Open Access
Parameter estimation for text analysis
Gregor Heinrich
- 01 Jan 2009
TL;DR: Presents parameter estimation methods common with discrete proba- bility distributions, which is of particular interest in text modeling, and central concepts like conjugate distributions and Bayesian networks are reviewed.
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Abstract: Presents parameter estimation methods common with discrete proba- bility distributions, which is of particular interest in text modeling. Starting with maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks are reviewed. As an application, the model of latent Dirichlet allocation (LDA) is explained in detail with a full derivation of an approximate inference algorithm based on Gibbs sampling, in- cluding a discussion of Dirichlet hyperparameter estimation. Finally, analysis methods of LDA models are discussed.
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Citations
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
A biterm topic model for short texts
Xiaohui Yan,Jiafeng Guo,Yanyan Lan,Xueqi Cheng +3 more
- 13 May 2013
TL;DR: The approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics, and is found that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model.
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Learning to classify short and sparse text & web with hidden topics from large-scale data collections
Xuan-Hieu Phan,Le-Minh Nguyen,Susumu Horiguchi +2 more
- 21 Apr 2008
TL;DR: A general framework for building classifiers that deal with short and sparse text & Web segments by making the most of hidden topics discovered from large-scale data collections that is general enough to be applied to different data domains and genres ranging from Web search results to medical text.
A dirichlet multinomial mixture model-based approach for short text clustering
Jianhua Yin,Jianyong Wang +1 more
- 24 Aug 2014
TL;DR: This paper proposed a collapsed Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture model for short text clustering and found that GSDMM can infer the number of clusters automatically with a good balance between the completeness and homogeneity of the clustering results, and is fast to converge.
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A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
Mehdi Allahyari,Seyedamin Pouriyeh,Mehdi Assefi,Saeid Safaei,Elizabeth D. Trippe,Juan B. Gutierrez,Krys J. Kochut +6 more
TL;DR: Several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering are described, which briefly explain text mining in biomedical and health care domains.
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References
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Inference of population structure using multilocus genotype data
TL;DR: Pritch et al. as discussed by the authors proposed a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations, which can be applied to most of the commonly used genetic markers, provided that they are not closely linked.
•Proceedings Article
Latent Dirichlet Allocation
David M. Blei,Andrew Y. Ng,Michael I. Jordan +2 more
- 03 Jan 2001
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.