NLTK 知识整理

NLTK 知识整理

nltk.corpus模块自带语料

NLTK comes with many corpora, toy grammars, trained models, etc. A complete list is posted at: http://nltk.org/nltk_data/

1. Run the Python interpreter and type the commands:

>>> import nltk
>>> nltk.download()

1. Test that the data has been installed as follows. (This assumes you downloaded the Brown Corpus):

>>> from nltk.corpus import brown
>>> brown.words()
['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]

API

• words(): list of str
• sents(): list of (list of str)
• paras(): list of (list of (list of str))
• tagged_words(): list of (str,str) tuple
• tagged_sents(): list of (list of (str,str))
• tagged_paras(): list of (list of (list of (str,str)))
• chunked_sents(): list of (Tree w/ (str,str) leaves)
• parsed_sents(): list of (Tree with str leaves)
• parsed_paras(): list of (list of (Tree with str leaves))
• xml(): A single xml ElementTree
• raw(): unprocessed corpus contents

For example, to read a list of the words in the Brown Corpus, use nltk.corpus.brown.words():

>>> from nltk.corpus import brown
>>> print(", ".join(brown.words()))
The, Fulton, County, Grand, Jury, said, ...

Tokenize 英文分词

Tokenize some text:

>>> import nltk
>>> sentence = """At eight o'clock on Thursday morning
... Arthur didn't feel very good."""
>>> nltk.word_tokenize(sentence)
['At', 'eight', "o'clock", 'on', 'Thursday', 'morning',
'Arthur', 'did', "n't", 'feel', 'very', 'good', '.']

References

[1] NLTK 3.2.5 documentation http://www.nltk.org/

[2] nltk.corpus package http://www.nltk.org/api/nltk.corpus.html#module-nltk.corpus