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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Proceedings ArticleDOI
Improving LDA topic models for microblogs via tweet pooling and automatic labeling
TL;DR: This paper empirically establishes that a novel method of tweet pooling by hashtags leads to a vast improvement in a variety of measures for topic coherence across three diverse Twitter datasets in comparison to an unmodified LDA baseline and a range of pooling schemes.
Proceedings Article
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes
TL;DR: The hierarchical Dirichlet process (HDP), a nonparametric Bayesian model for clustering problems involving multiple groups of data, is proposed and experimental results are reported showing the effective and superior performance of the HDP over previous models.
Proceedings ArticleDOI
Studying the History of Ideas Using Topic Models
TL;DR: Unsupervised topic modeling is applied to the ACL Anthology to analyze historical trends in the field of Computational Linguistics from 1978 to 2006, finding trends including the rise of probabilistic methods starting in 1988, a steady increase in applications, and a sharp decline of research in semantics and understanding between 1978 and 2001.
Proceedings ArticleDOI
Discovery of activity patterns using topic models
TL;DR: Experimental results show the ability of the approach to model and recognize daily routines without user annotation to be able to be used in this work.
Journal ArticleDOI
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective
TL;DR: This survey performs a comprehensive study of data collection from a data management point of view, providing a research landscape of these operations, guidelines on which technique to use when, and identify interesting research challenges.
References
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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.
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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.
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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.