Topic model相关文章总结

基础类主题模型

Hofmann T. Probabilistic latent semantic indexing[C]//Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 1999: 50-57.

Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation[J]. Journal of machine Learning research, 2003, 3(Jan): 993-1022.

Steyvers M, Griffiths T. Probabilistic topic models[J]. Handbook of latent semantic analysis, 2007, 427(7): 424-440.

Heinrich G. Parameter Estimation for Text Analysis[J]. Technical Report, 2008.

Griffiths T. Gibbs sampling in the generative model of latent dirichlet allocation[J]. 2002.

LDA数学八卦

稀疏性主题模型

Wang C, Blei D M. Decoupling sparsity and smoothness in the discrete hierarchical dirichlet process[C]//Advances in neural information processing systems. 2009: 1982-1989.

Lin T, Tian W, Mei Q, et al. The dual-sparse topic model: mining focused topics and focused terms in short text[C]//Proceedings of the 23rd international conference on World wide web. ACM, 2014: 539-550.

Wang S, Chen Z, Fei G, et al. Targeted Topic Modeling for Focused Analysis[C]//KDD. 2016: 1235-1244.

Zhu J, Xing E P. Sparse topical coding[C]// Twenty-Seventh Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2011:831-838.

非参主题模型

DPMM

Neal R M. Markov chain sampling methods for Dirichlet process mixture models[J]. Journal of computational and graphical statistics, 2000, 9(2): 249-265.

Yu X. Gibbs Sampling Methods for Dirichlet Process Mixture Model: Technical Details[J]. 2009.

Blei D M, Jordan M I. Variational inference for Dirichlet process mixtures[J]. Bayesian analysis, 2006, 1(1): 121-143.

Huang R, Yu G, Wang Z, et al. Dirichlet process mixture model for document clustering with feature partition[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(8): 1748-1759.

HDP

Teh Y W, Jordan M I, Beal M J, et al. Sharing clusters among related groups: Hierarchical Dirichlet processes[C]//Advances in neural information processing systems. 2005: 1385-1392.

Wang C, Blei D M. Decoupling sparsity and smoothness in the discrete hierarchical dirichlet process[C]//Advances in neural information processing systems. 2009: 1982-1989.【引入词稀疏性】

Williamson S, Wang C, Heller K A, et al. The IBP compound Dirichlet process and its application to focused topic modeling[C]//Proceedings of the 27th international conference on machine learning (ICML-10). 2010: 1151-1158.

Nguyen V A, Boyd-Graber J, Resnik P, et al. Modeling topic control to detect influence in conversations using nonparametric topic models[J]. Machine Learning, 2014, 95(3): 381-421.【三层】

Blei D M, Griffiths T L, Jordan M I. The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies[J]. Journal of the ACM (JACM), 2010, 57(2): 7.【多层】

周建英, 王飞跃, 曾大军. 分层Dirichlet过程及其应用综述[J]. 自动化学报, 2011, 37(4):389-407.【中文介绍非参不错的文章】

与word embeddings结合

Das R, Zaheer M, Dyer C. Gaussian LDA for Topic Models with Word Embeddings[C]//ACL (1). 2015: 795-804.

Nguyen D Q, Billingsley R, Du L, et al. Improving topic models with latent feature word representations[J]. Transactions of the Association for Computational Linguistics, 2015, 3: 299-313.

Batmanghelich K, Saeedi A, Narasimhan K, et al. Nonparametric spherical topic modeling with word embeddings[J]. arXiv preprint arXiv:1604.00126, 2016.

Xun G, Li Y, Zhao W X, et al. A Correlated Topic Model Using Word Embeddings[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.[doi> 10.24963/ijcai. 2017/588]. 2017.

Xun G, Li Y, Gao J, et al. Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017: 535-543.

Correlated Topic Model

Lafferty J D, Blei D M. Correlated topic models[C]//Advances in neural information processing systems. 2006: 147-154.

Xun G, Li Y, Zhao W X, et al. A Correlated Topic Model Using Word Embeddings[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.[doi> 10.24963/ijcai. 2017/588]. 2017.

标签LDA

Ramage D, Hall D, Nallapati R, et al. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1. Association for Computational Linguistics, 2009: 248-256.

Ramage D, Manning C D, Dumais S. Partially labeled topic models for interpretable text mining[C]//Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2011: 457-465.

其他主题模型

Jo Y, Oh A H. Aspect and sentiment unification model for online review analysis[C]//Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011: 815-824.【Sentence-LDA】

Cheng X, Yan X, Lan Y, et al. Btm: Topic modeling over short texts[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(12): 2928-2941.【BTM短文本处理】

Hoang T A, Lim E P. Modeling Topics and Behavior of Microbloggers: An Integrated Approach[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2017, 8(3): 44.【类似于引入背景词】

Rosen-Zvi M, Griffiths T, Steyvers M, et al. The author-topic model for authors and documents[C]//Proceedings of the 20th conference on Uncertainty in artificial intelligence. AUAI Press, 2004: 487-494.【author-topic model 】

Titov I, McDonald R. Modeling online reviews with multi-grain topic models[C]//Proceedings of the 17th international conference on World Wide Web. ACM, 2008: 111-120.【类似于背景词】