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
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Journal ArticleDOI
Predicting crime using Twitter and kernel density estimation
TL;DR: This article uses Twitter-specific linguistic analysis and statistical topic modeling to automatically identify discussion topics across a major city in the United States and shows that the addition of Twitter data improves crime prediction performance versus a standard approach based on kernel density estimation.
Posted Content
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
TL;DR: A novel abstractive model is proposed which is conditioned on the article’s topics and based entirely on convolutional neural networks, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.
Proceedings ArticleDOI
Latent dirichlet allocation for tag recommendation
TL;DR: This paper introduces an approach based on Latent Dirichlet Allocation (LDA) for recommending tags of resources in order to improve search and shows that the approach achieves significantly better precision and recall than the use of association rules.
Journal ArticleDOI
A density-based method for adaptive LDA model selection
TL;DR: A method of adaptively selecting the best LDA model based on density is proposed, and experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics.
Proceedings Article
On smoothing and inference for topic models
TL;DR: In this article, the authors compare the performance of topic models with collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation, and find that the main differences are attributable to the amount of smoothing applied to the counts.
References
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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.
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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.
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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.