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
More filters
Proceedings ArticleDOI
Improving bug localization using structured information retrieval
TL;DR: This work provides a thorough grounding of IR-based bug localization research in fundamental IR theoretical and empirical knowledge and practice and presents BLUiR, which embodies this insight, requires only the source code and bug reports, and takes advantage of bug similarity data if available.
Journal ArticleDOI
Methodologies for Cross-Domain Data Fusion: An Overview
TL;DR: High-level principles of each category of methods are introduced, and examples in which these techniques are used to handle real big data problems are given, to help a wide range of communities find a solution for data fusion in big data projects.
Proceedings Article
Topic models conditioned on arbitrary features with Dirichlet-multinomial regression
David Mimno,Andrew McCallum +1 more
TL;DR: A Dirichlet-multinomial regression topic model that includes a log-linear prior on document-topic distributions that is a function of observed features of the document, such as author, publication venue, references, and dates is proposed.
Proceedings ArticleDOI
Turning big data into tiny data: constant-size coresets for k-means, PCA and projective clustering
TL;DR: The authors' coresets with the merge-and-reduce approach obtain embarrassingly parallel streaming algorithms for problems such as k-means, PCA and projective clustering, and a simple recursive coreset construction that produces coresets of size.
Journal ArticleDOI
Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks.
TL;DR: The results show that productive authors tend to directly coauthor with and closely cite colleagues sharing the same research interests; they do not generally collaborate directly with colleagues having different research topics, but instead directly or indirectly cite them; and highly cited authors do not Generally co author with each other, but closely cite each other.
References
More filters
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.