RNN for text classification in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does RNN stand for and what is its main use in text classification?

RNN stands for Recurrent Neural Network. It is mainly used to process sequences like text by remembering previous words to understand context for classification.

beginner

How does an RNN handle input sequences differently from a regular neural network?

An RNN processes input one step at a time and keeps a memory of previous steps, allowing it to understand order and context in sequences like sentences.

intermediate

What is the role of the hidden state in an RNN?

The hidden state stores information from previous inputs in the sequence, helping the RNN remember context as it processes each new word.

intermediate

Why might a simple RNN struggle with long text sequences?

Simple RNNs can forget information from earlier in the sequence due to problems like vanishing gradients, making it hard to learn long-range dependencies.

intermediate

Name two advanced RNN variants that help with remembering long-term context.

Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are two RNN variants designed to better remember long-term information in sequences.

What is the main advantage of using an RNN for text classification?

A It uses convolution to detect features.

B It processes all words independently without order.

C It can remember previous words to understand context.

D It only works with fixed-length inputs.

RNNs process sequences step-by-step and keep memory of previous inputs, which helps understand context in text.

What problem do LSTM and GRU solve in RNNs?

A They speed up training by skipping steps.

B They help remember information over long sequences.

C They reduce the size of the input data.

D They convert text into images.

LSTM and GRU are designed to keep important information longer and avoid forgetting it during training.

Which part of an RNN holds information from previous words?

A Hidden state

B Input embedding

C Output layer

D Loss function

The hidden state stores information from earlier inputs to help the RNN understand context.

Why can simple RNNs struggle with very long text sequences?

A They cannot process sequences longer than 10 words.

B They only work with numeric data.

C They require labeled images for training.

D They forget earlier information due to vanishing gradients.

Vanishing gradients cause simple RNNs to lose information from earlier in long sequences.

In text classification with RNNs, what is the typical final step?

A Use the last hidden state to predict the class.

B Convert text to images.

C Ignore the sequence order.

D Use convolution layers to classify.

The last hidden state summarizes the sequence and is used to predict the text's class.

Explain how an RNN processes a sentence for text classification.

Think about how the network reads words one by one and remembers what it saw before.

Describe why LSTM or GRU might be better than a simple RNN for long text sequences.

Consider how these variants help keep important information longer.