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Proceedings of the 2014 ACM conference on Web science, 2014
Topic modeling, in particular the Latent Dirichlet Allocation (LDA) model, has recently emerged as an important tool for understanding large datasets, in particular, user-generated datasets in social studies of the Web. In this work, we investigate the instability of LDA inference, propose a new metric of similarity between topics and a criterion of ...
Sergei Koltcov +2 more
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Topic modeling, in particular the Latent Dirichlet Allocation (LDA) model, has recently emerged as an important tool for understanding large datasets, in particular, user-generated datasets in social studies of the Web. In this work, we investigate the instability of LDA inference, propose a new metric of similarity between topics and a criterion of ...
Sergei Koltcov +2 more
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Collective Latent Dirichlet Allocation
2008 Eighth IEEE International Conference on Data Mining, 2008In this paper, we propose a new variant of latent Dirichlet allocation (LDA): Collective LDA (C-LDA), for multiple corpora modeling. C-LDA combines multiple corpora during learning such that it can transfer knowledge from one corpus to another; meanwhile it keeps a discriminative node which represents the corpus ID to constrain the learned topics in ...
Zhi-Yong Shen, Jun Sun, Yi-Dong Shen
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2002
Summary: 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.
Blei, David M. +2 more
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Summary: 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.
Blei, David M. +2 more
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Latent Dirichlet allocation based generative adversarial networks
Neural Networks, 2020Generative adversarial networks have been extensively studied in recent years and powered a wide range of applications, ranging from image generation, image-to-image translation, to text-to-image generation, and visual recognition. These methods typically model the mapping from latent space to image with single or multiple generators.
Lili Pan +6 more
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Multi-dependent Latent Dirichlet Allocation
2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2017Latent Dirichlet Allocation (LDA) is an attractive topic model research because LDA is so flexible for solving different problems. Because of its different core dependencies, it can be applied to many topics, such as emotion detection, information systems or image clustering.
Wei-Cheng Hsin, Jen-Wei Huang
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Topic Modeling Using Latent Dirichlet allocation
ACM Computing Surveys, 2021We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the enormous collection of documents by reducing them in a topical subspace.
Uttam Chauhan, Apurva Shah
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Slow mixing for Latent Dirichlet Allocation
Statistics & Probability Letters, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation
IEEE Geoscience and Remote Sensing Letters, 2010In this letter, we are interested in the annotation of large satellite images, using semantic concepts defined by the user. This annotation task combines a step of supervised classification of patches of the large image and the integration of the spatial information between these patches.
Lienou, Marie +2 more
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Labeled Phrase Latent Dirichlet Allocation
2016In recent years, topic modeling, such as Latent Dirichlet Allocation (LDA) and its variations, has been widely used to discover the abstract topics in text corpora. There are two state-of-the-art topic models: Labeled LDA (LLDA) and PhraseLDA. LLDA is a supervised generative model which considers the label information, but it does not take into ...
Yi-Kun Tang, Xian-Ling Mao, Heyan Huang
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Latent Dirichlet allocation-based temporal summarization
International Journal of Web Information Systems, 2019PurposeDuring crises such as accidents or disasters, an enormous volume of information is generated on the Web. Both people and decision-makers often need to identify relevant and timely content that can help in understanding what happens and take right decisions, as soon it appears online.
Ahmed Amir Tazibt, Farida Aoughlis
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