Question

我将在我的帐户受空间配额限制的群集上使用nltk.tokenize.word_tokenize。在家里，我按nltk下载了所有nltk.download()资源，但据我发现，它需要大约2.5GB。

这对我来说似乎有点矫枉过正。你能否建议nltk.tokenize.word_tokenize的最小（或几乎是最小）依赖关系是什么？到目前为止，我已经看过nltk.download('punkt')，但我不确定它是否足够，大小是多少。为了使它能运作，我究竟应该运行什么？

Answer 1

你是对的。你需要Punkt Tokenizer模型。它有13 MB，nltk.download('punkt')应该可以做到。

Answer 2

简而言之：

nltk.download('punkt')

就足够了。

长：

如果您打算使用NLTK进行标记化，则无需下载NLTk中提供的所有模型和语料库。

实际上，如果您只是使用word_tokenize()，那么您将不需要nltk.download()中的任何资源。如果我们查看代码，基本上TreebankWordTokenizer的默认word_tokenize()不应使用任何其他资源：

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', '.']

可是：

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

但如果我们看一下https://github.com/nltk/nltk/blob/develop/nltk/tokenize/init.py#L93，情况就不是这样了。似乎word_tokenize已隐式调用sent_tokenize()，需要punkt模型。

我不确定这是一个错误还是一个功能，但似乎旧的习语可能会因当前代码而过时：

>>> 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', '.']]

可以简单地说：

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

但是我们看到word_tokenize()将字符串列表的列表展平为单个字符串列表。

或者，您可以尝试使用新的标记器，该标记器将根据toktok.py添加到NLTK https://github.com/jonsafari/tok-tok，而不需要预先训练的模型。

Answer 3

如果您在 lambda 中有大量 NLTK 泡菜，则代码编辑器将无法编辑。使用 Lambda 层。您可以只上传 NLTK 数据并将数据包含在如下代码中。

nltk.data.path.append("/opt/tmp_nltk")

Answer 4

import nltk
nltk.download('punkt')

from nltk.tokenize import sent_tokenize, word_tokenize

EXAMPLE_TEXT = "Hello Mr.Smith,how are you doing today?"

print(sent_tokenize(EXAMPLE_TEXT))

下载什么以使nltk.tokenize.word_tokenize有效？

4 个答案: