下载：概率主题模型【PDF，PPT】

1. Thomas Hoffman and Jan Puzicha, 1999. Probabilistic Latent Semantic Indexing. Proceedings of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50-57.
2. Thomas Hofmann, 1999. Probabilistic Latent Semantic Analysis. Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI).
3. Thomas Hofmann, 2001. Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning, Vol. 42, No. 1-2, pp. 177-196.
4. Liangjie Hong, 2010. A Tutorial on Probabilistic Latent Semantic Analysis. Department of Computer Science and Engineering, Lehigh University.
5. David M. Blei, Andrew Y. Ng and Michael I. Jordan, 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research, Vol. 3, No. Jan, pp. 993-1022.
6. Thomas L. Griffiths and Mark Steyvers, 2004. Finding Scientific Topics. Proceedings of the National Academy of Sciences of the United States of America, Vol. 101, No. Suppl. 1, pp. 5228-5235.
7. Gregor Heinrich, 2009. Parameter Estimation for Text Analysis. Technical Report, Version 2.9. vsonix GmbH and University of Leipzig.
8. Shuo Xu, Qingwei Shi, Xiaodong Qiao, Lijun Zhu, Han Zhang, Hanmin Jung, Seungwoo Lee, and Sung-Pil Choi, 2014. A Dynamic Users’ Interest Discovery Model with Distributed Inference Algorithm. International Journal of Distributed Sensor Networks, Vol. 2014, pp. 1-11. code
9. 史庆伟, 乔晓东, 徐硕, 农国武, 2013. 作者主题演化模型及其在研究兴趣演化分析中的应用. 情报学报, Vol. 32, No. 9, pp. 912-919. code
10. Shuo Xu, Ling Li, Congcong Wang, Xin An, and Guancan Yang, 2022. An Improved Author-Topic (AT) Model with Authorship Credit Allocation Schemes. Journal of Information Science. Note
11. Xin An, Shuo Xu, Yali Wen, and Mingxing Hu, 2014. A Shared Interest Discovery Model for Co-author Relationship in SNS. International Journal of Distributed Sensor Networks, Vol. 2014, pp. 820715. codes
12. 张晗, 徐硕, 乔晓东, 2014. 融合科技文献内外部特征的主题模型发展综述. 情报学报, Vol. 33, No. 10, pp. 1108-1120.
13. Xuerui Wang, Andrew McCallum, and Xing Wei, 2007. Topical N-grams: Phrase and Topic Discovery, with an Application to Information Retrieval. ICDM, 697-702.
14. Shuo Xu, Liyuan Hao, Guancan Yang, Kun Lu, and Xin An, 2021. A Topic Models based Framework for Detecting and Forecasting Emerging Technologies. Technology Forecasting and Social Change, Vol. 162, pp. 120366. Note
15. Zheng Wang, Shuo Xu, and Lijun Zhu, 2018. Semantic Relation Extraction Aware of N-Gram Features from Unstructured Biomedical Text. Journal of Biomedical Informatics, Vol. 86, pp. 59-70. Note
16. 安欣，徐硕, 2019. 基于主题N元语法模型的科技报告主题分析. 农业图书情报, Vol. 31, No. 6, pp. 21-30.
17. Shuo Xu, Dongsheng Zhai, Feifei Wang, Xin An, Hongshen Pang, and Yirong Sun, 2019. A Novel Method for Topic Linkages between Scientific Publications and Patens. Journal of the Association for Information Science and Technology, Vol. 70, No. 9, pp. 1026-1042. Note
18. Shuo Xu, Liyuan Hao, Xin An, Guancan Yang, and Feifei Wang, 2019. Emerging Research Topics Detection with Multiple Machine Learning Models. Journal of Informetrics, Vol. 13, No. 4, pp. 100983.
19. Shuo Xu, Junwan Liu, Dongsheng Zhai, Xin An, Zheng Wang, and Hongshen Pang, 2018. Overlapping Thematic Structures Extraction with Mixed-Membership Stochastic Blockmodel. Scientometrics, Vol. 117, No. 1, pp. 61-84. Note
20. Ting Hua, Chang-Tien Lu, Jaegul Choo, and Chandan K. Reddy, 2020. Probabilistic Topic Modeling for Comparative Analysis of Document Collections. ACM Transactions on Knowledge Discovery from Data, Vol. 14, No. 2, pp. 24:1-24:27.
21. Shuo Xu, Ling Li, Xin An, Liyuan Hao, and Guancan Yang, 2021. An Approach for Detecting the Commonality and Specialty between Scientific Publications and Patents. Scientometrics, Vol. 126, No. 9, pp. 7445-7475. Note
22. Rajarshi Das, Manzil Zaheer, and Chris Dyer, 2015. Gaussian LDA for Topic Models with Word Embeddings. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 795-804.