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2,842
Latent dirichlet allocation
 Journal of Machine Learning Research
, 2003
"... We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a threelevel 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, ..."
Abstract

Cited by 4365 (92 self)
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, 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
Choices, values and frames.
 American Psychologist,
, 1984
"... Making decisions is like speaking prosepeople do it all the time, knowingly or unknowingly. It is hardly surprising, then, that the topic of decision making is shared by many disciplines, from mathematics and statistics, through economics and political science, to sociology and psychology. The stu ..."
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Cited by 684 (9 self)
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Making decisions is like speaking prosepeople do it all the time, knowingly or unknowingly. It is hardly surprising, then, that the topic of decision making is shared by many disciplines, from mathematics and statistics, through economics and political science, to sociology and psychology
A Maximum Entropy Approach to Adaptive Statistical Language Modeling
 Computer, Speech and Language
, 1996
"... An adaptive statistical languagemodel is described, which successfullyintegrates long distancelinguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document's h ..."
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Cited by 293 (12 self)
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's history, we propose and use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from multiple sources must be combined. Traditionally, linear interpolation and its variants have been used
A Neural Network Approach to Topic Spotting
, 1995
"... This paper presents an application of nonlinear neural networks to topic spotting. Neural networks allow us to model higherorder interaction between document terms and to simultaneously predict multiple topics using shared hidden features. In the context of this model, we compare two approaches to d ..."
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Cited by 188 (1 self)
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document, the task is to output for each topic the probability that the topic is prese...
Probabilistic AuthorTopic Models for Information Discovery
 THE TENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING
, 2004
"... We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a twostage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probabilit ..."
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Cited by 173 (11 self)
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We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a twostage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a
Evaluation methods for topic models
 In ICML
, 2009
"... A natural evaluation metric for statistical topic models is the probability of heldout documents given a trained model. While exact computation of this probability is intractable, several estimators for this probability have been used in the topic modeling literature, including the harmonic mean me ..."
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Cited by 111 (10 self)
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A natural evaluation metric for statistical topic models is the probability of heldout documents given a trained model. While exact computation of this probability is intractable, several estimators for this probability have been used in the topic modeling literature, including the harmonic mean
Topic
, 2004
"... School completion Introduction and Subject Early childhood care and education/intervention programs have been shown to significantly enhance children’s prospects for academic success by reducing the probability of referral to special education, grade retention, and leaving school prior to high schoo ..."
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School completion Introduction and Subject Early childhood care and education/intervention programs have been shown to significantly enhance children’s prospects for academic success by reducing the probability of referral to special education, grade retention, and leaving school prior to high
Asking sensitive questions: The impact of data collection mode, question format, and question context
 PUBLIC OPINION QUARTERLY
, 1996
"... This study compared three methods of collecting survey data about sexual behaviors and other sensitive topics: computerassisted personal interviewing (CAPI), computerassisted selfadministered interviewing (CASI), and audio computerassisted selfadministered interviewing (ACASI). Interviews wer ..."
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Cited by 223 (12 self)
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This study compared three methods of collecting survey data about sexual behaviors and other sensitive topics: computerassisted personal interviewing (CAPI), computerassisted selfadministered interviewing (CASI), and audio computerassisted selfadministered interviewing (ACASI). Interviews
Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter. WWW
, 2011
"... ABSTRACT There is a widespread intuitive sense that different kinds of information spread differently online, but it has been difficult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment. Here we study this ..."
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Cited by 171 (4 self)
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this issue on Twitter, analyzing the ways in which tokens known as hashtags spread on a network defined by the interactions among Twitter users. We find significant variation in the ways that widelyused hashtags on different topics spread. Our results show that this variation is not attributable simply
Topics in Probability: Random Walks and
, 2005
"... What is a random walk? hard to define in general. Better start with an example. Here’s THE example from the founder of the field, George Polya: ”... he and his fiancee (would) also set out for a stroll in the woods, and then suddenly I met them there. And then I met them the same morning repeatedly, ..."
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taking a step north, south, east or west with equal probabilities every second. Will they meet? On Z2 this is equivalent to a single walker, asking if and how often he comes back. Definition. A Simple Random Walk on a graph G is a stochastic process xi with Prob(xi+1 = v) = 1 dxi for v ∼ xi, and 0
Results 1  10
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2,842