Bag of Words and Text Vectorization Quiz

1. Which of the following best describes TF-IDF (Term Frequency-Inverse Document Frequency) weighting?

Counts raw word occurrences in each document

Weights terms by frequency and rarity across the corpus

Removes all common words from the vocabulary

Converts text into binary presence/absence vectors

2. What is the main advantage of using TF-IDF over simple count vectorization?

It processes text faster

It reduces the impact of common words and highlights unique terms

It automatically removes all stopwords

It preserves word order in documents

3. True or False: Word embeddings like Word2Vec and GloVe are examples of dense vector representations.

True

False

4. Which vectorization approach combines both term frequency and document frequency information?

Count Vectorization

Binary Vectorization

TF-IDF Vectorization

One-Hot Encoding

5. In a Bag of Words vector for document 'cat dog cat bird', the position for 'cat' would have value:

1 (present/absent)

2 (frequency count)

0.5 (normalized proportion)

3 (position in sequence)

6. True or False: Tokenization is the process of converting a document into individual words or tokens.

True

False

7. When converting text to vectors, what is the purpose of normalization?

To remove punctuation from text

To scale vector values to a standard range (e.g., 0-1)

To eliminate words with numbers

To convert all text to lowercase

8. The IDF (Inverse Document Frequency) component of TF-IDF gives higher weights to terms that are:

Frequent in all documents (common terms)

Rare and specific to few documents

Longest in character length

Alphabetically first in the vocabulary

9. Which of the following is a characteristic of n-grams in text processing?

They capture sequences of n consecutive words

They remove all vowels from words

They convert text to lowercase automatically

They eliminate duplicate words

10. True or False: A dense vector representation requires less memory than a sparse vector for text data.

True

False

11. In Count Vectorization, the value at position [document i, term j] represents:

Whether the term appears in the document (binary)

The frequency of the term in document i

The inverse document frequency of the term

The position of the term in the document

12. True or False: The Bag of Words model preserves the sequential order of words in a document.

True

False

13. In vectorization, what does the dimensionality of a text vector typically represent?

The number of sentences in the document

The size of the vocabulary (number of unique terms)

The length of the longest word in the document

The number of documents in the corpus

14. Which preprocessing step removes words like 'the', 'is', 'and' that appear frequently but carry little semantic meaning?

Lemmatization

Stopword removal

Stemming

Tokenization

15. What is the vocabulary size in a Bag of Words model with 5,000 unique words from a corpus?

5,000 (the vector dimension for each document)

The total number of words in all documents

The average number of words per document

The number of documents in the corpus

16. In text vectorization, a sparse vector representation typically means:

Most elements are zero and only a few contain non-zero values

The vector has fewer dimensions than the vocabulary size

The vector requires more memory than a dense representation

All elements in the vector are positive integers

17. In the Bag of Words model, what is the primary limitation when processing the sentence 'The cat sat on the mat' versus 'The mat sat on the cat'?

Word order and semantic meaning are lost

The model cannot handle repeated words

Computational complexity increases exponentially

Stopwords are automatically removed

18. Which technique reduces words to their root form (e.g., 'running', 'runs' → 'run')?

Tokenization

Lemmatization

Stemming

Normalization

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DataX Bag of Words and Text Vectorization Quiz

1. Which of the following best describes TF-IDF (Term Frequency-Inverse Document Frequency) weighting?

2.

What first name or nickname would you like us to use?

2. What is the main advantage of using TF-IDF over simple count vectorization?

3. True or False: Word embeddings like Word2Vec and GloVe are examples of dense vector representations.

4. Which vectorization approach combines both term frequency and document frequency information?

5. In a Bag of Words vector for document 'cat dog cat bird', the position for 'cat' would have value:

6. True or False: Tokenization is the process of converting a document into individual words or tokens.

7. When converting text to vectors, what is the purpose of normalization?

8. The IDF (Inverse Document Frequency) component of TF-IDF gives higher weights to terms that are:

9. Which of the following is a characteristic of n-grams in text processing?

10. True or False: A dense vector representation requires less memory than a sparse vector for text data.

11. In Count Vectorization, the value at position [document i, term j] represents:

12. True or False: The Bag of Words model preserves the sequential order of words in a document.

13. In vectorization, what does the dimensionality of a text vector typically represent?

14. Which preprocessing step removes words like 'the', 'is', 'and' that appear frequently but carry little semantic meaning?

15. What is the vocabulary size in a Bag of Words model with 5,000 unique words from a corpus?

16. In text vectorization, a sparse vector representation typically means:

17. In the Bag of Words model, what is the primary limitation when processing the sentence 'The cat sat on the mat' versus 'The mat sat on the cat'?

18. Which technique reduces words to their root form (e.g., 'running', 'runs' → 'run')?

19. The ______ vector model represents documents as unordered collections of words with their frequencies.

20. ______ is the process of breaking text into individual words or meaningful units for analysis.