Correlated Topic Models

By David M. Blei, John D. Lafferty. 2005

618 citations recorded by the source corpus.

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Citations in the corpus

Assumed Density Filtering Methods for Learning Bayesian Neural Networks (2016, methodology): “We cope by upper bounding the offending expectation (Blei and Lafferty 2006),”
Statistical topic models for multi-label document classification (2011, background): “ution that merely reﬂects relative label frequencies. We note that several models that represent or induce topic dependencies have been investigated in the past for unsupervised topic modeling (e.g., Blei and Lafferty (2005); Teh et al. (2004); Mimno et al. (2007); Blei et al. (2010)). Although thes”
Dynamic mixed membership blockmodel for evolving networks (2009, methodology): “In principle, we can use the dMMSB to capture not only membership correlation within and between actors at a specific time (as did in Blei and Laerty (2006a) ), but also dynamic coupling (or co-evolution) of the overall functional roles in the whole network via the covariance matrix '. In the simpl”
Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality (2014, methodology): “To measure the human-interpretability of topics, the authors proposed a word intrusion task and conducted experiments using three topic models: Latent Dirichlet Allocation (LDA: Blei et al. (2003)), Probabilistic Latent Semantic Indexing (PLSI: Hofmann (1999)) and the Correlated Topic Model (CTM: Bl”
On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations (2010, background): “Correlated Topic Models [ 12 ] is one such approach which tries to capture relation between topics using a covariance matrix.”
MedLDA: maximum margin supervised topic models (2012, methodology): “In fact, the likelihood component of MedLDA can be any other form of generative topic model, such as correlated topic models (Blei and Lafferty, 2005), or latent space Markov random fields, such as exponential family harmoniums (Welling et al.”
Structured Stochastic Variational Inference (2014, unknown): “ other hierarchical models. Unlike SVI, it can also address models without tractable local conditionals, such as multilevel logistic regressions (Gelman and Hill, 2007) or the correlated topic model (Blei and Lafferty, 2006). 2.2 Approximating Distribution Our goal is to approximate the intractable ”
Pachinko allocation: DAG-structured mixture models of topic correlations (2006, methodology): “Another model that captures the correlations among topics is the correlated topic model introduced by Blei and Laerty (2006) .”
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation (2015, methodology): “topics but also because inferring such correlations can significantly improve predictions, especially when inferring the remaining words in a document after only a few have been revealed [7].”
Max-margin Latent Dirichlet Allocation for Image Classification and Annotation (2011, background): “Examples of topic models include the probabilistic latent semantic analysis (pLSI) [10], latent Dirichlet allocation (LDA) [7], correlated topic models (CTM) [5], etc.”
Hidden Topic Markov Models (2007, background): “References [1] R.”
Towards Bayesian Deep Learning: A Survey (2016, background): “ other information or to perform other tasks. For example, after LDA, different variants of topic models based on it have been proposed. [6], [61] are proposed to incorporate temporal information and [5] extends LDA by assuming correlations among topics. [29] extends LDA from the batch mode to the o”
Cross-Lingual Latent Topic Extraction (2010, background): “, 2004), contextual topic analysis (Mei and Zhai, 2006), dynamic and correlated topic models (Blei and Lafferty, 2005; Blei and Lafferty, 2006), and opinion analysis (Mei et al.”
Topic modelling for qualitative studies (2017, background): “Correlated topic models (CTM) avoid this unrealistic assumption, admitting that some topics are closer to each other and share words with each other; CTMs use logistic normal distribution instead of Dirichlet to model correlations between topics [14].”
Joint latent topic models for text and citations (2008, methodology): “Most of the models in this framework such as Dynamic topic models [5, 15], Pachinko Allocation [11], Correlated Topic Model [3], etc.”
Covariance in Unsupervised Learning of Probabilistic Grammars (2010, methodology): “We begin by motivating the modeling of covariance among the probabilities of grammar derivation events, and propose the use of logistic normal distributions (Aitchison, 1986; Blei and Lafferty, 2006) over multinomials to build priors over grammars (Section 3).”