= Latent Dirichlet allocation
{wiki=Latent_Dirichlet_allocation}
Latent Dirichlet Allocation (LDA) is a generative probabilistic model often used in natural language processing and machine learning for topic modeling. It provides a way to discover the underlying topics in a collection of documents. Here's a high-level overview of how it works: 1. **Assumptions**: LDA assumes that each document is composed of a mixture of topics, and each topic is characterized by a distribution over words.
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