Topic Models: Latent Dirichlet Allocation

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Description

This lecture covers the basics of topic modeling, focusing on Latent Dirichlet Allocation (LDA). It explains the process of clustering documents into topics, estimating topic-word distributions, and inferring document-topic distributions. The lecture also discusses the generative process of LDA, the learning algorithms involved, and the limitations of the model. Additionally, it explores the use of LDA in digital humanities, its evaluation metrics, and its application as a form of dimensionality reduction. The lecture concludes with an overview of approximate inference methods and extensions to the LDA model.

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https://mediaspace.epfl.ch/media/0_fnm4wb2h

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Latent Dirichlet allocation

In natural language processing, Latent Dirichlet Allocation (LDA) is a Bayesian network (and, therefore, a generative statistical model) that explains a set of observations through unobserved groups, and each group explains why some parts of the data are similar. The LDA is an example of a Bayesian topic model. In this, observations (e.g., words) are collected into documents, and each word's presence is attributable to one of the document's topics. Each document will contain a small number of topics.

Topic model

In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both.

Binomial distribution

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