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•

Argument Mining: A Survey

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John Lawrence¹, Chris Reed¹•Institutions (1)

University of Dundee¹

16 Jan 2020-Computational Linguistics

TL;DR: The techniques that establish the foundations for argument mining are explored, a review of recent advances in argument mining techniques are provided, and the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general are discussed.

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Abstract: Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structur...

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255 citations

Cites methods from "Latent dirichlet allocation"

...In Lawrence et al. (2014), a LDA topic model is used to determine the topical similarity of consecutive propositions in a piece of text....
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...This same approach is implemented in Lawrence and Reed (2015), where the use of LDA topic models is replaced by using WordNet39 to determine the semantic similarity between propositions....
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...The results are comparable to those achieved using LDA, with precision of 0.82 and recall of 0.56....
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...The output from the LDA algorithm is then post-processed using a minimal seeding of predefined argumentative words to determine argument and domain topics....
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...This work is further developed in Nguyen and Litman (2015), where the same methodology and data set are used, but a Latent Dirichlet Allocation (LDA) (Blei, Ng, and Jordan 2003) topic model is first generated to separate argument and domain keywords....
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Proceedings Article•

Finding Bursty Topics from Microblogs

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Qiming Diao¹, Jing Jiang¹, Feida Zhu¹, Ee-Peng Lim¹•Institutions (1)

Singapore Management University¹

08 Jul 2012

TL;DR: A topic model that simultaneously captures two observations is proposed that helps find event-driven posts on microblogs and helps identify and filter out "personal" posts.

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Abstract: Microblogs such as Twitter reflect the general public's reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find topics that have bursty patterns on microblogs, we propose a topic model that simultaneously captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) posts published by the same user are more likely to have the same topic. The former helps find event-driven posts while the latter helps identify and filter out "personal" posts. Our experiments on a large Twitter dataset show that there are more meaningful and unique bursty topics in the top-ranked results returned by our model than an LDA baseline and two degenerate variations of our model. We also show some case studies that demonstrate the importance of considering both the temporal information and users' personal interests for bursty topic detection from microblogs.

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255 citations

Cites methods from "Latent dirichlet allocation"

...To discover topics, we can certainly apply standard topic models such as LDA (Blei et al., 2003), but with standard LDA temporal information is lost during topic discovery....
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Proceedings Article•DOI•

Unsupervised discovery of visual object class hierarchies

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Josef Sivic¹, Bryan Russell¹, Andrew Zisserman², William T. Freeman³, Alyosha Efros⁴ - Show less +1 more•Institutions (4)

École Normale Supérieure¹, University of Oxford², Massachusetts Institute of Technology³, Carnegie Mellon University⁴

23 Jun 2008

TL;DR: This work proposes to group visual objects using a multi-layer hierarchy tree that is based on common visual elements by adapting to the visual domain the generative hierarchical latent Dirichlet allocation (hLDA) model previously used for unsupervised discovery of topic hierarchies in text.

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Abstract: Objects in the world can be arranged into a hierarchy based on their semantic meaning (e.g. organism - animal - feline - cat). What about defining a hierarchy based on the visual appearance of objects? This paper investigates ways to automatically discover a hierarchical structure for the visual world from a collection of unlabeled images. Previous approaches for unsupervised object and scene discovery focused on partitioning the visual data into a set of non-overlapping classes of equal granularity. In this work, we propose to group visual objects using a multi-layer hierarchy tree that is based on common visual elements. This is achieved by adapting to the visual domain the generative hierarchical latent Dirichlet allocation (hLDA) model previously used for unsupervised discovery of topic hierarchies in text. Images are modeled using quantized local image regions as analogues to words in text. Employing the multiple segmentation framework of Russell et al. [22], we show that meaningful object hierarchies, together with object segmentations, can be automatically learned from unlabeled and unsegmented image collections without supervision. We demonstrate improved object classification and localization performance using hLDA over the previous non-hierarchical method on the MSRC dataset [33].

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255 citations

Cites methods from "Latent dirichlet allocation"

...Here we use the classification overlap score to compare the object hierarchy learned from the MSRC-B1 dataset, shown in figure 5, with partitions of the data obtained by the standard LDA model [6, 22, 25] with varying number of topics....
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...We begin by briefly reviewing the Latent Dirichlet Allocation (LDA) topic discovery model [6, 12] and then describe its extension to tree structured topic hierarchies [5]....
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...This model is a generalization of the (flat) LDA [6] model....
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Proceedings Article•DOI•

Cascade: crowdsourcing taxonomy creation

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Lydia B. Chilton¹, Greg Little, Darren Edge², Daniel S. Weld¹, James A. Landay¹ - Show less +1 more•Institutions (2)

University of Washington¹, Microsoft²

27 Apr 2013

TL;DR: Cascade is an automated workflow that allows crowd workers to spend as little at 20 seconds each while collectively making a taxonomy, and it is shown that on three datasets its quality is 80-90% of that of experts.

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Abstract: Taxonomies are a useful and ubiquitous way of organizing information. However, creating organizational hierarchies is difficult because the process requires a global understanding of the objects to be categorized. Usually one is created by an individual or a small group of people working together for hours or even days. Unfortunately, this centralized approach does not work well for the large, quickly changing datasets found on the web. Cascade is an automated workflow that allows crowd workers to spend as little at 20 seconds each while collectively making a taxonomy. We evaluate Cascade and show that on three datasets its quality is 80-90% of that of experts. Cascade has a competitive cost to expert information architects, despite taking six times more human labor. Fortunately, this labor can be parallelized such that Cascade will run in as fast as four minutes instead of hours or days.

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255 citations

Cites methods from "Latent dirichlet allocation"

...Clustering and Taxonomy Creation Machine learning algorithms, such as LDA [4], can automatically cluster data....
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...Second, their performance is bad; lacking common sense, LDA often creates incoherent clusters....
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...Despite recent progress, completely automated methods, such as Latent Dirichlet Allocation (LDA) and related AI techniques, produce low-quality taxonomies....
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...Machine learning algorithms, such as LDA [4], can automatically cluster data....
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Proceedings Article•

Natural Language Understanding

[...]

Anna Maria Di Sciullo¹•Institutions (1)

Université du Québec à Montréal¹

19 Jul 2009

TL;DR: I focus on three characteristics of natural language understanding systems that incorporate the properties that make humans able to understand language naturally and how these systems handle recursion.

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Abstract: I focus on three characteristics of natural language understanding systems that incorporate the properties that make humans able to understand language naturally. The first characteristic of such systems is that they handle recursion. A second property of these systems is that they process abstract hierarchical structures, and they are not limited to the processing of strings of characters or keywords. A third characteristic is that they connect physical forms and interpretations.

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254 citations

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References

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Book•

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

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Milton Abramowitz, Irene A. Stegun

01 Jan 1970

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)....
[...]
...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.

...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

[...]

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