Feature Extraction Quiz (BoW & TF-IDF)

Which of the following best describes the Bag of Words (BoW) model?

• It captures the order of words in a sentence.

• It uses semantic relationships between words.

• It converts text into a vector based on word frequency.

• It reduces the dimensionality of feature vectors using PCA.

Which technique assigns lower weights to common words like 'the' or 'is'?

• BoW

• TF

• TF-IDF

• One-hot encoding

What does TF in TF-IDF stand for?

• Term Frequency

• Textual Frequency

• Total Frequency

• Token Frequency

What is the purpose of IDF in TF-IDF?

• To normalize word frequencies

• To highlight frequent terms in the corpus

• To penalize terms that appear in many documents

• To tokenize the input text

Which of the following is a limitation of BoW?

• It cannot be used for long documents.

• It is not applicable to English language.

• It ignores word order and context.

• It works only with numerical data.

Which method could address the limitation of BoW by capturing phrases?

• Stemming

• N-grams

• Stopword removal

• Lemmatization

Which of the following has the highest IDF value?

• A word that appears in every document

• A word that appears in no documents

• A word that appears in only one document

• A word that appears in half the documents

TF-IDF is generally preferred over BoW because:

• It uses deep learning.

• It requires no preprocessing.

• It balances term frequency with document rarity.

• It captures the meaning of sentences.

What does a TF-IDF score of zero mean?

• The term is very rare.

• The term does not appear in the document.

• The term appears in all documents.

• The term has no semantic meaning.

Which component of TF-IDF varies depending on the document itself?

• Term Frequency

• Inverse Document Frequency

• Corpus size

• Document length