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Dynamic topic models
- In ICML
, 2006
"... Scientists need new tools to explore and browse large collections of scholarly literature. Thanks to organizations such as JSTOR, which scan and index the original bound archives of many journals, modern scientists can search digital libraries spanning hundreds of years. A scientist, suddenly ..."
Abstract
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Cited by 681 (29 self)
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Scientists need new tools to explore and browse large collections of scholarly literature. Thanks to organizations such as JSTOR, which scan and index the original bound archives of many journals, modern scientists can search digital libraries spanning hundreds of years. A scientist, suddenly
The Author-Topic Model for Authors and Documents
"... We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial ..."
Abstract
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Cited by 366 (18 self)
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We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial
Probabilistic topic models
- IEEE Signal Processing Magazine
, 2010
"... Probabilistic topic models are a suite of algorithms whose aim is to discover the ..."
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Cited by 235 (6 self)
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Probabilistic topic models are a suite of algorithms whose aim is to discover the
Hierarchical topic models and the nested Chinese restaurant process
- Advances in Neural Information Processing Systems
, 2004
"... We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We take a Bayesian approach, generating an appropriate prior via a distribution on partitions that we refer to as the nested ..."
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Cited by 287 (32 self)
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We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We take a Bayesian approach, generating an appropriate prior via a distribution on partitions that we refer to as the nested
Evaluation methods for topic models
- In ICML
, 2009
"... A natural evaluation metric for statistical topic models is the probability of held-out 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 held-out 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
On smoothing and inference for topic models
- In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence
, 2009
"... Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation, and ..."
Abstract
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Cited by 119 (9 self)
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Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation
Topic modeling with network regularization
- In Proc. of the 17th WWW Conference
, 2008
"... In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data. The proposed method combines topic modeling and s ..."
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Cited by 102 (9 self)
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In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data. The proposed method combines topic modeling
Characterizing microblogs with topic models
- In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM 2010
, 2010
"... As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users ’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conf ..."
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Cited by 157 (4 self)
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model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.
Polylingual Topic Models
"... Topic models are a useful tool for analyzing large text collections, but have previously been applied in only monolingual, or at most bilingual, contexts. Meanwhile, massive collections of interlinked documents in dozens of languages, such as Wikipedia, are now widely available, calling for tools th ..."
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Cited by 89 (2 self)
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Topic models are a useful tool for analyzing large text collections, but have previously been applied in only monolingual, or at most bilingual, contexts. Meanwhile, massive collections of interlinked documents in dozens of languages, such as Wikipedia, are now widely available, calling for tools
Results 1 - 10
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20,733
