Breaking News: Grepper is joining You.com. Read the official announcement!

nltk wordpunct_tokenize vs word_tokenize

Juan Andres Russy Cervera answered on August 3, 2022 Popularity 9/10 Helpfulness 2/10

answer nltk wordpunct_tokenize vs word_tokenize

nltk wordpunct_tokenize vs word_tokenize

Comment

wordpunct_tokenize is based on a simple regexp tokenization. It is defined as

wordpunct_tokenize = WordPunctTokenizer().tokenize
which you can find here. Basically it uses the regular expression \w+|[^\w\s]+ to split the input.

word_tokenize on the other hand is based on a TreebankWordTokenizer, see the docs here. It basically tokenizes text like in the Penn Treebank. Here is a silly example that should show how the two differ.

sent = "I'm a dog and it's great! You're cool and Sandy's book is big. Don't tell her, you'll regret it! 'Hey', she'll say!"
>>> word_tokenize(sent)
['I', "'m", 'a', 'dog', 'and', 'it', "'s", 'great', '!', 'You', "'re", 
 'cool', 'and', 'Sandy', "'s", 'book', 'is', 'big', '.', 'Do', "n't", 'tell',
 'her', ',', 'you', "'ll", 'regret', 'it', '!', "'Hey", "'", ',', 'she', "'ll", 'say', '!']
>>> wordpunct_tokenize(sent)
['I', "'", 'm', 'a', 'dog', 'and', 'it', "'", 's', 'great', '!', 'You', "'",
 're', 'cool', 'and', 'Sandy', "'", 's', 'book', 'is', 'big', '.', 'Don',
 "'", 't', 'tell', 'her', ',', 'you', "'", 'll', 'regret', 'it', '!', "'", 
 'Hey', "',", 'she', "'", 'll', 'say', '!']
As we can see, wordpunct_tokenize will split pretty much at all special symbols and treat them as separate units. word_tokenize on the other hand keeps things like 're together. It doesn't seem to be all that smart though, since as we can see it fails to separate the initial single quote from 'Hey'.

Interestingly, if we write the sentence like this instead (single quotes as string delimiter and double quotes around "Hey"):

sent = 'I\'m a dog and it\'s great! You\'re cool and Sandy\'s book is big. Don\'t tell her, you\'ll regret it! "Hey", she\'ll say!'
we get

>>> word_tokenize(sent)
['I', "'m", 'a', 'dog', 'and', 'it', "'s", 'great', '!', 'You', "'re", 
 'cool', 'and', 'Sandy', "'s", 'book', 'is', 'big', '.', 'Do', "n't", 
 'tell', 'her', ',', 'you', "'ll", 'regret', 'it', '!', '``', 'Hey', "''", 
 ',', 'she', "'ll", 'say', '!']
so word_tokenize does split off double quotes, however it also converts them to `` and ''. wordpunct_tokenize doesn't do this:

>>> wordpunct_tokenize(sent)
['I', "'", 'm', 'a', 'dog', 'and', 'it', "'", 's', 'great', '!', 'You', "'", 
 're', 'cool', 'and', 'Sandy', "'", 's', 'book', 'is', 'big', '.', 'Don', 
 "'", 't', 'tell', 'her', ',', 'you', "'", 'll', 'regret', 'it', '!', '"', 
 'Hey', '",', 'she', "'", 'll', 'say', '!']

Popularity 9/10 Helpfulness 2/10 Language whatever

Source: stackoverflow.com

Tags: nltk whatever

Link to this answer
Share Copy Link

Contributed on Aug 03 2022

Juan Andres Russy Cervera

0 Answers Avg Quality 2/10

nltk wordpunct_tokenize vs word_tokenize

Contents

More Related Answers

nltk wordpunct_tokenize vs word_tokenize

Grepper

Documentation

Social

Legal

Contact

Oops, You will need to install Grepper and log-in to perform this action.