要使nltk.tokenize.word_tokenize工作，需要下载什么？

Question

我将在集群上使用nltk.tokenize.word_tokenize，在这个集群中，我的帐户受到空间配额的限制。在家里，我通过nltk.download()下载了所有的nltk资源，但是，正如我发现的，它需要大约2.5GB。

我觉得这有点过分了。您能建议一下nltk.tokenize.word_tokenize的最小（或几乎最小）依赖项是什么吗？到目前为止，我已经看到了nltk.download('punkt')，但我不确定它是否足够，大小是多少。我到底应该跑什么才能成功？

Answer 1

简而言之：

nltk.download('punkt')

就够了。

---

在long中：

如果只想使用NLTK进行标记化，则不需要下载NLTk中可用的所有模型和语料库。

实际上，如果您只是使用word_tokenize()，那么您就不需要nltk.download()中的任何资源。如果我们看一下代码，默认的word_tokenize()基本上就是TreebankWordTokenizer不应该使用任何额外的资源：

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', '.']

但是：

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''
**********************************************************************

但如果我们看看https://github.com/nltk/nltk/blob/develop/nltk/tokenize/init.py#L93的话，情况似乎并非如此。似乎word_tokenize隐式地调用了sent_tokenize()，这需要punkt模型。

我不确定这是一个bug还是一个特性，但是考虑到当前的代码，旧的习惯用法似乎已经过时了：

>>> 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', '.']]

它可以是：

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

但是我们看到word_tokenize()将字符串列表扁平化为单个字符串列表。

---

或者，您可以尝试使用一个新的标记器，该标记器是基于不需要预先训练模型的https://github.com/jonsafari/tok-tok添加到NLTK ^{}中的。

Answer 2

你说得对。你需要朋克标记器模型。它有13mb，nltk.download('punkt')应该能做到这一点。