Bag of Words (CountVectorizer) in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Bag of Words model in text processing?

It is a way to represent text by counting how many times each word appears, ignoring grammar and word order.

beginner

What does CountVectorizer do in NLP?

CountVectorizer converts a collection of text documents into a matrix of word counts, showing how often each word appears in each document.

beginner

Why does Bag of Words ignore word order?

Because it focuses only on the frequency of words, not their position, to simplify text into numbers for machine learning.

intermediate

How does CountVectorizer handle new words not seen during training?

It ignores words not in its vocabulary, so new words in test data are not counted in the output matrix.

intermediate

What is a limitation of the Bag of Words model?

It loses the meaning from word order and context, so it can’t understand phrases or sentence structure.

What does CountVectorizer output for a set of text documents?

A A matrix of word counts per document

B A list of sentences

C A summary of text meaning

D A list of synonyms

CountVectorizer creates a matrix showing how many times each word appears in each document.

Which aspect does Bag of Words ignore?

A Word frequency

B Word spelling

C Word order

D Word count

Bag of Words counts words but does not consider the order they appear in.

If a new word appears in test data but not in training, what happens in CountVectorizer?

A It ignores the word

B It counts the word normally

C It adds the word to the vocabulary

D It throws an error

CountVectorizer ignores words not in its learned vocabulary.

Why is Bag of Words useful for machine learning?

A It translates text into another language

B It converts text into numbers that models can understand

C It summarizes text meaning

D It corrects grammar mistakes

Machine learning models need numbers, and Bag of Words turns text into numeric counts.

Which is a common problem with Bag of Words?

A It requires labeled data

B It needs a lot of memory for small texts

C It only works with numbers

D It loses context and word order

Bag of Words does not keep the order or meaning of words, only counts.

Explain how CountVectorizer transforms text data into a format usable by machine learning models.

Think about how text is turned into numbers by counting words.

Describe one main limitation of the Bag of Words model and why it matters.

Consider what information is lost when only counting words.