《Python自然语言处理》答案第一、二章

12/(4+1)

26**100

len(text2)
len(set(text2))

len(list(nltk.bigrams(text5)))

[w for w in sorted(text5) if w.startswith('b')]

 def find_word(text,word):
   ...:     pos=0
   ...:     while pos<len(text):
   ...:         try:
   ...:             pos=text.index(word,pos)+1
   ...:             print(pos)
   ...:         except Exception as e:
   ...:             print('all have bean found!')
   ...:             return
   ...:
find_word(list(text9),'sunset')

fd=FreqDist(text5)
[w for (w,_) in fd.most_common() if len(w)==4]

[w for w in text6 if w.isupper()]

[w for w in list(text6) if w.endswith('ize') and w.find('pt')!=-1 and w[0].isupper() and w[1:].islower()]

[w for w in sent if w .startswith('sh')]
[w for w in sent if len(w)>4]

def percent(word,text):
    fd=FreqDist(text)
    return '{}%'.format((fd[word])*100/len(text))

persusion==nltk.Text(nltk.corpus.gutenberg.words('austen-persuasion.txt'))
len(persusion)
len(set(persusion))

cfd=ConditionalFreqDist((target,fileid[:4]) for fileid in state_union.fileids() for word in
 state_union.words(fileid) for target in ['men','women','people'] if target == word.lower()
)                                                                                          

male_names=names.words('male.txt')
female_names=names.words('female.txt')
fd_male=nltk.FreqDist(male_names)
fd_female=nltk.FreqDist(female_names)
cfd=nltk.ConditionalFreqDist((fd_male[name],name[0]) 
for fileid in names.fileids() 
for name in names.words(fileid) 
    if fd_male[name]>fd_female[name])

len(set(w for (w,p) in cmudict.entries()))
fd=FreqDist([len(pron) for (word,pron) in cmudict.entries()])
fd.most_common()[0][1]/len(cmudict.entries())

fd=FreqDist(brown.words())
[w for (w,_) in fd.most_common() if fd[w]>3]

 def word_diversity(words):
    ...:     return len(words)/len(set(words))
for category in brown.categories():
    ...:     diversity=word_diversity(brown.words(categories=category))
    ...:     print('%s\t%.2f'%(category,diversity))

def fun(text):                                                                    
    fd=FreqDist([w.lower() for w in text if w not in stopwords.words('english')]) 
    return [w for (w,_) in fd.most_common()[:50]]                                 

 def fun(text):
    ...:     fd=FreqDist([(w1,w2) for (w1,w2) in bigrams(text) if w1 not in stopwords.words('english') and w2 not in stopwords.words('english')])
    ...:     return [w for w in fd.most_common()[:50]]

def word_freq(text,word):
    ...:     count=nltk.Text(text).count(word)
    ...:     return count/len(text)