2.4 KiB
String processing
Instructions
Tokenization is the process of breaking down a string into smaller pieces, called tokens. In natural language processing, tokenization typically refers to the process of breaking down a sentence into words or breaking down a paragraph into sentences.
Create a file string_processing.py which will have a function tokenize(sentence) that given a sentence will do the following:
- remove all punctuation marks and special characters
- separate all words like so:
"it's not 3" => ['it', 's', 'not', '3'] - put all the words in lowercase
- return a list of all the words.
Usage
Here is a possible test.py to test your functions:
import string_processing
if __name__ == '__main__':
my_sentence = "It's not possible, you can't ask for a raise"
print(string_processing.tokenize(my_sentence))
$ python test.py
['it', 's', 'not', 'possible', 'you', 'can', 't', 'ask', 'for', 'a', 'raise']
Hints
The re library is a module for working with regular expressions. It provides a set of functions for working with regular expressions, including:
re.sub(): Replaces all occurrences of a regular expression pattern in a string with a replacement string.
text = "This is a test sentence. It has multiple punctuation marks!"
# Replace all exclamation marks with question marks
new_text = re.sub("!", "?", text)
print(new_text)
and the output:
This is a test sentence. It has multiple punctuation marks?
The .lower() method is used to convert the sentence to lowercase before tokenizing it.
text = "This Is A TeST Sentence."
lower_text = text.lower()
print(lower_text)
and the output:
this is a test sentence.
The .split() method is used to split the sentence into a list of words.
text = "This is a test sentence."
words = text.split()
print(words)
and the output:
['This', 'is', 'a', 'test', 'sentence.']