In regular expression, the
\W character class matches any non-word character (i.e., any character that is not a letter, digit, or underscore). The
+ quantifier matches one or more occurrences of the previous character or character class, in this case, one or more non-word characters.
re.sub() function is used to replace all occurrences of a pattern in a string with a specified replacement. In this case, the pattern
\W+ matches one or more non-word characters, and the replacement is a single space
' '. Therefore, the
re.sub('\W+', ' ', text) statement replaces all non-word characters in the
text string with a single space.
The purpose of this statement is to remove all non-word characters and replace them with a space, effectively cleaning up the text and preparing it for further text processing tasks such as tokenization or part-of-speech tagging.
Now coming to your second question
In natural language processing (NLP), reducing words to their base or root form is an important step in text preprocessing. The reason for this is that it can help to simplify the text data and reduce the dimensionality of the feature space.
For example, consider a text dataset that contains the following three sentences:
- The cat is jumping over the fence.
- The cats are jumping over the fences.
- The jumped cat will not jump again.
Although these sentences have different grammatical structures and contain different words, they all share a common meaning: a cat or cats jumping over a fence or fences. By reducing each word to its base or root form, we can simplify the text data and represent each sentence as a set of common base or root words, such as “cat”, “jump”, and “fence”.
Reducing words to their base or root form can also help to address the problem of sparsity in text data, where many words occur only once or a few times in the dataset. By reducing words to their base or root form, we can group together words that have a similar meaning or function, and thus reduce the number of unique words in the dataset.
Furthermore, reducing words to their base or root form can also help to address the problem of word variation, where the same word can appear in different forms due to factors such as tense, plurality, or case. By reducing words to their base or root form, we can group together words that have a similar meaning, even if they appear in different forms.
Therefore, reducing words to their base or root form is an important step in NLP text preprocessing, as it can help to simplify the text data, reduce dimensionality, address sparsity and variation problems, and improve the accuracy of downstream NLP tasks such as text classification or sentiment analysis.