Error when extract noun-phrases from the training corpus and remove stop words using NLTK

I am new to both, python and NLTK. I have to extract noun phrase from the corpus and then remove the stop words by using NLTK. I already do my coding but still have error. Can anyone help me to fix this problem? Or please also recommend if there is any better solution. Thank you

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords

docid='19509'
title='Example noun-phrase and stop words'
print('Document id:'),docid
print('Title:'),title

#list noun phrase
content='This is a sample sentence, showing off the stop words filtration.'
is_noun = lambda pos: pos[:2] == 'NN'
tokenized = nltk.word_tokenize(content)
nouns = [word for (word,pos) in nltk.pos_tag(tokenized) if is_noun(pos)]
print('All Noun Phrase:'),nouns

#remove stop words
stop_words = set(stopwords.words("english"))

example_words = word_tokenize(nouns)
filtered_sentence = []

for w in example_words:
  if w not in stop_words:
     filtered_sentence.append(w)

print('Without stop words:'),filtered_sentence

And I got the following error

Traceback (most recent call last):
 File "C:\Users\User\Desktop\NLP\stop_word.py", line 20, in <module>
  example_words = word_tokenize(nouns)
 File "C:\Python27\lib\site-packages\nltk\tokenize\__init__.py", line 109,in 
 word_tokenize
  return [token for sent in sent_tokenize(text, language)
 File "C:\Python27\lib\site-packages\nltk\tokenize\__init__.py", line 94, in 
 sent_tokenize
  return tokenizer.tokenize(text)
 File "C:\Python27\lib\site-packages\nltk\tokenize\punkt.py", line 1237, in 
 tokenize
  return list(self.sentences_from_text(text, realign_boundaries))
 File "C:\Python27\lib\site-packages\nltk\tokenize\punkt.py", line 1285, in 
 sentences_from_text
  return [text[s:e] for s, e in self.span_tokenize(text,realign_boundaries)]
 File "C:\Python27\lib\site-packages\nltk\tokenize\punkt.py", line 1276, in 
 span_tokenize
  return [(sl.start, sl.stop) for sl in slices]
 File "C:\Python27\lib\site-packages\nltk\tokenize\punkt.py", line 1316, in 
 _realign_boundaries
  for sl1, sl2 in _pair_iter(slices):
 File "C:\Python27\lib\site-packages\nltk\tokenize\punkt.py", line 310, in 
 _pair_iter
  prev = next(it)
 File "C:\Python27\lib\site-packages\nltk\tokenize\punkt.py", line 1289, in 
 _slices_from_text
  for match in self._lang_vars.period_context_re().finditer(text):
TypeError: expected string or buffer

Solution

You are getting this error because the function word_tokenize is expecting a string as an argument and you give a list of strings. As far as I understand what you are trying to achieve, you do not need tokenize at this point. Until the print('All Noun Phrase:'),nouns, you have all the nouns of your sentence. To remove the stopwords, you can use:

### remove stop words ###
stop_words = set(stopwords.words("english"))
# find the nouns that are not in the stopwords
nouns_without_stopwords = [noun for noun in nouns if noun not in stop_words]
# your sentence is now clear
print('Without stop words:',nouns_without_stopwords)

Of course, in this case you have the same result with nouns, because none of the nouns was a stopword.

I hope this helps.