面的文本，说明下载数据成功。

在进行下面的操作之前，一定要保证先导入数据(from nltk.book import *)

prac1：搜索文本：

1.concordance('要搜索的文本')

>>>text1.concordance('monstrous')

Displaying11 of 11matches:

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 andspears . Some were thick

d as you gazed ,and wondered what monstrous cannibal andsavage could ever hav

that has survived the flood ; most monstrousandmost mountainous ! That Himmal

they might scout at Moby Dick as a monstrous fable ,or still worse andmore 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 thereisno telling . But

of Whale- Bones ; for Whales of a monstrous size are oftentimes cast up dead u

2.similar('文本')：搜索那些词出现在相似的上下文中：

>>>text1.similar('monstrous')

exasperate imperial gamesome candid subtly contemptible lazy part

pitiable delightfully domineering puzzled determined vexatious

modifies fearless christian horrible mouldy doleful>>>text2.similar('monstrous')

very heartily so exceedingly a extremely good great remarkably

amazingly sweet as vast

可以看出text1和text2在使用monstrous这个词上在表达意思上完全不同，在text2中，monstrous有正面的意思。

3.common_contexts(['word1','word2'...]):研究共用2个或者2个以上的词汇的上下文。

>>>text2.common_contexts(['monstrous','very'])

a_lucky be_glad am_glad a_pretty is_pretty

4.dispersion_plot():位置信息离散图。每一列代表一个单词，每一行代表整个文本。(ps：该函数需要依赖numpy和matplotlib库)

>>>text4.dispersion_plot(['citizens','democracy','freedom','duties','America'])

piac2:计数词汇：

计数词汇主要函数为len(),sorted():用于排序,set()：用于得到唯一的词汇，去除重复。这些函数的用法和Python中一样，不做重复。

piac3：简单的统计：

该部分中很多函数都不在适用于python3，有的用法需要自己改进，有的则完全不可用

1.频率分布：FreqDist(文本)：统计文本中每个标识符出现的频率。该函数在Python3上使用需要改进。

例如我们在text1《白鲸记》中统计最常出现的50个词：

原始版本：

>>>fdist1=FreqDist(text1)>>>vocabulary1=fdist1.keys()>>>vocabulary[:50]

但是在Python3中却行不通了。我们需要自己对其进行排序;

>>>fdist1=FreqDist(text1)>>>len(fdist1)19317

>>>vocabulary1=sorted(fd.items(),key=lambda jj:jj[1],reverse=True)>>>s=[]>>>for i inrange(len(vocabulary1)):

s.append(vocabulary1[i][0])>>>print(s)

[',', 'the', '.', 'of', 'and', 'a', 'to', ';', 'in', 'that', "'", '-', 'his', 'it', 'I', 's', 'is', 'he', 'with', 'was','as', '"', 'all', 'for', 'this', '!', 'at', 'by', 'but', 'not', '--', 'him', 'from', 'be', 'on', 'so', 'whale', 'one','you', 'had', 'have', 'there', 'But', 'or', 'were', 'now', 'which', '?', 'me', 'like']

在本书中，凡是涉及到FreqDist的都需要对其进行改进操作。

2.细粒度选择词：这里需要用到Python的列表推导式。

例如：选择text1中长度大于15的单词：

>>>V=sorted([w for w in set(text1) if len(w)>15])

['CIRCUMNAVIGATION','Physiognomically','apprehensiveness','cannibalistically','characteristically','circumnavigating','circumnavigation','circumnavigations','comprehensiveness','hermaphroditical','indiscriminately','indispensableness','irresistibleness','physiognomically','preternaturalness','responsibilities','simultaneousness','subterraneousness','supernaturalness','superstitiousness','uncomfortableness','uncompromisedness','undiscriminating','uninterpenetratingly']

判断的条件还有：s.startwith(t)、s.endwith(t)、t in s、s.islower()、s.isupper()、s.isalpha():都是字母、s.isalnum():字母和数字、s.isdigit()、s.istitle()

3.词语搭配和双连词：collocations()函数可以帮助我们完成这一任务。

如查看text4中的搭配：

>>>text4.collocations()

United States; fellow citizens; four years; years ago; Federal

Government; General Government; American people; Vice President; Old

World; Almighty God; Fellow citizens; Chief Magistrate; Chief Justice;

God bless; every citizen; Indian tribes; public debt; one another;

foreign nations; political parties

本书的第一章中还有一个babelize_shell()翻译函数，键入后会出现下面错误：

NameError: name 'babelize_shell' is not defined

原因是因为该模块已经不再可用了。

利用Python的条件分支和循环就可以简单的来处理一些文本信息。