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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Proceedings Article
A Latent Dirichlet Model for Unsupervised Entity Resolution
TL;DR: This work proposes a novel sampling algorithm for collective entity resolution which is unsupervised and also takes entity relations into account, and demonstrates the utility and practicality of the relational entity resolution approach for author resolution in two real-world bibliographic datasets.
Proceedings ArticleDOI
Topic Modeling for Short Texts with Auxiliary Word Embeddings
TL;DR: A simple, fast, and effective topic model for short texts, named GPU-DMM, based on the Dirichlet Multinomial Mixture model, which achieves comparable or better topic representations than state-of-the-art models, measured by topic coherence.
Proceedings ArticleDOI
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