Getting a Large List of Nouns (or Adjectives) in Python with NLTK; or Python Mad Libs

machine-learning, nltk, python

Solution

It's worth noting that Wordnet is actually one of the corpora included in the NLTK downloader by default. So you could conceivably just use the solution you already found without having to reinvent any wheels.

For instance, you could just do something like this to get all noun synsets:

from nltk.corpus import wordnet as wn

for synset in list(wn.all_synsets('n')):
    print synset

# Or, equivalently
for synset in list(wn.all_synsets(wn.NOUN)):
    print synset

That example will give you every noun that you want and it will even group them into their synsets so you can try to be sure that they're being used in the correct context.

If you want to get them all into a list you can do something like the following (though this will vary quite a bit based on how you want to use the words and synsets):

all_nouns = []
for synset in wn.all_synsets('n'):
    all_nouns.extend(synset.lemma_names())

Or as a one-liner:

all_nouns = [word for synset in wn.all_synsets('n') for word in synset.lemma_names()]

Problem

Like this question, I am interested in getting a large list of words by part of speech (a long list of nouns; a list of adjectives) to be used programmatically elsewhere. This answer has a solution using the WordNet database (in SQL) format. Is there a way to get at such list using the corpora/tools built into the Python NLTK. I could take a large bunch of text, parse it and then store the nouns and adjectives. But given the dictionaries and other tools built in, is there a smarter way to simply extract the words that are already present in the NLTK datasets, encoded as nouns/adjectives (whatever)? Thanks.

Original source