What to download in order to make nltk.tokenize.word_tokenize work?

Question

I am going to use nltk.tokenize.word_tokenize on a cluster where my account is very limited by space quota. At home, I downloaded all nltk resource

Answer 1

In short:

nltk.download('punkt')

would suffice.

---

In long:

You don't necessary need to download all the models and corpora available in NLTk if you're just going to use NLTK for tokenization.

Actually, if you're just using word_tokenize(), then you won't really need any of the resources from nltk.download(). If we look at the code, the default word_tokenize() that is basically the TreebankWordTokenizer shouldn't use any additional resources:

alvas@ubi:~$ ls nltk_data/
chunkers  corpora  grammars  help  models  stemmers  taggers  tokenizers
alvas@ubi:~$ mv nltk_data/ tmp_move_nltk_data/
alvas@ubi:~$ python
Python 2.7.11+ (default, Apr 17 2016, 14:00:29) 
[GCC 5.3.1 20160413] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from nltk import word_tokenize
>>> from nltk.tokenize import TreebankWordTokenizer
>>> tokenizer = TreebankWordTokenizer()
>>> tokenizer.tokenize('This is a sentence.')
['This', 'is', 'a', 'sentence', '.']

But:

alvas@ubi:~$ ls nltk_data/
chunkers  corpora  grammars  help  models  stemmers  taggers  tokenizers
alvas@ubi:~$ mv nltk_data/ tmp_move_nltk_data
alvas@ubi:~$ python
Python 2.7.11+ (default, Apr 17 2016, 14:00:29) 
[GCC 5.3.1 20160413] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from nltk import sent_tokenize
>>> sent_tokenize('This is a sentence. This is another.')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/nltk/tokenize/__init__.py", line 90, in sent_tokenize
    tokenizer = load('tokenizers/punkt/{0}.pickle'.format(language))
  File "/usr/local/lib/python2.7/dist-packages/nltk/data.py", line 801, in load
    opened_resource = _open(resource_url)
  File "/usr/local/lib/python2.7/dist-packages/nltk/data.py", line 919, in _open
    return find(path_, path + ['']).open()
  File "/usr/local/lib/python2.7/dist-packages/nltk/data.py", line 641, in find
    raise LookupError(resource_not_found)
LookupError: 
**********************************************************************
  Resource u'tokenizers/punkt/english.pickle' not found.  Please
  use the NLTK Downloader to obtain the resource:  >>>
  nltk.download()
  Searched in:
    - '/home/alvas/nltk_data'
    - '/usr/share/nltk_data'
    - '/usr/local/share/nltk_data'
    - '/usr/lib/nltk_data'
    - '/usr/local/lib/nltk_data'
    - u''
**********************************************************************

>>> from nltk import word_tokenize
>>> word_tokenize('This is a sentence.')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/nltk/tokenize/__init__.py", line 106, in word_tokenize
    return [token for sent in sent_tokenize(text, language)
  File "/usr/local/lib/python2.7/dist-packages/nltk/tokenize/__init__.py", line 90, in sent_tokenize
    tokenizer = load('tokenizers/punkt/{0}.pickle'.format(language))
  File "/usr/local/lib/python2.7/dist-packages/nltk/data.py", line 801, in load
    opened_resource = _open(resource_url)
  File "/usr/local/lib/python2.7/dist-packages/nltk/data.py", line 919, in _open
    return find(path_, path + ['']).open()
  File "/usr/local/lib/python2.7/dist-packages/nltk/data.py", line 641, in find
    raise LookupError(resource_not_found)
LookupError: 
**********************************************************************
  Resource u'tokenizers/punkt/english.pickle' not found.  Please
  use the NLTK Downloader to obtain the resource:  >>>
  nltk.download()
  Searched in:
    - '/home/alvas/nltk_data'
    - '/usr/share/nltk_data'
    - '/usr/local/share/nltk_data'
    - '/usr/lib/nltk_data'
    - '/usr/local/lib/nltk_data'
    - u''
**********************************************************************

But it looks like that's not the case, if we look at https://github.com/nltk/nltk/blob/develop/nltk/tokenize/init.py#L93. It seems like word_tokenize has implicitly called sent_tokenize() which requires the punkt model.

I am not sure whether this is a bug or a feature but it seems like the old idiom might be outdated given the current code:

>>> from nltk import sent_tokenize, word_tokenize
>>> sentences = 'This is a foo bar sentence. This is another sentence.'
>>> tokenized_sents = [word_tokenize(sent) for sent in sent_tokenize(sentences)]
>>> tokenized_sents
[['This', 'is', 'a', 'foo', 'bar', 'sentence', '.'], ['This', 'is', 'another', 'sentence', '.']]

It can simply be:

>>> word_tokenize(sentences)
['This', 'is', 'a', 'foo', 'bar', 'sentence', '.', 'This', 'is', 'another', 'sentence', '.']

But we see that the word_tokenize() flattens the list of list of string to a single list of string.

---

Alternatively, you can try to use a new tokenizer that was added to NLTK toktok.py based on https://github.com/jonsafari/tok-tok that requires no pre-trained models.

Answer 2

You are right. You need Punkt Tokenizer Models. It has 13 MB and nltk.download('punkt') should do the trick.