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New Technology of Library and Information Service  2014, Vol. 30 Issue (10): 63-69    DOI: 10.11925/infotech.1003-3513.2014.10.10
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A Survey of the Approach of Topic Evolution Model Based on Topic Model
Zhao Yingguang, Hong Na, An Xinying
Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
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Abstract  

[Objective] Organize and analyze the approachs of topic evolution model based on topic model, summary the advantages and disadvantages of all models, then introduce this methods into the fields of information analysis. [Coverage] The literatures are obtained from "Google Scholar" and "Web of Science" by the keywords/topics of "Topic/Theme Evolution"、"Time Topic Model" and "Dynamic Topic Model" together with citation searching, and 25 literatures are used as references at last. [Methods] Explore the implementation mechanism, functional characteristics, advantages and disadvantages and the fields of application by literature analysis. [Results] The current models focus on researching the variable topic number, online processing and continuous time span, many models have one or two functions and could meet most of the applications. [Limitations] Some specific implementations of the models are lack of depth analysis. [Conclusions] The task about evolution analysis of various text source, granularity and time spans should take account of the concrete requirement, so as to apply the appropriate model according to its features.

Key wordsTopic model      LDA      Topic evolution     
Received: 05 May 2014      Published: 28 November 2014
:  TP391  

Cite this article:

Zhao Yingguang, Hong Na, An Xinying. A Survey of the Approach of Topic Evolution Model Based on Topic Model. New Technology of Library and Information Service, 2014, 30(10): 63-69.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2014.10.10     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2014/V30/I10/63

[1] 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.
[2] Hofmann T. Probabilistic Latent Semantic Indexing [C]. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1999: 50-57.
[3] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3(4-5): 993-1022.
[4] Rosen-Zvi M, Griffiths T, Steyvers M, et al. The Author- Topic Model for Authors and Documents [C]. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2004.
[5] Blei D M, Lafferty J D. Correlated Topic Models [C]. In: Proceedings of the 23rd International Conference on Machine Learning. 2006.
[6] 单斌, 李芳. 基于 LDA 话题演化研究方法综述[J]. 中文信息学报, 2010, 24(6): 43-49. (Shan Bin, Li Fang. A Survey of Topic Evolution Based on LDA [J]. Journal of Chinese Information Processing, 2010, 24(6): 43-49.)
[7] Elshamy W S. Continuous-time Infinite Dynamic Topic Models [D]. Manhattan, Kansas: Kansas State University, 2013.
[8] Daud A, Li J, Zhou L, et al. Knowledge Discovery Through Directed Probabilistic Topic Models: A Survey [J]. Frontiers of Computer Science in China, 2010, 4(2): 280-301.
[9] Wang X, McCallum A. Topics Over Time: A Non-Markov Continuous-Time Model of Topical Trends [C]. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2006: 424-433.
[10] Ding W, Chen C. Dynamic Topic Detection and Tracking: A Comparison of HDP, C-word, and Cocitation Methods [J]. Journal of the Association for Information Science and Technology, 2014. DOI: 10.1002/asi.23134.
[11] Griffiths T L, Steyvers M. Finding Scientific Topics [J]. Proceedings of the National Academiy of Sciences of the United States of America, 2004, 101(S1): 5228-5235.
[12] Blei D M, Lafferty J D. Dynamic Topic Models [C]. In: Proceedings of the 23rd International Conference on Machine Learning. ACM, 2006: 113-120.
[13] 楚克明, 李芳. 基于 LDA 模型的新闻话题的演化[J]. 计算机应用与软件, 2011, 28(4): 4-7. (Chu Keming, Li Fang. LDA Model-Based News Topic Evolution [J]. Computer Applications and Software, 2011, 28(4): 4-7.)
[14] 胡吉明, 陈果. 基于动态LDA主题模型的内容主题挖掘与演化[J]. 图书情报工作, 2014, 58(2): 138-142. (Hu Jiming, Chen Guo. Mining and Evolution of Content Topics Based on Dynamic LDA [J]. Library and Information Service, 2014, 58(2): 138-142.)
[15] Ahmed A, Xing E P. Dynamic Non-Parametric Mixture Models and the Recurrent Chinese Restaurant Process: With Applications to Evolutionary Clustering [C]. In: Proceedings of the SIAM International Conference on Data Mining, Atlanta, Georgia, USA. 2008: 219-230.
[16] Ahmed A, Xing E P. Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream [C]. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2010.
[17] Teh Y W, Jordan M I, Beal M J, et al. Hierarchical Dirichlet Processes [J]. Journal of the American Statistical Association, 2004, 101(476): 1566-1581.
[18] Cui W, Liu S, Tan L, et al. Textflow: Towards Better Understanding of Evolving Topics in Text [J]. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(12): 2412-2421.
[19] Xu T, Zhang Z, Yu P S, et al. Dirichlet Process Based Evolutionary Clustering [C]. In: Proceedings of the 8th International Conference on Data Mining. 2008: 648-657.
[20] Wang C, Blei D, Heckerman D. Continuous Time Dynamic Topic Models [OL]. arXiv: 1206.3298.
[21] Wei X, Sun J, Wang X. Dynamic Mixture Models for Multiple Time-Series [C]. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India. 2007: 2909-2914.
[22] AlSumait L, Barbará D, Domeniconi C. On-Line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking [C]. In: Proceedings of the 8th IEEE International Conference on Data Mining. IEEE, 2008: 3-12.
[23] 胡艳丽, 白亮, 张维明. 一种话题演化建模与分析方法[J]. 自动化学报, 2012, 38(10): 1690-1697.(Hu Yanli, Bai Liang, Zhang Weiming. Modeling and Analyzing Topic Evolution [J]. Acta Automatica Sinica, 2012, 38(10): 1690-1697)
[24] Iwata T, Yamada T, Sakurai Y, et al. Online Multiscale Dynamic Topic Models [C]. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2010: 663-672.
[25] Wang C, Paisley J W, Blei D M. Online Variational Inference for the Hierarchical Dirichlet Process [C]. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. 2011: 752-760.

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