import nltk
s='They wind back the clock,while we chase after the wind'
s1=nltk.word_tokenize(s)
s2=nltk.pos_tag(s1) #list型別

d={'colorless':'ADJ','idea':'N','sleep':'V'} #create a dict
del d['sleep'] #delete an item from dict d
d.update({'color':'N'}) #add new item into dict d
d2=nltk.defaultdict(list)
d2['N'].append('color') #add new word for type N

from nltk.book import *
text1.concordance('go')
text1.concordance('went')

from nltk.corpus import brown

brown_tagged_sents=brown.tagged_sents(categories='news')

brown_sents=brown.sents(categories='news')

unigram_tagger=nltk.UnigramTagger(brown_sents)

unigram_tagger.tag(brown_sents[2007])

unigram_tagger.evaluate(brown_tagged_sents)

import nltk

from nltk.corpus import brown

brown_sents=brown.sents(categories='news')

brown_tagged_sents=brown.tagged_sents(categories='news')

affixtagger=nltk.AffixTagger(brown_tagged_sents)

affistagger.tag(brown_sents[2007])

import nltk

from nltk.corpus import brown

brown_sents=brown.sents(categories='news')

brown_tagged_sents=brown.tagged_sents(categories='news')

bigram_tagger=nltk.BigramTagger(brown_tagged_sents)

bigram_tagger.tag(brown_sents[2007])

bigram_tagger.evaluate(brown_tagged_sents)

d={'year':2016,'month':8,'day':15}

print %s-%s-%s %( d['year'],d['month'],d['day'])

sorted(set(brown.word(categories='news')))