第一章解决的问题：
搜索文本
搜索相似词
搜索共同上下文
词汇分布图
自动生成文章
计数词汇
重复词密度
关键词密度
词链表
连接
追加
索引
切片索引从0开始，要注意
简单统计频率分布
细粒度的选择词
词语搭配
其他统计结果
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# -*- coding: utf-8 -*-
"""
Created on Wed Jul 27 13:50:43 2016
@author: Administrator
"""
import nltk
nltk.download()
from nltk.book import *
###搜索文本
#搜索单词
text1.concordance("monstrous")
text2.concordance("affection")
text3.concordance("lived")
text5.concordance("lol")
#搜索相似词
text1.similar("monstrous")
text2.similar("monstrous")
#搜索共同上下文
text2.common_contexts(["monstrous", "very"])
#词汇分布图
text4.dispersion_plot(["citizens", "democracy", "freedom", "duties", "America"])
#自动生成文章
#text1.generate()
###计数词汇
len(text3)
sorted(set(text3))
len(set(text3))
#重复词密度
from __future__ import division
len(text3) / len(set(text3))
#关键词密度
text3.count("smote")
100 * text4.count('a') / len(text4)
def lexical_diversity(text):
return len(text) / len(set(text))
def percentage(count, total):
return 100 * count / total
lexical_diversity(text3)
lexical_diversity(text5)
percentage(4, 5)
percentage(text4.count('a'), len(text4))
###词链表
sent1 = ['Call', 'me', 'Ishmael', '.']
sent1
len(sent1)
lexical_diversity(sent1)
print sent2
print sent3
#连接
sent4+sent1
#追加
sent1.append("some")
print sent1
#索引
text4[173]
text4.index('awaken')
#切片
print text5[16715:16735]
print text6[1600:1625]
#索引从0开始，要注意
sent = ['word1', 'word2', 'word3', 'word4', 'word5','word6', 'word7', 'word8', 'word9', 'word10']
print sent[0]
print sent[9]
print sent[10]
print sent[5:8]
print sent[5]
print sent[6]
print sent[7]
print sent[:3]
print text2[141525:]
sent[0] = 'First'
sent[9] = 'Last'
sent[1:9] = ['Second', 'Third']
print sent
sent[9]
###简单统计
#频率分布
fdist1 = FreqDist(text1)
fdist1
vocabulary1 = fdist1.keys()
vocabulary1[:50]
fdist1['whale']
fdist1.plot(50, cumulative=True)
fdist1.hapaxes()
#细粒度的选择词
V = set(text4)
long_words = [w for w in V if len(w) > 15]
sorted(long_words)
V = set(text5)
long_words = [w for w in V if len(w) > 15]
sorted(long_words)
fdist5 = FreqDist(text5)
sorted([w for w in set(text5) if len(w) > 7 and fdist5[w] > 7])
#词语搭配
from nltk.util import bigrams
list(bigrams(['more', 'is', 'said', 'than', 'done']))
text4.collocations()
text8.collocations()
###其他统计结果
[len(w) for w in text1]
fdist = FreqDist([len(w) for w in text1])
fdist
fdist.keys()
fdist.items()
fdist.max()
fdist[3]
fdist.freq(3)