Latent Dirichlet Allocation

By Hazel Victoria Campbell, Abram Hindle, Eleni Stroulia. 2003

2,266 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Materials Chemistry, Natural Language Processing, Probabilistic & Bayesian Methods

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Citations in the corpus

Learning Visual Representations with Caption Annotations (2020, methodology): “ound-truth concept vectors can be easily obtained by considering the most frequent bi-grams [32] or even n-grams [37] in captions. More sophisticated ways to obtain articial labels include using LDA [5] to discover latent topics in captions [20]. In addition to these existing methods, we look for wa”
Variational Inference: A Review for Statisticians (2016, methodology): “…al. 2007; Liang, Jordan, and Klein 2009), grammar induction (Kurihara and Sato 2006; Naseem et al. 2010; Cohen and Smith 2010), models of streaming text (Yogatama et al. 2014), topic modeling (Blei, Ng, and Jordan 2003), and hidden Markov models and part-of-speech tagging (Wang and Blunsom 2013).”
Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN (2018, methodology): “For example, Latent Dirichlet Allocation [4] has been used to extract “topics” from corpus, and then represent documents in the topic space.”
Topic Aware Neural Response Generation (2016, background): “Twitter LDA belongs to the family of probabilistic topic models (Blei, Ng, and Jordan 2003) and represents the state-of-the-art topic model for short texts (Zhao et al. 2011).”
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo (2015, methodology): “In the past few decades, the Bayesian approach has been intensively used in modelling speeches (Rabiner, 1989), text documents (Blei et al., 2003), images/videos (Fei-Fei & Perona, 2005), social networks (Airoldi et al., 2009), brain activity (Penny et al., 2011), and is often considered gold…”
A Comprehensive Survey of Deep Learning for Image Captioning (2018, methodology): “of an image at multiple scales. This technique can extract proper visual information of a particular object. For extracting scene specific context, it first uses the Latent Dirichlet Allocation (LDA) [14] for generating a dictionary from all the captions of the dataset. Then a multilayer perceptron ”
Graph-Sparse LDA: A Topic Model with Structured Sparsity (2014, methodology): “edusing the “bagof words” representationthat is commonfor topic models. Let the data Xconsist of the counts of each of the V words in the vocabulary for each of the Ndocuments. The standard LDA model [1] posits the following generative process for the words win comprising each document (data instanc”
Deep and Hierarchical Implicit Models (2017, methodology): “Global variables can be used to pool information across data points for hierarchical regression (Gelman & Hill, 2006), topic models (Blei et al., 2003), and Bayesian nonparametrics (Teh & Jordan, 2010).”
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge (2016, background): “These mechanisms may be explicitly stated, as in Latent Dirichlet Allocation (LDA) (Blei et al., 2003), or implicitly stated, as with matrix factorization techniques (Hofmann, 1999; Ding et al., 2008; Buntine and Jakulin, 2006).”
Social Media Text Classification under Negative Covariate Shift (2015, methodology): “, 2000) or LDA (Blei et al., 2003), supervised learning usually give much higher accuracy.”
MMALFM (2018, methodology): “mation Systems, Vol. 1, No. 1, Article 1. Publication date: January 2019. MMALFM: Explainable Recommendation by Leveraging Reviews and Images 1:7 2.3 Multi-modal Topic Model Topic models, such as LDA [8], have achieved great success in single modality scenarios, and thus they have been extended to s”
Turning big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering (2013, neutral): “Constrained versions of this problem (that are usually NP-hard) include the nonnegative matrix factorization (NNMF) [2], when the j-subspace should be spanned by positive vectors, and Latent Dirichlet Allocation (LDA) [3] which generalizes NNMF by assuming a more general prior distribution that defi”
PageRank beyond the Web (2014, methodology): “For the ﬁrst question, Jockers [2012] deﬁnes a complicated distance metric between books using topic modeling ideas from latent Dirichlet allocation [Blei et al., 2003].”
A clinical text classification paradigm using weak supervision and deep representation (2019, methodology): “To derive document representation using topic modeling, we employed Latent Dirichlet Allo-cation (LDA) [55], and set the number of topics as 100 to be consistent with the dimension of word embeddings.”
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning (2017, methodology): “found that DNNs outperform a traditional Bag of Words technique (42, 43) if provided sufficient training data (30).”
Topic Modeling for Short Texts with Auxiliary Word Embeddings (2016, methodology): “Conventional topic modeling techniques, e.g., pLSA and LDA, are widely used to infer latent topical structure from text corpus [2, 12].”
Caste in the News: A Computational Analysis of Indian Newspapers (2019, methodology): “Particularly in our case study, LDA generated a set of topics, each represented by a set of key-words, from which we propose to deduce the general subject of the written discourse.”