Topic-Based Generative Models for Text Information Access
暂无分享,去创建一个
[1] David Kauchak,et al. Modeling word burstiness using the Dirichlet distribution , 2005, ICML.
[2] Ata Kabán,et al. On an equivalence between PLSI and LDA , 2003, SIGIR.
[3] Inderjit S. Dhillon,et al. Clustering with Bregman Divergences , 2005, J. Mach. Learn. Res..
[4] Mark A. Girolami,et al. A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections , 2004, Journal of Intelligent Information Systems.
[5] Yee Whye Teh,et al. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation , 2006, NIPS.
[6] Michael I. Jordan,et al. Hierarchical Dirichlet Processes , 2006 .
[7] Xin Jin,et al. Web usage mining based on probabilistic latent semantic analysis , 2004, KDD.
[8] David Cohn,et al. Learning to Probabilistically Identify Authoritative Documents , 2000, ICML.
[9] Donna K. Harman,et al. Overview of the Fourth Text REtrieval Conference (TREC-4) , 1995, TREC.
[10] Michael I. Jordan,et al. A latent variable model for chemogenomic profiling , 2005, Bioinform..
[11] Max Welling,et al. Distributed Algorithms for Topic Models , 2009, J. Mach. Learn. Res..
[12] Slava M. Katz. Distribution of content words and phrases in text and language modelling , 1996, Natural Language Engineering.
[13] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[14] Pietro Perona,et al. Memory bounded inference in topic models , 2008, ICML '08.
[15] Thomas L. Griffiths,et al. The Author-Topic Model for Authors and Documents , 2004, UAI.
[16] Max Welling,et al. Deterministic Latent Variable Models and Their Pitfalls , 2008, SDM.
[17] CHENGXIANG ZHAI,et al. A study of smoothing methods for language models applied to information retrieval , 2004, TOIS.
[18] Wray L. Buntine. Estimating Likelihoods for Topic Models , 2009, ACML.
[19] L. J. Savage,et al. Symmetric measures on Cartesian products , 1955 .
[20] Charles Elkan,et al. Accounting for burstiness in topic models , 2009, ICML '09.
[21] Fabrizio Sebastiani,et al. Machine learning in automated text categorization , 2001, CSUR.
[22] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[23] Hal Daumé,et al. A geometric view of conjugate priors , 2010, Machine Learning.
[24] Thomas L. Griffiths,et al. The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies , 2007, JACM.
[25] John D. Lafferty,et al. Model-based feedback in the language modeling approach to information retrieval , 2001, CIKM '01.
[26] Sebastian Thrun,et al. Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.
[27] Michael I. Jordan,et al. Modeling annotated data , 2003, SIGIR.
[28] M. Meilă. Comparing clusterings---an information based distance , 2007 .
[29] Michael I. Jordan,et al. DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification , 2008, NIPS.
[30] Thomas L. Griffiths,et al. Probabilistic author-topic models for information discovery , 2004, KDD.
[31] Wray L. Buntine. Variational Extensions to EM and Multinomial PCA , 2002, ECML.
[32] John D. Lafferty,et al. A correlated topic model of Science , 2007, 0708.3601.
[33] L. Azzopardi,et al. Topic based language models for ad hoc information retrieval , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[34] Wei Li,et al. Pachinko allocation: DAG-structured mixture models of topic correlations , 2006, ICML.
[35] Andrew McCallum,et al. A comparison of event models for naive bayes text classification , 1998, AAAI 1998.
[36] C. Goutte,et al. Co-Occurrence Models in Music Genre Classification , 2005, 2005 IEEE Workshop on Machine Learning for Signal Processing.
[37] J. Dickey. Multiple Hypergeometric Functions: Probabilistic Interpretations and Statistical Uses , 1983 .
[38] Jean-Cédric Chappelier,et al. PLSI: The True Fisher Kernel and beyond , 2009, ECML/PKDD.
[39] Andrew McCallum,et al. Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email , 2007, J. Artif. Intell. Res..
[40] Éric Gaussier,et al. Relation between PLSA and NMF and implications , 2005, SIGIR '05.
[41] Ramesh Nallapati,et al. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora , 2009, EMNLP.
[42] Daniel Gatica-Perez,et al. Modeling Semantic Aspects for Cross-Media Image Indexing , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Kris Popat,et al. A Hierarchical Model for Clustering and Categorising Documents , 2002, ECIR.
[44] Tom Minka,et al. Expectation-Propogation for the Generative Aspect Model , 2002, UAI.
[45] P. Donnelly,et al. Inference of population structure using multilocus genotype data. , 2000, Genetics.
[46] Gregor Heinrich,et al. A Generic Approach to Topic Models , 2009, ECML/PKDD.
[47] Aleks Jakulin,et al. Discrete Component Analysis , 2005, SLSFS.
[48] David R. Karger,et al. Tackling the Poor Assumptions of Naive Bayes Text Classifiers , 2003, ICML.
[49] Charles Elkan,et al. Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution , 2006, ICML.
[50] ChengXiang Zhai,et al. A mixture model for contextual text mining , 2006, KDD '06.
[51] Daniel Gatica-Perez,et al. PLSA-based image auto-annotation: constraining the latent space , 2004, MULTIMEDIA '04.
[52] Andrew Zisserman,et al. Scene Classification Via pLSA , 2006, ECCV.
[53] Thomas Hofmann,et al. Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.
[54] Max Welling,et al. Asynchronous distributed estimation of topic models for document analysis , 2011, Statistical Methodology.
[55] Thomas L. Griffiths,et al. A probabilistic approach to semantic representation , 2019, Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society.
[56] Christopher Joseph Pal,et al. Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification , 2006, AAAI.
[57] Ramesh Nallapati,et al. Joint latent topic models for text and citations , 2008, KDD.
[58] Alexander Zien,et al. Semi-Supervised Learning , 2006 .
[59] Yee Whye Teh,et al. On Smoothing and Inference for Topic Models , 2009, UAI.
[60] Charles Elkan,et al. Deriving TF-IDF as a Fisher Kernel , 2005, SPIRE.
[61] François Yvon,et al. Using LDA to detect semantically incoherent documents , 2008, CoNLL.
[62] Andrew McCallum,et al. Rethinking LDA: Why Priors Matter , 2009, NIPS.
[63] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[64] D. K. Harmon,et al. Overview of the Third Text Retrieval Conference (TREC-3) , 1996 .
[65] John F. Canny,et al. GaP: a factor model for discrete data , 2004, SIGIR '04.
[66] David D. Lewis,et al. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.
[67] Éric Gaussier,et al. The BNB Distribution for Text Modeling , 2008, ECIR.
[68] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[69] Jean-Cédric Chappelier,et al. Revisiting Fisher Kernels for Document Similarities , 2006, ECML.
[70] ChengXiang Zhai,et al. Statistical Language Models for Information Retrieval: A Critical Review , 2008, Found. Trends Inf. Retr..
[71] Aleks Jakulin,et al. Applying Discrete PCA in Data Analysis , 2004, UAI.
[72] David M. Pennock,et al. Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments , 2001, UAI.
[73] W. Bruce Croft,et al. LDA-based document models for ad-hoc retrieval , 2006, SIGIR.
[74] Thomas L. Griffiths,et al. Hierarchical Topic Models and the Nested Chinese Restaurant Process , 2003, NIPS.
[75] Mark Steyvers,et al. Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[76] Hanna M. Wallach,et al. Topic modeling: beyond bag-of-words , 2006, ICML.
[77] Thomas L. Griffiths,et al. Integrating Topics and Syntax , 2004, NIPS.
[78] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.
[79] Jean-Cédric Chappelier,et al. An Ad Hoc Information Retrieval Perspective on PLSI through Language Model Identification , 2009, ICTIR.