Latent dirichlet allocation
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TLDR
This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.Abstract:
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.read more
Citations
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The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation
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Posted Content
Autoencoding Variational Inference For Topic Models
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TL;DR: This work presents what is to their knowledge the first effective AEVB based inference method for latent Dirichlet allocation (LDA), which it is called Autoencoded Variational Inference For Topic Model (AVITM).
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Partisan asymmetries in online political activity
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Proceedings ArticleDOI
Bayesian Unsupervised Topic Segmentation
Jacob Eisenstein,Regina Barzilay +1 more
TL;DR: A novel Bayesian approach to unsupervised topic segmentation is described, showing that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation.
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