Latent dirichlet allocation
Reads0
Chats0
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
Proceedings ArticleDOI
Animals on the Web
Tamara L. Berg,David Forsyth +1 more
TL;DR: This work demonstrates a method for identifying images containing categories of animals using a clustering method applied to text on web pages and shows unequivocal evidence that visual information improves performance for this task.
Proceedings ArticleDOI
Mining correlated bursty topic patterns from coordinated text streams
TL;DR: A general probabilistic algorithm which can effectively discover correlated bursty patterns and their bursty periods across text streams even if the streams have completely different vocabularies is proposed, which can be applied to any coordinated text streams to discover correlated topic patterns that burst in multiple streams in the same period.
Journal ArticleDOI
Probabilistic Topic Models for Learning Terminological Ontologies
TL;DR: A new approach for automatic learning of terminological ontologies from text corpus based on probabilistic topic models, which shows that the method outperforms other methods in terms of recall and precision measures.
Proceedings ArticleDOI
Modeling Interestingness with Deep Neural Networks
TL;DR: In this article, the authors use deep neural networks to learn deep semantic models (DSM) of "interestingness" in click transitions between source and target documents derived from web browser logs, which can be used for contextual entity search, automatic text highlighting, prefetching documents of likely interest, automated content recommendation, automated advertisement placement, etc.
Journal ArticleDOI
Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications
TL;DR: Nonnegative matrix factorization (NMF) aims to factor a data matrix into low-rank latent factor matrices with nonnegativity constraints with nonNegativity constraints.
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.