Latent dirichlet allocation

doi:10.5555/944919.944937

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Latent dirichlet allocation

Journal Article•DOI•

Latent dirichlet allocation

David M. Blei¹, Andrew Y. Ng², Michael I. Jordan¹•Institutions (2)

University of California, Berkeley¹, Stanford University²

01 Mar 2003-Journal of Machine Learning Research (JMLR.org)-Vol. 3, pp 993-1022

TL;DR: 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.

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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.

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Proceedings Article•DOI•

Equip tourists with knowledge mined from travelogues

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Qiang Hao¹, Rui Cai², Changhu Wang², Rong Xiao², Jiangming Yang², Yanwei Pang¹, Lei Zhang² - Show less +3 more•Institutions (2)

Tianjin University¹, Microsoft²

26 Apr 2010

TL;DR: This paper proposes a probabilistic topic model, named as Location-Topic model, which has the advantages of differentiability between two kinds of topics, i.e., local topics which characterize locations and global topics which represent other common themes shared by various locations.

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Abstract: With the prosperity of tourism and Web 2.0 technologies, more and more people have willingness to share their travel experiences on the Web (e.g., weblogs, forums, or Web 2.0 communities). These so-called travelogues contain rich information, particularly including location-representative knowledge such as attractions (e.g., Golden Gate Bridge), styles (e.g., beach, history), and activities (e.g., diving, surfing). The location-representative information in travelogues can greatly facilitate other tourists' trip planning, if it can be correctly extracted and summarized. However, since most travelogues are unstructured and contain much noise, it is difficult for common users to utilize such knowledge effectively. In this paper, to mine location-representative knowledge from a large collection of travelogues, we propose a probabilistic topic model, named as Location-Topic model. This model has the advantages of (1) differentiability between two kinds of topics, i.e., local topics which characterize locations and global topics which represent other common themes shared by various locations, and (2) representation of locations in the local topic space to encode both location-representative knowledge and similarities between locations. Some novel applications are developed based on the proposed model, including (1) destination recommendation for on flexible queries, (2) characteristic summarization for a given destination with representative tags and snippets, and (3) identification of informative parts of a travelogue and enriching such highlights with related images. Based on a large collection of travelogues, the proposed framework is evaluated using both objective and subjective evaluation methods and shows promising results.

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178 citations

Proceedings Article•DOI•

Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations

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Niklas Jakob¹, Stefan Hagen Weber², Mark-Christoph Müller¹, Iryna Gurevych¹•Institutions (2)

Technische Universität Darmstadt¹, Siemens²

06 Nov 2009

TL;DR: It is shown that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations and the opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.

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Abstract: In this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative filtering. Each of these approaches requires different amounts of manual interaction. We collected a data set of reviews with corresponding ordinal (star) ratings of several thousand movies to evaluate the different features for the collaborative filtering. We employ a state-of-the-art collaborative filtering engine for the recommendations during our evaluation and compare the performance with and without using the features representing user preferences mined from the free-text reviews provided by the users. The opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.

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178 citations

Cites methods from "Latent dirichlet allocation"

...Since it allows to control the number of clusters produced and since it has been successfully applied to several tasks in the past, we decided to employ Latent Dirichlet Allocation [4] for the clustering....
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...Since it allows to control the number of clusters produced and since it has been successfully applied to several tasks in the past, we decided to employ Latent Dirichlet Allocation [4] for the clustering....
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Proceedings Article•

Mixed Membership Stochastic Blockmodels

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Edoardo M. Airoldi¹, David M. Blei¹, Stephen E. Fienberg², Eric P. Xing²•Institutions (2)

Princeton University¹, Carnegie Mellon University²

08 Dec 2008

TL;DR: This paper describes a latent variable model of such data called the mixed membership stochastic blockmodel, which extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation.

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Abstract: In many settings, such as protein interactions and gene regulatory networks, collections of author-recipient email, and social networks, the data consist of pair-wise measurements, e.g., presence or absence of links between pairs of objects. Analyzing such data with probabilistic models requires non-standard assumptions, since the usual independence or exchangeability assumptions no longer hold. In this paper, we introduce a class of latent variable models for pairwise measurements: mixed membership stochastic blockmodels. Models in this class combine a global model of dense patches of connectivity (blockmodel) with a local model to instantiate node-specific variability in the connections (mixed membership). We develop a general variational inference algorithm for fast approximate posterior inference. We demonstrate the advantages of mixed membership stochastic blockmodel with applications to social networks and protein interaction networks.

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178 citations

Cites background or methods from "Latent dirichlet allocation"

...(2) A number of approxiate inference algorithms for mixed membership models have appeared in recent years, including mean-field variational methods (Blei et al., 2003; Teh et al., 2007), expectation propagation (Minka and Lafferty, 2002), and Monte Carlo Markov chain sampling (MCMC) (Erosheva and…...
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...Unfortunately, a closed form solution for the approximate maximum likelihood estimate of ~α does not exist Blei et al. (2003)....
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...Mixed membership models, such as latent Dirichlet allocation [1], have emerged in recent years as a flexible modeling tool for data where the single group assumption is violated by the heterogeneity within a unit of analysis—e....
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...Mixed membership models, such as latent Dirichlet allocation (Blei et al., 2003), have emerged in recent years as a flexible modeling tool for data where the single cluster assumption is violated by the heterogeneity within of a data point....
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...They have been successfully applied in many domains, such as document analysis (Minka and Lafferty, 2002; Blei et al., 2003; Buntine and Jakulin, 2006), surveys (Berkman et al., 1989; Erosheva, 2002), image processing (Li and Perona, 2005), transcriptional regulation (Airoldi et al., 2006b), and…...
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Proceedings Article•DOI•

A topic-based approach for narrowing the search space of buggy files from a bug report

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Anh Tuan Nguyen¹, Tung Thanh Nguyen¹, Jafar M. Al-Kofahi¹, Hung Viet Nguyen¹, Tien N. Nguyen¹ - Show less +1 more•Institutions (1)

Iowa State University¹

06 Nov 2011

TL;DR: An automated approach to help developers reduce efforts by narrowing the search space of buggy files when they are assigned to address a bug report by developing a specialized topic model that represents technical aspects as topics in the textual contents of bug reports and source files.

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Abstract: Locating buggy code is a time-consuming task in software development. Given a new bug report, developers must search through a large number of files in a project to locate buggy code. We propose BugScout, an automated approach to help developers reduce such efforts by narrowing the search space of buggy files when they are assigned to address a bug report. BugScout assumes that the textual contents of a bug report and that of its corresponding source code share some technical aspects of the system which can be used for locating buggy source files given a new bug report. We develop a specialized topic model that represents those technical aspects as topics in the textual contents of bug reports and source files, and correlates bug reports and corresponding buggy files via their shared topics. Our evaluation shows that BugScout can recommend buggy files correctly up to 45% of the cases with a recommended ranked list of 10 files.

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178 citations

Cites background or methods from "Latent dirichlet allocation"

...In this paper, we extend Latent Dirichlet Allocation (LDA) [4] to model the relation among a bug report and its corresponding buggy source files....
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...In LDA, a document is considered to be generated by a “machine” which is driven via parameters by the hidden factors called topics, and its words are taken from some vocabulary [4]....
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...Let us describe the B-component in our BugScout model, which is extended from LDA [4]....
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...S-component in BugScout is adopted from LDA [4]....
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...5: Illustration of LDA [4]...
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Proceedings Article•

Collapsed Variational Inference for HDP

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Yee Whye Teh¹, Kenichi Kurihara², Max Welling³•Institutions (3)

University College London¹, Tokyo Institute of Technology², University of California, Irvine³

03 Dec 2007

TL;DR: This work obtains the first variational algorithm to deal with the hierarchical Dirichlet process and with hyperparameters ofDirichlet variables, and shows a significant improvement in accuracy.

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Abstract: A wide variety of Dirichlet-multinomial 'topic' models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in such models, variational techniques have certain advantages such as easy assessment of convergence, easy optimization without the need to maintain detailed balance, a bound on the marginal likelihood, and side-stepping of issues with topic-identifiability. The most accurate variational technique thus far, namely collapsed variational latent Dirichlet allocation, did not deal with model selection nor did it include inference for hyperparameters. We address both issues by generalizing the technique, obtaining the first variational algorithm to deal with the hierarchical Dirichlet process and to deal with hyperparameters of Dirichlet variables. Experiments show a significant improvement in accuracy.

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178 citations

Cites background or methods from "Latent dirichlet allocation"

...Finally, for all LDA algorithms we used α = 0.1, π = 1/K. 2We actually set these values using a fixed but somewhat elaborate scheme which is the reason they ended up different for each dataset....
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...We found that the CV-HDP performs significantly better than the CV-LDA on both test-set likelihood and the variational bound....
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...Our approach is an extension of the collapsed VB approximation for LDA (CV-LDA) presented in [7], and represents the first VB approximation to the HDP1....
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...The number of topics used in CV-HDP was truncated at 40, 80, and 120 topics, corresponding to the number of topics used in the LDA algorithms....
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...For LDA and its cousins, there are alternatives based on variational Bayesian (VB) approximations [3] and on expectation propagation (EP) [5]....
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References

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Handbook of mathematical functions : with formulas, graphs, and mathematical tables

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Milton Abramowitz, Irene A. Stegun

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17,608 citations

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Bayesian Data Analysis

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Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin¹ - Show less +2 more•Institutions (1)

University of California, Irvine¹

01 Jan 1995

TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.

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Abstract: FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.

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16,079 citations

"Latent dirichlet allocation" refers background in this paper

...Finally, Griffiths and Steyvers (2002) have presented a Markov chain Monte Carlo algorithm for LDA....
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...Structures similar to that shown in Figure 1 are often studied in Bayesian statistical modeling, where they are referred to ashierarchical models(Gelman et al., 1995), or more precisely asconditionally independent hierarchical models(Kass and Steffey, 1989)....
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...Structures similar to that shown in Figure 1 are often studied in Bayesian statistical modeling, where they are referred to as hierarchical models (Gelman et al., 1995), or more precisely as conditionally independent hierarchical models (Kass and Steffey, 1989)....
[...]

Journal Article•DOI•

Indexing by Latent Semantic Analysis

[...]

Scott Deerwester¹, Susan T. Dumais², George W. Furnas², Thomas K. Landauer², Richard A. Harshman³ - Show less +1 more•Institutions (3)

University of Chicago¹, Telcordia Technologies², University of Western Ontario³

01 Sep 1990-Journal of the Association for Information Science and Technology

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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Abstract: A new method for automatic indexing and retrieval is described. The approach is 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. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.

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12,443 citations

"Latent dirichlet allocation" refers methods in this paper

...To address these shortcomings, IR researchers have proposed several other dimensionality reduction techniques, most notably latent semantic indexing (LSI) (Deerwester et al., 1990)....
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...To address these shortcomings, IR researchers have proposed several other dimensionality reduction techniques, most notablylatent semantic indexing (LSI)(Deerwester et al., 1990)....
[...]

Book•

Introduction to Modern Information Retrieval

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Gerard Salton, Michael J. McGill

01 Jan 1983

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.

...read moreread less

Abstract: Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd introduction to modern information retrieval as the choice of reading, you can find here.

...read moreread less

12,059 citations

"Latent dirichlet allocation" refers background or methods in this paper

...In the populartf-idf scheme (Salton and McGill, 1983), a basic vocabulary of “words” or “terms” is chosen, and, for each document in the corpus, a count is formed of the number of occurrences of each word....
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...We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model....
[...]

Book•

Theory of probability

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Harold Jeffreys, R. Bruce Lindsay

01 Jan 1939

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.

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Abstract: 1. Fundamental notions 2. Direct probabilities 3. Estimation problems 4. Approximate methods and simplifications 5. Significance tests: one new parameter 6. Significance tests: various complications 7. Frequency definitions and direct methods 8. General questions

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7,086 citations