How to get rid of punctuation using NLTK tokenizer?

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I'm just starting to use NLTK and I don't quite understand how to get a list of words from text. If I use nltk.word_tokenize(), I get a list of words and punctuation. I need only the words instead. How can I get rid of punctuation? Also word_tokenize doesn't work with multiple sentences: dots are added to the last word.

Take a look at the other tokenizing options that nltk provides here. For example, you can define a tokenizer that picks out sequences of alphanumeric characters as tokens and drops everything else:

from nltk.tokenize import RegexpTokenizer

tokenizer = RegexpTokenizer(r'\w+')
tokenizer.tokenize('Eighty-seven miles to go, yet.  Onward!')

Output:

['Eighty', 'seven', 'miles', 'to', 'go', 'yet', 'Onward']

How to get rid of punctuation using NLTK tokenizer?, offers a function called translate() that will map one set of characters to another. I’m just starting to use NLTK and I don’t quite understand how to get a list of words from text. If I use nltk.word_tokenize(), I get a list of words and punctuation. I need only the words instead. How can I get rid of punctuation? Also word_tokenize doesn’t work with multiple sentences: dots are added to the last word.

You do not really need NLTK to remove punctuation. You can remove it with simple python. For strings:

import string
s = '... some string with punctuation ...'
s = s.translate(None, string.punctuation)

Or for unicode:

import string
translate_table = dict((ord(char), None) for char in string.punctuation)   
s.translate(translate_table)

and then use this string in your tokenizer.

P.S. string module have some other sets of elements that can be removed (like digits).

How to Clean Text for Machine Learning with Python, Sentence Tokenizer. This tokenizer divides a text into a list of sentences, by using an unsupervised algorithm to build a model for abbreviation words, collocations, and words that start sentences. It must be trained on a large collection of plaintext in the target language before it can be used. In this tutorial, You will learn how to write a program to remove punctuation and stopwords in python using nltk library. How to remove punctuation in python nltk. We will regular expression with wordnet library.

Below code will remove all punctuation marks as well as non alphabetic characters. Copied from their book.

http://www.nltk.org/book/ch01.html

import nltk

s = "I can't do this now, because I'm so tired.  Please give me some time. @ sd  4 232"

words = nltk.word_tokenize(s)

words=[word.lower() for word in words if word.isalpha()]

print(words)

output

['i', 'ca', 'do', 'this', 'now', 'because', 'i', 'so', 'tired', 'please', 'give', 'me', 'some', 'time', 'sd']

punkt - nltk - Python documentation, How do you split text into a sentence in Python? I'm just starting to use NLTK and I don't quite understand how to get a list of words from text. If I use nltk.word_tokenize(), I get a list of words and punctuation. I need only the words instead. How can I get rid of punctuation? Also word_tokenize doesn't work with multiple sentences: dots are added to the last word.

As noticed in comments start with sent_tokenize(), because word_tokenize() works only on a single sentence. You can filter out punctuation with filter(). And if you have an unicode strings make sure that is a unicode object (not a 'str' encoded with some encoding like 'utf-8').

from nltk.tokenize import word_tokenize, sent_tokenize

text = '''It is a blue, small, and extraordinary ball. Like no other'''
tokens = [word for sent in sent_tokenize(text) for word in word_tokenize(sent)]
print filter(lambda word: word not in ',-', tokens)

Why is removing stop words not always a good idea, How to remove punctuation and stopwords in python nltk - 2020 with example program. from nltk.tokenize import result = tokenizer.tokenize('hey! how. The following are code examples for showing how to use nltk.tokenize.TweetTokenizer(). They are extracted from open source Python projects. You can vote up the examples you like or vote down the ones you don't like. You can also save this page to your account.

I just used the following code, which removed all the punctuation:

tokens = nltk.wordpunct_tokenize(raw)

type(tokens)

text = nltk.Text(tokens)

type(text)  

words = [w.lower() for w in text if w.isalpha()]

How to split text into sentences in Python, Split by Whitespace and Remove Punctuation. Note: This Tokenization and Cleaning with NLTK from nltk.tokenize import word_tokenize. word_tokenize works only at the sentence level. So you'll have to split at the sentence level and the tokenize the sentences. You can remove punctuation without using NLTK.

How to remove punctuation and stopwords in python nltk, If I use nltk.word_tokenize() , I get a list of words and punctuation. I need only the words instead. How can I get rid of punctuation? from nltk.stem import LancasterStemmer, SnowballStemmer, RegexpStemmer, WordNetLemmatizer #this was part of the NLP notebook import nltk nltk.download('punkt') #import sentence tokenizer from nltk import sent_tokenize #import word tokenizer from nltk import word_tokenize #list of stopwords from nltk.corpus import stopwords import string Dealing

How to get rid of punctuation using NLTK tokenizer?, Removing Punctuation and Stop Words nltk. preprocess.py. import string. import nltk. from nltk.tokenize import RegexpTokenizer. from nltk.corpus import stopwords [w for w in tokens if not w in stopwords.words('english')]. A tokenizer that divides a string into substrings by splitting on the specified string (defined in subclasses). Identify the tokens using integer offsets (start_i, end_i) , where s [start_i:end_i] is the corresponding token. Return a tokenized copy of s. A processing interface for tokenizing a string. Subclasses must define tokenize () or

Removing Punctuation and Stop Words nltk · GitHub, A tokenizer that divides a string into substrings by splitting on the specified string Remove Twitter username handles from text. Used to realign punctuation that should be included in a sentence although it follows the period (or ?, !) The process of converting data to something a computer can understand is referred to as pre-processing. One of the major forms of pre-processing is to filter out useless data. In natural language processing, useless words (data), are referred to as stop words. Stop Words: A stop word is a commonly used word (such as “the”, “a”, “an

Comments
  • Why don't you remove the punctuation yourself? nltk.word_tokenize(the_text.translate(None, string.punctuation)) should work in python2 while in python3 you can do nltk.work_tokenize(the_text.translate(dict.fromkeys(string.punctuation))).
  • This doesn't work. Nothing happens with the text.
  • The workflow assumed by NLTK is that you first tokenize into sentences and then every sentence into words. That is why word_tokenize() does not work with multiple sentences. To get rid of the punctuation, you can use a regular expression or python's isalnum() function.
  • It does work: >>> 'with dot.'.translate(None, string.punctuation) 'with dot'(note no dot at the end of the result) It may cause problems if you have things like 'end of sentence.No space', in which case do this instead: the_text.translate(string.maketrans(string.punctuation, ' '*len(string.punctuation))) which replaces all punctuation with white spaces.
  • Oops, it works indeed, but not with Unicode strings.
  • Note that if you use this option, you lose natural language features special to word_tokenize like splitting apart contractions. You can naively split on the regex \w+ without any need for the NLTK.
  • To illustrate @sffc comment, you might lose words such as "Mr."
  • its replacing ' n't ' to 't' how to get rid of this?
  • Remove all punctuation using the list expression that also works too. a = "*fa,fd.1lk#$" print("".join([w for w in a if w not in string.punctuation]))
  • Just be aware that using this method you will lose the word "not" in cases like "can't" or "don't", that may be very important for understanding and classifying the sentence. It is better using sentence.translate(string.maketrans("", "", ), chars_to_remove), where chars_to_remove can be ".,':;!?"