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
Automatic tag recommendation algorithms for social recommender systems
Yang Song,Lu Zhang,C. Lee Giles +2 more
TL;DR: This article proposes two novel document-centered approaches that are capable of making effective and efficient tag recommendations in real scenarios and suggests that they can substantially improve the performance of tag recommendations when compared to the user-centered methods, as well as topic models LDA and SVM classifiers.
Proceedings ArticleDOI
Generative Modeling Using the Sliced Wasserstein Distance
TL;DR: This work considers an alternative formulation for generative modeling based on random projections which, in its simplest form, results in a single objective rather than a saddle-point formulation and finds its approach to be significantly more stable compared to even the improved Wasserstein GAN.
Proceedings Article
Improving Topic Coherence with Regularized Topic Models
TL;DR: This work proposes two methods to regularize the learning of topic models by creating a structured prior over words that reflect broad patterns in the external data that make topic models more useful across a broader range of text data.
Journal ArticleDOI
Researching Mental Health Disorders in the Era of Social Media: Systematic Review.
TL;DR: The scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research are explored.
Proceedings ArticleDOI
Detecting topic evolution in scientific literature: how can citations help?
TL;DR: An iterative topic evolution learning framework is proposed by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model, which clearly shows that citations can help to understand topic evolution better.
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.