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
Sensing Trending Topics in Twitter
Luca Maria Aiello,Georgios Petkos,Carlos Martin,David Corney,Symeon Papadopoulos,R. Skraba,Ayse Göker,Ioannis Kompatsiaris,Alejandro Jaimes +8 more
TL;DR: It is found that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel.
Proceedings Article
Proximal Methods for Sparse Hierarchical Dictionary Learning
TL;DR: This work considers a tree-structured sparse regularization to learn dictionaries embedded in a hierarchy, thus providing a competitive alternative to probabilistic topic models.
Proceedings ArticleDOI
Checking app behavior against app descriptions
TL;DR: Applied on a set of 22,500+ Android applications, the CHABADA prototype identified several anomalies and flagged 56% of novel malware as such, without requiring any known malware patterns.
Proceedings Article
Continuous time dynamic topic models
TL;DR: The continuous time dynamic topic model (cDTM) as discussed by the authors is a variational approximate inference algorithm that takes advantage of the sparsity of observations in text, a property that lets us easily handle many time points.
Proceedings Article
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
TL;DR: This paper presents DiscLDA, a discriminative variation on Latent Dirichlet Allocation in which a class-dependent linear transformation is introduced on the topic mixture proportions, and obtains a supervised dimensionality reduction algorithm that uncovers the latent structure in a document collection while preserving predictive power for the task of classification.
References
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Bayesian Data Analysis
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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
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Harold Jeffreys,R. Bruce Lindsay +1 more
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