Latent Dirichlet Allocation (LDA) in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Latent Dirichlet Allocation (LDA)?

LDA is a method to find hidden topics in a collection of documents. It groups words that often appear together to discover themes without reading the documents.

beginner

What are the main components of LDA?

LDA has three main parts: documents, topics, and words. Each document is made of topics, and each topic is made of words with certain probabilities.

intermediate

How does LDA represent documents and topics?

LDA represents each document as a mix of topics, and each topic as a mix of words. This means a document can talk about many topics in different amounts.

intermediate

What role do Dirichlet distributions play in LDA?

Dirichlet distributions help LDA decide how topics are spread in documents and how words are spread in topics. They control the mix and make the model flexible.

beginner

Why is LDA considered an unsupervised learning method?

Because LDA finds topics without needing labeled data or knowing the topics beforehand. It learns patterns just by looking at the words in documents.

What does LDA primarily discover in a set of documents?

A Document length

B Sentiment scores

C Hidden topics

D Named entities

LDA is designed to find hidden topics that explain the words in documents.

In LDA, what is a 'topic' best described as?

A A sentence

B A group of related words

C A document title

D A single word

A topic is a collection of words that often appear together and represent a theme.

Which distribution does LDA use to model topic proportions in documents?

A Dirichlet distribution

B Normal distribution

C Uniform distribution

D Binomial distribution

LDA uses Dirichlet distributions to model how topics are mixed in documents.

Why is LDA called 'unsupervised' learning?

A It predicts document categories

B It uses labeled topics

C It requires human input for training

D It does not require labeled data

LDA learns topics from data without needing labels or predefined categories.

What is the output of LDA for each document?

A A mixture of topics with probabilities

B A single topic label

C A list of keywords only

D A sentiment score

LDA outputs a probability distribution showing how much each topic contributes to the document.

Explain how LDA models documents and topics using probability distributions.

Think about how LDA assigns topics to words and topics to documents.

Describe why LDA is useful for discovering hidden themes in large text collections.

Consider how LDA helps when you have many documents but no clear categories.