Use Tagger (5.1)

Moving forward to Chapter 5 although Exercise of Chap 4 is still remaining.

>>> text = nltk.word_tokenize("And now for somthing completely different")
>>> nltk.pos_tag(text)
[('And', 'CC'), ('now', 'RB'), ('for', 'IN'), ('somthing', 'VBG'), ('completely', 'RB'), ('different', 'JJ')]
RB: adverb
    occasionally unabatingly maddeningly adventurously professedly
    stirringly prominently technologically magisterially predominately
    swiftly fiscally pitilessly ...

Another example;

>>> text = nltk.word_tokenize("They refuse to permit us to obtain the refuse permit")
>>> nltk.pos_tag(text)
[('They', 'PRP'), ('refuse', 'VBP'), ('to', 'TO'), ('permit', 'VB'), ('us', 'PRP'), ('to', 'TO'), ('obtain', 'VB'), ('the', 'DT'), ('refuse', 'NN'), ('permit', 'NN')]

So far I just understand different POS was displayed even for the same words like 'refuse' and 'permit'.

>>> text = nltk.Text(word.lower() for word in nltk.corpus.brown.words())
>>> text.similar('woman')
Building word-context index...
man day time year car moment world family house boy child country job
state girl place war way case question
>>> text.similar('bought')
made done put said found had seen given left heard been brought got
set was called felt in that told
>>> text.similar('over')
in on to of and for with from at by that into as up out down through
about all is
>>> text.similar('the')
a his this their its her an that our any all one these my in your no
some other and

text.similar() is to search words which are used at same situation.