Which preprocessing step removes common words with no informational value like 'a' and 'the'?

Which preprocessing step removes common words with no informational value like 'a' and 'the'?

• A. Stop Word Removal
• B. Stemming
• C. Lemmatization
• D. Bag of Words

Answer: (A)

In NLP preprocessing, removing stop words targets words that occur very frequently but carry little meaning for distinguishing documents, such as "a" and "the." By filtering these out, you reduce noise and shrink the feature space, which helps models focus on more informative terms and speeds up processing, especially before turning text into numerical features like a Bag of Words vector. Stemming and lemmatization are about normalizing words to their base forms (stems or lemmas) so different forms of the same word are treated similarly, not about removing words. Bag of Words is a representation method that counts word occurrences, and it can include or exclude stop words depending on preprocessing, but the specific step that removes common, low-information words is stop word removal.

In NLP preprocessing, removing stop words targets words that occur very frequently but carry little meaning for distinguishing documents, such as "a" and "the." By filtering these out, you reduce noise and shrink the feature space, which helps models focus on more informative terms and speeds up processing, especially before turning text into numerical features like a Bag of Words vector.

Stemming and lemmatization are about normalizing words to their base forms (stems or lemmas) so different forms of the same word are treated similarly, not about removing words. Bag of Words is a representation method that counts word occurrences, and it can include or exclude stop words depending on preprocessing, but the specific step that removes common, low-information words is stop word removal.