Extracting all Nouns from a text file using nltk

If you are open to options other than NLTK, check out TextBlob. It extracts all nouns and noun phrases easily:

>>> from textblob import TextBlob
>>> txt = """Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the inter
actions between computers and human (natural) languages."""
>>> blob = TextBlob(txt)
>>> print(blob.noun_phrases)
[u'natural language processing', 'nlp', u'computer science', u'artificial intelligence', u'computational linguistics']

import nltk
lines = 'lines is some string of words'
tokenized = nltk.word_tokenize(lines)
nouns = [word for (word, pos) in nltk.pos_tag(tokenized) if(pos[:2] == 'NN')]
print (nouns)

Just simplied abit more.

You can achieve good results using nltk, Textblob, SpaCy or any of the many other libraries out there. These libraries will all do the job but with different degrees of efficiency.

import nltk
from textblob import TextBlob
import spacy
nlp = spacy.load('en')
nlp1 = spacy.load('en_core_web_lg')

txt = """Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages."""

On my windows 10 2 cores, 4 processors, 8GB ram i5 hp laptop, in jupyter notebook, I ran some comparisons and here are the results.

For TextBlob:

%%time
print([w for (w, pos) in TextBlob(txt).pos_tags if pos[0] == 'N'])

And the output is

>>> ['language', 'processing', 'NLP', 'field', 'computer', 'science', 'intelligence', 'linguistics', 'inter', 'actions', 'computers', 'languages']
    Wall time: 8.01 ms #average over 20 iterations

For nltk:

%%time
print([word for (word, pos) in nltk.pos_tag(nltk.word_tokenize(txt)) if pos[0] == 'N'])

And the output is

>>> ['language', 'processing', 'NLP', 'field', 'computer', 'science', 'intelligence', 'linguistics', 'inter', 'actions', 'computers', 'languages']
    Wall time: 7.09 ms #average over 20 iterations

For spacy:

%%time
print([ent.text for ent in nlp(txt) if ent.pos_ == 'NOUN'])

And the output is

>>> ['language', 'processing', 'field', 'computer', 'science', 'intelligence', 'linguistics', 'inter', 'actions', 'computers', 'languages']
    Wall time: 30.19 ms #average over 20 iterations

It seems nltk and TextBlob are reasonably faster and this is to be expected since store nothing else about the input text, txt. Spacy is way slower. One more thing. SpaCy missed the noun NLP while nltk and TextBlob got it. I would shot for nltk or TextBlob unless there is something else I wish to extract from the input txt.

Check out a quick start into spacy here.
Check out some basics about TextBlob here.
Check out nltk HowTos here

import nltk

lines = 'lines is some string of words'
# function to test if something is a noun
is_noun = lambda pos: pos[:2] == 'NN'
# do the nlp stuff
tokenized = nltk.word_tokenize(lines)
nouns = [word for (word, pos) in nltk.pos_tag(tokenized) if is_noun(pos)] 

print nouns
>>> ['lines', 'string', 'words']

Useful tip: it is often the case that list comprehensions are a faster method of building a list than adding elements to a list with the .insert() or append() method, within a 'for' loop.