Latent dirichlet allocation
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.
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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TL;DR: This work introduces psychologists to social media language research, identifying descriptive and predictive analyses that language data allow and describes how raw language data can be accessed and quantified for inclusion in subsequent analyses, exploring personality as expressed on Facebook to illustrate.
Abstract: Language data available through social media provide opportunities to study people at an unprecedented scale. However, little guidance is available to psychologists who want to enter this area of research. Drawing on tools and techniques developed in natural language processing, we first introduce psychologists to social media language research, identifying descriptive and predictive analyses that language data allow. Second, we describe how raw language data can be accessed and quantified for inclusion in subsequent analyses, exploring personality as expressed on Facebook to illustrate. Third, we highlight challenges and issues to be considered, including accessing and processing the data, interpreting effects, and ethical issues. Social media has become a valuable part of social life, and there is much we can learn by bringing together the tools of computer science with the theories and insights of psychology. (PsycINFO Database Record
154 citations
TL;DR: A topic oriented community detection approach which combines both social objects clustering and link analysis, which can achieve a better performance when the topics are at least as important as the links to the analysis.
Abstract: Community detection is an important issue in social network analysis. Most existing methods detect communities through analyzing the linkage of the network. The drawback is that each community identified by those methods can only reflect the strength of connections, but it cannot reflect the semantics such as the interesting topics shared by people. To address this problem, we propose a topic oriented community detection approach which combines both social objects clustering and link analysis. We first use a subspace clustering algorithm to group all the social objects into topics. Then we divide the members that are involved in those social objects into topical clusters, each corresponding to a distinct topic. In order to differentiate the strength of connections, we perform a link analysis on each topical cluster to detect the topical communities. Experiments on real data sets have shown that our approach was able to identify more meaningful communities. The quantitative evaluation indicated that our approach can achieve a better performance when the topics are at least as important as the links to the analysis.
154 citations
Cites background from "Latent dirichlet allocation"
...There are several topic models, such as pLSI [26], LDA [27], AT [28], etc. Interactive applications or social network analysis motivate research on topic modeling or topicbased community detection....
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...There are several topic models, such as pLSI [26], LDA [27], AT [28], etc....
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Posted Content•
TL;DR: A hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference is presented that reduces the bias of variational inference and generalizes to many Bayesian hidden-variable models.
Abstract: We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational inference and generalizes to many Bayesian hidden-variable models.
154 citations
Cites background from "Latent dirichlet allocation"
...Topic models are hierarchical Bayesian models of document collections (Blei et al., 2003)....
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Proceedings Article•
19 Jun 2014TL;DR: Extensive experiments with ML algorithms for topic modeling, collaborative filtering, and PageRank show that both approaches significantly increase convergence speeds, behaving similarly when there are no stragglers, but SSP outperforms BSP in the presence ofstragglers.
Abstract: Many modern machine learning (ML) algorithms are iterative, converging on a final solution via many iterations over the input data. This paper explores approaches to exploiting these algorithms' convergent nature to improve performance, by allowing parallel and distributed threads to use loose consistency models for shared algorithm state. Specifically, we focus on bounded staleness, in which each thread can see a view of the current intermediate solution that may be a limited number of iterations out-of-date. Allowing staleness reduces communication costs (batched updates and cached reads) and synchronization (less waiting for locks or straggling threads). One approach is to increase the number of iterations between barriers in the oft-used Bulk Synchronous Parallel (BSP) model of parallelizing, which mitigates these costs when all threads proceed at the same speed. A more flexible approach, called Stale Synchronous Parallel (SSP), avoids barriers and allows threads to be a bounded number of iterations ahead of the current slowest thread. Extensive experiments with ML algorithms for topic modeling, collaborative filtering, and PageRank show that both approaches significantly increase convergence speeds, behaving similarly when there are no stragglers, but SSP outperforms BSP in the presence of stragglers.
154 citations
Patent•
12 Dec 2007TL;DR: In this paper, a word is selected from a received text and features are identified from the word. The features are applied to a model to identify probabilities for sets of part-of-speech tags.
Abstract: A word is selected from a received text and features are identified from the word. The features are applied to a model to identify probabilities for sets of part-of-speech tags. The probabilities for the sets of part-of-speech tags are used to weight scores for possible part-of-speech tags for the selected word to form weighted scores. The weighted scores are used to select a part-of-speech tag for the word and the selected part of speech tag is stored or output. The scores for the possible part-of-speech tags are based on variational approximation parameters trained from a sparse prior over probability distributions describing the probability of a part-of-speech tag given a word.
153 citations
References
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Book•
01 Jan 1970
17,608 citations
Book•
01 Jan 1995TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
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.
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)....
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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.
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.
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•
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.
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.
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....
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Book•
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.
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
7,086 citations