Results 211 to 220 of about 13,047 (256)
Collective Latent Dirichlet Allocation
2008 Eighth IEEE International Conference on Data Mining, 2008 In 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 ...
Zhiyong Shen, Jun Sun, Yi-Dong Shen
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Enriched Latent Dirichlet Allocation for Sentiment Analysis
Expert Systems, 2020AbstractOne of the main benefits of unsupervised learning is that there is no need for labelled data. As a method of this category, latent Dirichlet allocation (LDA) estimates the semantic relations between the words of the text effectively and can play an important role in solving various issues, including emotional analysis in combination with other ...
Amjad Osmani +2 more
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Bug localization using latent Dirichlet allocation
Information and Software Technology, 2010Context: Some recent static techniques for automatic bug localization have been built around modern information retrieval (IR) models such as latent semantic indexing (LSI). Latent Dirichlet allocation (LDA) is a generative statistical model that has significant advantages, in modularity and extensibility, over both LSI and probabilistic LSI (pLSI ...
Nicholas A Kraft, Letha H Etzkorn
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Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation
The Stata Journal, 2018 This paper introduces the ldagibbs command which implements Latent Dirichlet Allocation in Stata. Latent Dirichlet Allocation is the most popular machine learning topic model. Topic models automatically cluster text documents into a user chosen number of
Carlo Schwarz
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Distributed Latent Dirichlet Allocation on Streams
ACM Transactions on Knowledge Discovery from Data, 2021Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge ...
Yunyan Guo, Jianzhong Li 0001
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Authorship Attribution with Latent Dirichlet Allocation
2022The problem of authorship attribution – attributing texts to their original authors – has been an active research area since the end of the 19th century, attracting increased interest in the last decade. Most of the work on authorship attribution focuses on scenarios with only a few candidate authors, but recently considered cases with tens to ...
Yanir Seroussi +2 more
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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.
David M. Blei +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.
David M. Blei +2 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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Slow mixing for Latent Dirichlet Allocation
Statistics & Probability Letters, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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