NLP Text Classification

Question 1

Which machine learning algorithm is considered a generative classifier commonly used for text classification due to its simplicity and strong baseline performance?

Accepted Answer

Naive Bayes

Answer

Naive Bayes is a generative classifier that models the joint probability of features and labels, and despite its 'naive' independence assumption, it performs surprisingly well on text data.

Question 2

In text classification, what is the primary purpose of TF-IDF (Term Frequency-Inverse Document Frequency)?

Accepted Answer

To weight words by importance relative to a corpus

Answer

TF-IDF weights a word higher when it appears frequently in a document but rarely across the corpus, highlighting distinctive terms for classification.

Question 3

Which evaluation metric is most appropriate for a highly imbalanced text classification dataset where the positive class is rare?

Accepted Answer

F1-Score

Answer

F1-Score balances precision and recall, making it more informative than accuracy when the class distribution is skewed and the minority class matters most.

Question 4

What is multi-label text classification?

Accepted Answer

Assigning multiple non-exclusive labels to a single text instance

Answer

Multi-label classification allows each document to belong to several categories simultaneously, such as a news article tagged as both 'politics' and 'economy'.

Question 5

In the Bag-of-Words (BoW) model for text classification, which information is explicitly discarded?

Accepted Answer

Word order and syntax

Answer

The BoW model treats a document as an unordered set of word counts, discarding grammatical structure, word order, and context.

NLP Practice Test

NLP Practice Test

NLP Text Classification