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

Support Vector Machine

Answer

Random Forest

Answer

k-Nearest Neighbors

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

To reduce the dimensionality of text data

Answer

To tokenize sentences into individual words

Answer

To remove stop words from documents

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

Accuracy

Answer

Mean Squared Error

Answer

Perplexity

Question 4

What is multi-label text classification?

Accepted Answer

Assigning multiple non-exclusive labels to a single text instance

Answer

Classifying text into more than two mutually exclusive categories

Answer

Using multiple classifiers and voting on the final output

Answer

Breaking text into labeled spans for sequence tagging

Question 5

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

Accepted Answer

Word order and syntax

Answer

Word frequency

Answer

Document length

Answer

Vocabulary size

Question 6

Which deep learning architecture introduced the concept of self-attention that dramatically improved text classification benchmarks?

Accepted Answer

Transformer

Answer

Recurrent Neural Network (RNN)

Answer

Convolutional Neural Network (CNN)

Answer

Long Short-Term Memory (LSTM)

Question 7

What does 'zero-shot text classification' mean in the context of modern NLP?

Accepted Answer

Classifying text into categories that were not seen during model training

Answer

Training a model with no labeled data using only rule-based methods

Answer

Using zero regularization to prevent overfitting in classifiers

Answer

Classifying text with 100% accuracy without any errors