NLP Text Preprocessing 3

Question 1

Which regular expression pattern correctly matches any whitespace character in Python's `re` module?

Accepted Answer

\s

Answer

\s matches any whitespace character including spaces, tabs, newlines, and other Unicode whitespace.

Question 2

What is Unicode normalization form NFC used for in text preprocessing?

Accepted Answer

Composing characters into their canonical composed form

Answer

NFC (Canonical Decomposition followed by Canonical Composition) ensures characters with diacritics are stored in a single composed code point rather than multiple characters.

Question 3

In the context of text preprocessing for social media data, what does 'denoising' typically involve?

Accepted Answer

Cleaning hashtags, URLs, emojis, and slang from text

Answer

Denoising social media text involves removing or normalizing noisy elements like URLs, hashtags, @ mentions, emojis, and informal spelling.

Question 4

What does the 'max_features' parameter control in scikit-learn's CountVectorizer?

Accepted Answer

The size of the vocabulary built from top frequent terms

Answer

max_features limits the vocabulary to the top N most frequent terms across the corpus, reducing dimensionality.

Question 5

Which tokenization approach handles contractions like "don't" most correctly for downstream NLP tasks?

Accepted Answer

Splitting into 'do' and "n't" as a negation marker

Answer

Splitting into 'do' and "n't" preserves the negation information as a distinct token, which is linguistically motivated and useful for sentiment analysis.

NLP Practice Test

NLP Practice Test

NLP Text Preprocessing 3