Latent dirichlet allocation
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
More filters
Journal ArticleDOI
Political Cleavages within Industry: Firm level lobbying for Trade Liberalization
TL;DR: The authors found that productive exporting firms are more likely to lobby to reduce tariffs, especially when their products are sufficiently differentiated, while import-competing firms need not fear product substitution, and they also find that highly differentiated products have lower tariff rates.
Journal ArticleDOI
Clustering scientific documents with topic modeling
TL;DR: This paper investigates methods, including LDA and its extensions, for separating a set of scientific publications into several clusters and explores potential scientometric applications of such text analysis capabilities.
Proceedings Article
Asynchronous Distributed Learning of Topic Models
TL;DR: It is demonstrated that the asynchronous algorithms presented are able to learn global topic models that are statistically as accurate as those learned by the standard LDA and HDP samplers, but with significant improvements in computation time and memory.
Proceedings Article
Dynamic Non-Parametric Mixture Models and the Recurrent Chinese Restaurant Process: with Applications to Evolutionary Clustering.
Amr Ahmed,Eric P. Xing +1 more
TL;DR: The temporal Dirichlet process mixture model (TDPM) is introduced as a framework for evolutionary clustering and is given a detailed and intuitive construction using the recurrent Chinese restaurant process (RCRP) metaphor, as well as a Gibbs sampling algorithm to carry out posterior inference in order to determine the optimal cluster evolution.
Journal ArticleDOI
Speech Graphs Provide a Quantitative Measure of Thought Disorder in Psychosis
Natália Bezerra Mota,Natália Bezerra Mota,Nivaldo A. P. de Vasconcelos,Nivaldo A. P. de Vasconcelos,Nathalia Lemos,Ana C. Pieretti,Osame Kinouchi,Guillermo A. Cecchi,Mauro Copelli,Sidarta Ribeiro +9 more
TL;DR: The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence.
References
More filters
Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Book
Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Book
Theory of probability
Harold Jeffreys,R. Bruce Lindsay +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.