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
Journal ArticleDOI
Transparency and deliberation within the FOMC: a computational linguistics approach
TL;DR: This paper exploited a natural experiment in the Federal Open Market Committee in 1993 together with computational linguistic models (particularly Latent Dirichlet Allocation) to measure the effect of increased transparency on debate.
Book
Text mining : applications and theory
Michael W. Berry,Jacob Kogan +1 more
TL;DR: This work focuses on text mining and cybercrime, and on the development of nonnegative matrix factorization for email classification problems using NMF-based classification methods.
Proceedings ArticleDOI
Source Code Retrieval for Bug Localization Using Latent Dirichlet Allocation
TL;DR: In this article, the authors present an LDA-based static technique for bug localization based on the latent Dirichlet allocation (LDA) model, which has significant advantages over both LSI and probabilistic LSI.
Book
Expertise Retrieval
TL;DR: This survey highlights advances in models and algorithms relevant to expertise retrieval as an emerging subdiscipline in information retrieval and draws connections among methods proposed in the literature and summarizes them in five groups of basic approaches.
Proceedings ArticleDOI
Opinion integration through semi-supervised topic modeling
Yue Lu,ChengXiang Zhai +1 more
TL;DR: This paper formally defines this new integration problem and proposes to use semi-supervised topic models to solve the problem in a principled way and can be used to integrate a well written review with opinions in an arbitrary text collection about any topic to potentially support many interesting applications in multiple domains.
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.