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
Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products
TL;DR: Novel insights available through Twitter for tobacco surveillance are attested through the high prevalence of positive sentiment, correlated in complex ways with social image, personal experience, and recently popular products such as hookah and electronic cigarettes.
Journal ArticleDOI
On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing
Chris Ding,Tao Li,Wei Peng +2 more
TL;DR: It is shown that PLSI and NMF (with the I-divergence objective function) optimize the same objective function, although PLSi andNMF are different algorithms as verified by experiments.
Proceedings ArticleDOI
A Thousand Frames in Just a Few Words: Lingual Description of Videos through Latent Topics and Sparse Object Stitching
TL;DR: This paper proposes a hybrid system consisting of a low level multimodal latent topic model for initial keyword annotation, a middle level of concept detectors and a high level module to produce final lingual descriptions that captures the most relevant contents of a video in a natural language description.
Proceedings Article
Gaia: geo-distributed machine learning approaching LAN speeds
Kevin Hsieh,Aaron Harlap,Nandita Vijaykumar,Dimitris Konomis,Gregory R. Ganger,Phillip B. Gibbons,Onur Mutlu +6 more
TL;DR: A new, general geo-distributed ML system, Gaia, is introduced that decouples the communication within a data center from the communication between data centers, enabling different communication and consistency models for each.
Proceedings Article
TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction
TL;DR: This paper presents TopicRank, a graph-based keyphrase extraction method that relies on a topical representation of the document and significantly outperforms state-of-the-art methods on three datasets.
References
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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.