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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Journal Article•DOI•

Social media analytics – Challenges in topic discovery, data collection, and data preparation

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Stefan Stieglitz¹, Milad Mirbabaie¹, Björn Ross¹, Christoph Neuberger²•Institutions (2)

University of Duisburg-Essen¹, Ludwig Maximilian University of Munich²

01 Apr 2018-International Journal of Information Management

TL;DR: An extended and structured literature analysis is conducted through which the most important challenges for researchers are discussed and potential solutions proposed and used to extend an existing framework on social media analytics.

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492 citations

Proceedings Article•DOI•

Simultaneous image classification and annotation

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Chong Wang¹, David M. Blei¹, Fei-Fei Li¹•Institutions (1)

Princeton University¹

20 Jun 2009

TL;DR: A new probabilistic model for jointly modeling the image, its class label, and its annotations is developed, which derives an approximate inference and estimation algorithms based on variational methods, as well as efficient approximations for classifying and annotating new images.

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Abstract: Image classification and annotation are important problems in computer vision, but rarely considered together. Intuitively, annotations provide evidence for the class label, and the class label provides evidence for annotations. For example, an image of class highway is more likely annotated with words “road,” “car,” and “traffic” than words “fish,” “boat,” and “scuba.” In this paper, we develop a new probabilistic model for jointly modeling the image, its class label, and its annotations. Our model treats the class label as a global description of the image, and treats annotation terms as local descriptions of parts of the image. Its underlying probabilistic assumptions naturally integrate these two sources of information. We derive an approximate inference and estimation algorithms based on variational methods, as well as efficient approximations for classifying and annotating new images. We examine the performance of our model on two real-world image data sets, illustrating that a single model provides competitive annotation performance, and superior classification performance.

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490 citations

Cites methods from "Latent dirichlet allocation"

...As for LDA, computing this posterior exactly is not possible [4]....
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...We build on recent machine learning and computer vision research in probabilistic topic models, such as latent Dirichlet allocation (LDA) [4] and probabilistic latent semantic indexing [10] (pLSI)....
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Journal Article•DOI•

An architecture for parallel topic models

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Alexander J. Smola¹, Shravan Narayanamurthy²•Institutions (2)

Australian National University¹, Yahoo!²

01 Sep 2010

TL;DR: This paper describes a high performance sampling architecture for inference of latent topic models on a cluster of workstations and shows that this architecture is entirely general and that it can be extended easily to more sophisticated latent variable models such as n-grams and hierarchies.

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Abstract: This paper describes a high performance sampling architecture for inference of latent topic models on a cluster of workstations. Our system is faster than previous work by over an order of magnitude and it is capable of dealing with hundreds of millions of documents and thousands of topics.The algorithm relies on a novel communication structure, namely the use of a distributed (key, value) storage for synchronizing the sampler state between computers. Our architecture entirely obviates the need for separate computation and synchronization phases. Instead, disk, CPU, and network are used simultaneously to achieve high performance. We show that this architecture is entirely general and that it can be extended easily to more sophisticated latent variable models such as n-grams and hierarchies.

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489 citations

Cites background from "Latent dirichlet allocation"

...Latent Dirichlet Allocation (LDA) [3] and related topic models are particularly useful when it comes to infer overall groups of information (e....
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...For a much more detailed discussion see [3, 6]....
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Proceedings Article•DOI•

Learning to Match using Local and Distributed Representations of Text for Web Search

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Bhaskar Mitra¹, Fernando Diaz², Nick Craswell²•Institutions (2)

University College London¹, Microsoft²

03 Apr 2017

TL;DR: This work proposes a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that Matching with distributed representations complements matching with traditional local representations.

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Abstract: Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space. In traditional information retrieval models, on the other hand, terms have discrete or local representations, and the relevance of a document is determined by the exact matches of query terms in the body text. We hypothesize that matching with distributed representations complements matching with traditional local representations, and that a combination of the two is favourable. We propose a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that matches the query and the document using learned distributed representations. The two networks are jointly trained as part of a single neural network. We show that this combination or 'duet' performs significantly better than either neural network individually on a Web page ranking task, and significantly outperforms traditional baselines and other recently proposed models based on neural networks.

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489 citations

Cites background from "Latent dirichlet allocation"

...LDA-based document models combine exact matching with inexact matching [39]....
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...In contrast, latent semantic analysis (LSA) [5], probabilistic latent semantic analysis (PLSA) [14] and latent Dirichlet allocation (LDA) [2, 39] learn lowdimensional vector representations of terms, and match the query against the document in the latent semantic space....
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Journal Article•DOI•

A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases

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Justin Grimmer¹•Institutions (1)

Harvard University¹

21 Dec 2010-Political Analysis

TL;DR: A statistical model is introduced that attends to the structure of political rhetoric when measuring expressed priorities: statements are naturally organized by author to simultaneously estimate the topics in the texts, as well as the attention political actors allocate to the estimated topics.

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Abstract: Political scientists lack methods to efficiently measure the priorities political actors emphasize in statements. To address this limitation, I introduce a statistical model that attends to the structure of political rhetoric when measuring expressed priorities: statements are naturally organized by author. The expressed agenda model exploits this structure to simultaneously estimate the topics in the texts, as well as the attention political actors allocate to the estimated topics. I apply the method to a collection of over 24,000 press releases from senators from 2007, which I demonstrate is an ideal medium to measure how senators explain their work in Washington to constituents. A set of examples validates the estimated priorities and demonstrates their usefulness for testing theories of how members of Congress communicate with constituents. The statistical model and its extensions will be made available in a forthcoming free software package for the R computing language.

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484 citations

Cites methods or result from "Latent dirichlet allocation"

...Unfortunately, a closed form for the shape parameters a does not exist, so we use a Newton-Raphson algorithm, developed in Blei et al. (2003) to perform the optimization....
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...The derivation throughout this appendix is a fairly standard in the application of variational inference to mixture models and therefore should have similarities to the derivations in Bishop (2006) and Blei et al. (2003). Note, that I provide these derivations because variational inference in political science is nonstandard....
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References

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Book•

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

[...]

Milton Abramowitz, Irene A. Stegun

01 Jan 1970

17,608 citations

Book•

Bayesian Data Analysis

[...]

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.

...read moreread less

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)....
[...]
...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)....
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Book•

Introduction to Modern Information Retrieval

[...]

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.

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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.

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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

[...]

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