Latent dirichlet allocation
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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
Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)
TL;DR: A computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM) derives a new representation of medical objects by embedding them in a low-dimensional vector space that facilitates algebraic and statistical manipulations such as projection onto 2D plane, object grouping, and risk stratification.
Proceedings ArticleDOI
Dirt Cheap Web-Scale Parallel Text from the Common Crawl
Jason Smith,Herve Saint-Amand,Magdalena Plamada,Philipp Koehn,Chris Callison-Burch,Chris Callison-Burch,Adam Lopez +6 more
TL;DR: This large-scale experiment brings web-scale parallel text to the masses by mining the Common Crawl, a public Web crawl hosted on Amazon’s Elastic Cloud using an open-source extension of the STRAND algorithm.
Journal ArticleDOI
Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods
TL;DR: Two alternative methods for retaining keywords that match expert selection much better and reveal the research specialization of the domain with more details are introduced: TF-inverse document frequency (TF-IDF) and TF-Keyword Activity Index (TF -KAI).
Journal ArticleDOI
An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit
TL;DR: This study evaluates several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit, and shows that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures.
Journal ArticleDOI
Mining Mobile User Preferences for Personalized Context-Aware Recommendation
TL;DR: This article develops two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios and show that both approaches are effective and outperform baselines with respect to mining personal context- aware preferences for mobile users.
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.