Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (4): 131-138.doi: 10.6040/j.issn.1672-3961.0.2021.311
SUN Zhiwei1, SONG Mingyang1, PAN Zehua2, JING Liping1*
CLC Number:
[1] BLEI D M. Probabilistic topic models[J]. Commun-ications of the ACM, 2012, 55(4): 77-84. [2] MEI Q, LING X, WONDRA M, et al. Topic sentiment mixture: modeling facets and opinions in weblogs[C] //Proceedings of the 16th international conference on World Wide Web. New York, USA: Association for Computing Machinery, 2007: 171-180. [3] GUO W, WU S, WANG L, et al. Social-relational topic model for social networks[C] //Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. New York, USA: Association for Computing Machinery, 2015: 1731-1734. [4] PENNACCHIOTTI M, GURUMURTHY S. Investigating topic models for social media user recommendation[C] //Proceedings of the 20th International Conference Companion on World Wide Web. New York, USA: Association for Computing Machinery, 2011: 101-102. [5] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. The Journal of Machine Learning Research, 2003, 3: 993-1022. [6] CHIEN J T, LEE C H, TAN Z H. Latent Dirichlet mixture model[J]. Neurocomputing, 2018, 278: 12-22. [7] AGARWAL D, CHEN B C. fLDA: matrix factorization through latent dirichlet allocation[C] //Proceedings of the Third ACM International Conference on Web Search and Data Mining. New York, USA: Association for Computing Machinery, 2010: 91-100. [8] 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, 2010, 57(2): 1-30. [9] KORSHUNOVA I, XIONG H, FEDORYSZAK M, et al. Discriminative topic modeling with logistic LDA[C] //Proceedings of the 33rd International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2019: 6770-6780. [10] NG A Y, JORDAN M I. On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes[C] //Proceedings of the 14th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2001: 841-848. [11] CAO Z, LI S, LIU Y, et al. A novel neural topic model and its supervised extension[C] //Proceedings of the 29th AAAI Conference on Artificial Intelligence. Menlo Park, USA: AAAI Press, 2015: 2210-2216. [12] MENG Y, HUANG J, WANG G, et al. Discriminative topic mining via category-name guided text embedding[C] //Proceedings of the 29th Web Conference. New York, USA: Association for Computing Machinery, 2020: 2121-2132. [13] DEERWESTER S, DUMAIS S T, FURNAS G W, et al. Indexing by latent semantic analysis[J]. Journal of the American society for information science, 1990, 41(6): 391-407. [14] HOFMANN T. Probabilistic latent semantic indexing[C] //Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: Association for Computing Machinery, 1999: 50-57. [15] BLEI D M, LAFFERTY J D. Correlated topic models[C] //Proceedings of the 18th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2005: 147-154. [16] BLEI D M, JORDAN M I. Modeling annotated data[C] //Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: Association for Computing Machinery, 2003: 127-134. [17] WANG X, MCCALLUM A. Topics over time: a non-markov continuous-time model of topical trends[C] //Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2006: 424-433. [18] MIMNO D M, MCCALLUM A. Topic models conditioned on arbitrary features with Dirichlet-multinomial regression[C] //Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence. Corvallis, USA: AUAI Press, 2008: 411-418. [19] BLEI D M, MCAULIFFE J D. Supervised topic models[C] //Proceedings of the 20th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2007: 121-128. [20] LACOSTE-JULIEN S, SHA F, JORDAN M I. DiscLDA: Discriminative learning for dimensionality reduction and classification[C] //Proceedings of the 21st International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2008: 897-904. [21] MIAO Y, YU L, BLUNSOM P. Neural variational inference for text processing[C] //Proceedings of the 33rd International Conference on Machine Learning. New York, USA: Association for Computing Machinery, 2016: 1727-1736. [22] MIAO Y, GREFENSTETTE E, BLUNSOM P. Discovering discrete latent topics with neural variational inference[C] //Proceedings of the 34th International Conference on Machine Learning. New York, USA: Association for Computing Machinery, 2017: 2410-2419. [23] SRIVASTAVA A, SUTTON C. Autoencoding variational inference for topic models[C] //Proceedings of the International Conference on Learning Representations. Toulon, France, 2017: 1-12. [24] KINGMA D P, WELLING M. Auto-encoding variational Bayes[C] //Proceedings of the International Conference on Learning Representations(ICLR). Banff, Canada, 2014: 12-14. [25] REZENDE D J, MOHAMED S, WIERSTRA D. Stochastic backpropagation and approximate inference in deep generative models[C] //Proceedings of the 31st International Conference on Machine Learning. New York, USA: Association for Computing Machinery, 2014: 1278-1286. [26] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. [27] KIM Y. Convolutional neural networks for sentence classification[C] //Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association of Computational Linguistics, 2014: 1746-1751. [28] JOHNSON R, ZHANG T. Effective use of word order for text categorization with convolutional neural networks[C] //Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, USA: Association of Computational Linguistics, 2015: 103-112. [29] PENNINGTON J, SOCHER R, MANNING C D. Glove: global vectors for word representation[C] //Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association of Computational Linguistics, 2014: 1532-1543. [30] BESAG J. Spatial interaction and the statistical analysis of lattice systems[J]. Journal of the Royal Statistical Society: Series B(Methodological), 1974, 36(2): 192-225. [31] CARDOSO-CACHOPO A. Improving methods for single-label text categorization[D]. Lisbon: Technical University of Lisbon, 2007. [32] LAU J H, NEWMAN D, BALDWIN T. Machine reading tea leaves: automatically evaluating topic coherence and topic model quality[C] //Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, USA: Association of Computational Linguistics, 2014: 530-539. [33] HOFFMAN M, BACH F, BLEI D M. Online learning for latent dirichlet allocation[C] //Proceedings of the 23rd International Conference on Neural Information Processing Systems. Cambridge, UK: MIT Press, 2010: 856-864. [34] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. [35] DAI A M, LE Q V. Semi-supervised sequence learning[C] //Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge, UK: MIT Press, 2015: 3079-3087. |
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