NLP Text Classification 2

Question 1

Which regularization technique is commonly applied in neural text classifiers to prevent overfitting by randomly deactivating neurons during training?

Accepted Answer

Dropout

Answer

Dropout randomly sets a fraction of neuron activations to zero during each training pass, forcing the network to learn redundant representations and reducing overfitting.

Question 2

What is the key advantage of fine-tuning a pre-trained language model (e.g., BERT) for text classification compared to training from scratch?

Accepted Answer

It leverages general language knowledge learned from large corpora, needing far less task-specific data

Answer

Pre-trained models encode rich linguistic knowledge from massive unlabeled corpora, so fine-tuning requires only a small labeled dataset to adapt them to a specific classification task.

Question 3

In text classification, what problem does 'class imbalance' refer to?

Accepted Answer

One class having significantly more examples than others in the training set

Answer

Class imbalance occurs when training data has a disproportionate number of examples for some classes, causing the model to be biased toward predicting the majority class.

Question 4

Which technique involves creating synthetic minority-class examples to address class imbalance in text classification?

Accepted Answer

SMOTE (Synthetic Minority Over-sampling Technique)

Answer

SMOTE generates new synthetic examples for the minority class by interpolating between existing minority-class instances, helping balance the training distribution.

Question 5

What is hierarchical text classification?

Accepted Answer

Organizing categories into a tree structure where documents are classified from broad to specific

Answer

Hierarchical classification leverages a taxonomy (e.g., Science → Biology → Genetics) to make classification decisions at multiple levels of specificity, improving accuracy on fine-grained categories.

NLP Practice Test

NLP Practice Test

NLP Text Classification 2