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
The DARPA Twitter Bot Challenge
V. S. Subrahmanian,Amos Azaria,Skylar Durst,Vadim Kagan,Aram Galstyan,Kristina Lerman,Linhong Zhu,Emilio Ferrara,Alessandro Flammini,Filippo Menczer,Andrew Stevens,Alex Dekhtyar,Shuyang Gao,Tad Hogg,Farshad Kooti,Yan Liu,Onur Varol,Prashant Shiralkar,V. G. Vinod Vydiswaran,Qiaozhu Mei,Tim Hwang +20 more
TL;DR: The most recent DARPA Challenge as mentioned in this paper focused on identifying influence bots on a specific topic within Twitter, and three top-ranked teams were identified by the DARPA Social Media in Strategic Communications program.
Proceedings Article
Modeling User Rating Profiles For Collaborative Filtering
TL;DR: A generative latent variable model for rating-based collaborative filtering called the User Rating Profile model (URP), which represents each user as a mixture of user attitudes, and the mixing proportions are distributed according to a Dirichlet random variable.
Journal ArticleDOI
Structured sparsity through convex optimization
TL;DR: In this article, the authors consider situations where they are not only interested in sparsity, but where some structural prior knowledge is available as well, and show that the $\ell_1$-norm can then be extended to structured norms built on either disjoint or overlapping groups of variables.
Journal ArticleDOI
Accurate and Effective Latent Concept Modeling for Ad Hoc Information Retrieval
TL;DR: Nous proposons une methode non supervisee pour the modelisation of concepts implicites d’une requete, dans le but of recreer la representation conceptuelle du besoin d‘information initial.
Proceedings Article
Sparse Additive Generative Models of Text
TL;DR: This approach has two key advantages: it can enforce sparsity to prevent overfitting, and it can combine generative facets through simple addition in log space, avoiding the need for latent switching variables.
References
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Bayesian Data Analysis
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Journal ArticleDOI
Indexing by Latent Semantic Analysis
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Gerard Salton,Michael J. McGill +1 more
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Harold Jeffreys,R. Bruce Lindsay +1 more
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