Bag of Words Model Quiz (Easy)

In a BoW model, how is a document typically represented?

What is one common preprocessing step before applying the BoW model?

How does the BoW model handle new words that appear in a test set but not in the training set?

Which of the following is a limitation of the Bag of Words model?

In Bag of Words, what does the term “vocabulary” refer to?

How can the weight of rare but important words be increased in a BoW model?

What is the Bag of Words (BoW) model primarily used for?

In the context of BoW, what is tokenization?

What does TF-IDF stand for, and how is it related to BoW?

What is a potential drawback of using a very large vocabulary in a BoW model for classification tasks?

In a BoW model, how does document length normalization typically affect the vector representation?