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
Building emotional dictionary for sentiment analysis of online news
TL;DR: An efficient algorithm and three pruning strategies are proposed to automatically build a word-level emotional dictionary for social emotion detection and a method based on topic modeling is proposed to construct a topic-level dictionary, where each topic is correlated with social emotions.
Journal ArticleDOI
Word network topic model: a simple but general solution for short and imbalanced texts
Yuan Zuo,Jichang Zhao,Ke Xu +2 more
TL;DR: This work presents a word co-occurrence network-based model named WNTM, which successfully enhances the semantic density of data space without importing too much time or space complexity and demonstrates its potential in precisely discovering newly emerging topics or unexpected events in Weibo at pretty early stages.
Journal ArticleDOI
Learning representations of microbe–metabolite interactions
James T. Morton,Alexander A. Aksenov,Alexander A. Aksenov,Louis-Félix Nothias,Louis-Félix Nothias,James R. Foulds,Robert A. Quinn,Michelle H. Badri,Tami L. Swenson,Marc W. Van Goethem,Trent R. Northen,Trent R. Northen,Yoshiki Vázquez-Baeza,Mingxun Wang,Mingxun Wang,Nicholas A. Bokulich,Aaron Watters,Se Jin Song,Richard Bonneau,Pieter C. Dorrestein,Pieter C. Dorrestein,Rob Knight +21 more
TL;DR: In this paper, the conditional probability that each molecule is present given the presence of a specific microorganism was estimated by using neural networks to infer the interactions between microbially produced metabolites and inflammatory bowel disease.
Journal ArticleDOI
Mixture Models With a Prior on the Number of Components
TL;DR: The most commonly used method of inference for MFMs is reversible jump Markov chain Monte Carlo, but it can be nontrivial to design good reversible jump moves, especially in high-dimensional spaces as discussed by the authors.
Journal ArticleDOI
High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length
Nizar Bouguila,Djemel Ziou +1 more
TL;DR: This work considers the application of the minimum message length (MML) principle to determine the number of clusters in a finite mixture model based on the generalized Dirichlet distribution.
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.