In python try the following.
Below are examples of how to access your corpus
|fileids()||the files of the corpus|
|fileids([categories])||the files of the corpus corresponding to these categories|
|categories()||the categories of the corpus|
|categories([fileids])||the categories of the corpus corresponding to these files|
|raw()||the raw content of the corpus|
|raw(fileids=[f1,f2,f3])||the raw content of the specified files|
|raw(categories=[c1,c2])||the raw content of the specified categories|
|words()||the words of the whole corpus|
|words(fileids=[f1,f2,f3])||the words of the specified fileids|
|words(categories=[c1,c2])||the words of the specified categories|
|sents()||the sentences of the whole corpus|
|sents(fileids=[f1,f2,f3])||the sentences of the specified fileids|
|sents(categories=[c1,c2])||the sentences of the specified categories|
|abspath(fileid)||the location of the given file on disk|
|encoding(fileid)||the encoding of the file (if known)|
|open(fileid)||open a stream for reading the given corpus file|
|root||if the path to the root of locally installed corpus|
|readme()||the contents of the README file of the corpus|
Answer the following:
- How many categories are in your corpus?
- How many sentences are in the corpus?
- How many sentences are in each category?
For instance for brown you can import it by
from nltk.corpus import brown brown.[function] brown.raw()
first: pip install matplotlib
from nltk.corpus import inaugural word1='country' word2='city' cfd = nltk.ConditionalFreqDist((target, fileid[:4])for fileid in inaugural.fileids()for w in inaugural.words(fileid)for target in [word1, word2] if w.lower().startswith(target)) cfd.plot()
Try finding two words to replace country and city. Find one word that is becoming more popular in recent years (2009) and one that was popular but is not longer.
NOTE: You only need to attach 1 page to answer these 2 problems in a Word.doc
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