Please wait a minute...
New Technology of Library and Information Service  2012, Vol. Issue (12): 58-65    DOI: 10.11925/infotech.1003-3513.2012.12.11
Current Issue | Archive | Adv Search |
Review on the LDA-based Techniques Detection for the Field Emerging Topic
Fan Yunman1,2, Ma Jianxia1
1. The Lanzhou Branch of National Science Library, Chinese Academy of Sciences, Lanzhou 730000, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China
Download: PDF(1147 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  Based on LDA,this paper reviews the development of the LDA model and several models which improve the LDA for the filed emerging topic detection.It describes two parameter inference algorithms of variational derivation and Gibbs sampling, and reviews the improvement of LDA in recent years,including the one modeling the evolution of the topics,the one modeling jointly with the content of document and meta data,the one with online learning, the topic evolution method combining LDA and citation analysis and so on;then compares and analyses different kinds of improvement models in details. The paper also reviews several main visualization techniques such as NIH-VB,TIARA and VxInsight. Finally,it discusses the key research problems of detecting the emerging topic by using LDA.
Key wordsTopic model      LDA      Citation analysis      Topical visualization     
Received: 15 October 2012      Published: 12 March 2013
:  TP393  

Cite this article:

Fan Yunman, Ma Jianxia. Review on the LDA-based Techniques Detection for the Field Emerging Topic. New Technology of Library and Information Service, 2012, (12): 58-65.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2012.12.11     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2012/V/I12/58

[1] Blei D M. Probabilistic Topic Models[J]. Communications of the ACM, 2012, 55(4): 77-84.
[2] Nigam K, Mccallum A K, Thrun S, et al. Text Classification from Labeled and Unlabeled Documents Using EM[J]. Machine Learning, 2000, 39(2-3): 103-134.
[3] Hofmann T. Probabilistic Latent Semantic Indexing[C]. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99). New York: ACM, 1999: 50-57.
[4] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[5] Jordan M I, Ghahramani Z, Jaakkola T S, et al. An Introduction to Variational Methods for Graphical Models[J]. Machine learning, 1999, 37(2): 183-233.
[6] Teh Y W, Newman D, Welling M. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation[C]. In: Proceedings of Neural Information Processing Systems. 2006: 1353-1360.
[7] Griffiths T. Gibbs Sampling in the Generative Model of Latent Dirichlet Allocation[OL]. [2012-06-09].http://people.cs.umass.edu/~wallach/courses/s11/cmpsci791ss/readings/griffiths02gibbs.pdf.
[8] Heinrich G. Parameter Estimation for Text Analysis[EB/OL]. [2012-06-09]. http://www. arbylon. net/publications/text-est. pdf.
[9] Wainwright M J, Jordan M I. Graphical Models, Exponential Families, and Variational Inference[J]. Foundations and Trends in Machine Learning, 2008,1 (1-2): 1-305.
[10] Ghahramani Z, Beal M J. Graphical Models and Variational Methods[A]. //Advanced Mean Field Methods:Theory and Practice[M]. Cambridge: MIT Press, 2001: 167-177.
[11] Blei D M, Lafferty J D. A Correlated Topic Model of Science[J]. Annals of Applied Statistics, 2007, 1(1):17-35.
[12] Aldous D J. Exchangeability and Related Topics[M].Berlin, Heidelberg: Springer, 1985: 1-198.
[13] Li W, Mccallum A. Pachinko Allocation: DAG-structured Mixture Models of Topic Correlations[C]. In: Proceedings of the 23rd International Conference on Machine Learning (ICML’06). New York: ACM, 2006: 577-584.
[14] Wang C, Blei D M. A Split-Merge MCMC Algorithm for the Hierarchical Dirichlet Process[J/OL]. Computing Research Repository. [2012-09-24]. http://arxiv.org/abs/1201.1657.
[15] 曹娟,张勇东,李锦涛,等. 一种基于密度的自适应最优LDA模型选择方法[J]. 计算机学报, 2008, 31(10): 1780-1787. (Cao Juan, Zhang Yongdong, Li Jintao, et al. A Method of Adaptively Selecting Best LDA Model Based on Density[J]. Chinese Journal of Computers, 2008, 31(10): 1780-1787.)
[16] Blei D M, Lafferty J D. Dynamic Topic Models[C]. In: Proceedings of the 23rd International Conference on Machine Learning (ICML’06). New York: ACM, 2006: 113-120.
[17] Wang C, Blei D M, Heckerman D. Continuous Time Dynamic Topic Models[C]. In: Proceedings of Uncertainty in Artificial Intelligence. 2008: 579-586.
[18] Wang X R, 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 (KDD’06). New York: ACM, 2006: 424-433.
[19] Wallach H M. Topic Modeling: Beyond Bag-of-words[C]. In: Proceedings of the 23rd International Conference on Machine Learning (ICML’06). New York: ACM, 2006: 977-984.
[20] Wang X R, McCallum A, Wei X. Topical N-grams: Phrase and Topic Discovery, with an Application to Information Retrieval[C]. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM’07). Washington, DC: IEEE Computer Society, 2007: 697-702.
[21] Wang X R, McCallum A. A Note onTopical N-grams[R]. 2005.
[22] Mann G S, Mimno D, McCallum A. Bibliometric Impact Measures Leveraging Topic Analysis[C]. In: Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL’06). New York: ACM, 2006: 65-74.
[23] 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 (UAI’04). Arlington: AUAI Press, 2004: 487-494.
[24] 王萍. 基于概率主题模型的文献知识挖掘[J]. 情报学报, 2011, 30(6): 583-590. (Wang Ping. Literature Knowledge Mining Based on Probabilistic Topic Model[J]. Journal of the China Society for Scientific and Technical Information, 2011, 30(6): 583-590.)
[25] Mimno D, McCallum A. Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression[C]. In: Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence (UAI’08). 2008: 411-418.
[26] Nallapati R M, Ahmed A, Xing E P, et al. Joint Latent Topic Models for Text and Citations[C]. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08). New York: ACM, 2008: 542-550.
[27] Tu Y N, Seng J L. Indices of Novelty for Emerging Topic Detection[J]. Information Processing & Management, 2012, 48(2): 303-325.
[28] Goodrum A A, McCain K W, Lawrence S, et al. Scholarly Publishing in the Internet Age: A Citation Analysis of Computer Science Literature[J]. Information Processing & Management, 2001, 37(5): 661-675.
[29] Web of Knowledge [DB/OL]. [2012-08-14]. http://apps.webofknowledge.com.
[30] 中华人民共和国国家知识产权局.专利检索[EB/OL]. [2012-08-14]. http://www.sipo.gov.cn/zljs/. (State Intellectual Property Office of PRC. Patent Retrieval[EB/OL]. [2012-08-14]. http://www.sipo.gov.cn/zljs/.)
[31] Dietz L, Bickel S, Scheffer T. Unsupervised Prediction of Citation Influences[C]. In: Proceedings of the 24th International Conference on Machine Learning (ICML’07). New York: ACM, 2007: 233-240.
[32] He Q, Chen B, Pei J, et al. Detecting Topic Evolution in Scientific Literature: How Can Citations Help[C]. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM’09). New York: ACM, 2009: 957-966.
[33] 贺亮, 李芳. 基于话题模型的科技文献话题发现和趋势分析[J]. 中文信息学报, 2012, 26(2): 109-115.(He Liang, Li Fang. Topic Discovery and Trend Analysis in Scientific Literature on Topic Model[J]. Journal of Chinese Information Processing, 2012, 26(2): 109-115.)
[34] 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. 2008: 3-12.
[35] Hoffman M D, Blei D M, Bach F. Online Learning for Latent Dirichlet Allocation[A]. //Lafferty J,Williams C K I,Shawe-Taylor J,et al. Advances in Neural Information Processing Systems[M].2010: 856-864.
[36] Banerjee A, Basu S. Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning[C]. In: Proceedings of SDM-SIAM International Conference on Data Mining. 2007.
[37] Herr B W, Talley E M, Burns G, et al. The NIH Visual Browser: An Interactive Visualization of Biomedical Research[C]. In: Proceedings of the 13th International Conference Information Visualization (IV’09). Washington D C: IEEE Computer Society, 2009: 505-509.
[38] Talley E M, Newman D, Mimno D, et al. Database of NIH Grants Using Machine-learned Categories and Graphical Clustering[J]. Nature Methods, 2011, 8(6): 443-444.
[39] Wei F R, Liu S X, Song Y Q, et al. TIARA: A Visual Exploratory Text Analytic System[C]. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10), Washington DC, USA. New York: ACM, 2010: 153-162.
[40] Boyack K W, Wylie B N, Davidson G S. Domain Visualization Using VxInsight? For Science and Technology Management[J]. Journal of the American Society for Information Science and Technology, 2002, 53 (9): 764-774.
[1] Lixin Xia,Jieyan Zeng,Chongwu Bi,Guanghui Ye. Identifying Hierarchy Evolution of User Interests with LDA Topic Model[J]. 数据分析与知识发现, 2019, 3(7): 1-13.
[2] Qingtian Zeng,Xiaohui Hu,Chao Li. Extracting Keywords with Topic Embedding and Network Structure Analysis[J]. 数据分析与知识发现, 2019, 3(7): 52-60.
[3] Peng Guan,Yuefen Wang,Zhu Fu. Analyzing Topic Semantic Evolution with LDA: Case Study of Lithium Ion Batteries[J]. 数据分析与知识发现, 2019, 3(7): 61-72.
[4] Bengong Yu,Yangnan Chen,Ying Yang. Classifying Short Text Complaints with nBD-SVM Model[J]. 数据分析与知识发现, 2019, 3(5): 77-85.
[5] Peiyao Zhang,Dongsu Liu. Topic Evolutionary Analysis of Short Text Based on Word Vector and BTM[J]. 数据分析与知识发现, 2019, 3(3): 95-101.
[6] Linna Xi,Yongxiang Dou. Examining Reposts of Micro-bloggers with Planned Behavior Theory[J]. 数据分析与知识发现, 2019, 3(2): 13-20.
[7] Jie Zhang,Junbo Zhao,Dongsheng Zhai,Ningning Sun. Patent Technology Analysis of Microalgae Biofuel Industrial Chain Based on Topic Model[J]. 数据分析与知识发现, 2019, 3(2): 52-64.
[8] Junwan Liu,Zhixin Long,Feifei Wang. Finding Collaboration Opportunities from Emerging Issues with LDA Topic Model and Link Prediction[J]. 数据分析与知识发现, 2019, 3(1): 104-117.
[9] Guijun Yang,Xue Xu,Fuqiang Zhao. Predicting User Ratings with XGBoost Algorithm[J]. 数据分析与知识发现, 2019, 3(1): 118-126.
[10] Yue He,Yue Feng,Shupeng Zhao,Yufeng Ma. Recommending Contents Based on Zhihu Q&A Community: Case Study of Logistics Topics[J]. 数据分析与知识发现, 2018, 2(9): 42-49.
[11] Tao Zhang,Haiqun Ma. Clustering Policy Texts Based on LDA Topic Model[J]. 数据分析与知识发现, 2018, 2(9): 59-65.
[12] Yanhua Xu,Yujie Miao,Lin Miao,Xueqiang Lv. Generating HSK Writing Essays with LDA Model[J]. 数据分析与知识发现, 2018, 2(9): 80-87.
[13] Ziming Zeng,Qianwen Yang. Sentiment Analysis for Micro-blogs with LDA and AdaBoost[J]. 数据分析与知识发现, 2018, 2(8): 51-59.
[14] Beibei Pang,Juanqiong Gou,Wenxin Mu. Extracting Topics and Their Relationship from College Student Mentoring[J]. 数据分析与知识发现, 2018, 2(6): 92-101.
[15] Yan Yu,Naixuan Zhao. Weighted Topic Model for Patent Text Analysis[J]. 数据分析与知识发现, 2018, 2(4): 81-89.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn