TF-IDF and Bag-of-Words Models Quiz

Answers and explanations appear as you take the quiz.

1. What key information does a bag-of-words model discard?
   • The order and grammar of words
   • The word counts
   • The vocabulary
   • The document length
2. What does the IDF part of TF-IDF do?
   • Boosts words common across all documents
   • Downweights words that appear in many documents
   • Counts characters
   • Translates rare words
3. A word that appears in every document in the collection gets an IDF value close to what?
   • One thousand
   • Negative infinity
   • Zero
   • The document count
4. How is a TF-IDF score for a term computed?
   • TF minus IDF
   • TF divided by IDF
   • TF plus the document length
   • TF multiplied by IDF
5. Which similarity measure is commonly used to compare TF-IDF document vectors?
   • Cosine similarity
   • Edit distance
   • Mean squared error
   • Bit count
6. Why does TF-IDF give high scores to certain words?
   • They are long words
   • They are frequent in one document but rare across the collection
   • They appear in every document
   • They are capitalized
7. In a bag-of-words representation, what does a vector position usually correspond to?
   • A sentence’s original word position
   • A document’s publication date
   • A term in the vocabulary
   • A grammatical parse-tree branch
8. In the common IDF formula log(N/df), what does df represent?
   • The number of times a word appears in one document
   • The total number of words in the vocabulary
   • The number of labels assigned to a document
   • The number of documents containing the term