[1] Landauer, T., Foltz, P., Laham, D.: An Introduction to Latent Semantic Analysis (1998). Discourse Processes, 25, 259-284. [2] Deerwester, S.: Improving Information Retrieval with Latent Semantic Indexing. Proceedings of the 51st ASIS Annual Meeting (ASIS ’88), volume 25, Atlanta, Georgia, October 1988. American Society for Information Science. [3] Bishop, C.: “Pattern Recognition and Machine Learning.” (Information Science and Statistics), Springer, 2006 [4] Edmunds, A. and Morris, A.: The problem of information overload in business organisations: a review of the literature. International Journal of Information Management, 20(1):17-28, 2000. [5] Blei, D.M., Lafferty, J.D.: A correlated topic model of Science. Annals of Applied Statistics 1(1), 17–35 (Aug 2007) [6] D. Blei, J. McAuliffe. Supervised topic models. Neural Information Processing Systems 21, 2007. [7] D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, January 2003. [8] Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science (1990) [9] Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America 101, 5228–5235 (Apr 2004) [10] B. Rosario, "Latent Semantic Indexing: An overview," School of Info. Management & Systems, U.C. Berkeley, 2000 [11] Hofmann, T., Cai, L., Ciaramita, M.: Learning with taxonomies: Classifying documents and words. In: Proceedings of Synatx, Semantics and Statistics NIPS Workshop (2003) [12] W. Su, D. Ziou and N. Bouguila, “A Hierarchical Statistical Framework for the Extraction of Semantically Related Words in Textual Documents”, Proc. Of the 8th International Conference on Rough Sets and Knowledge Technology (RSKT 2013), Lecture Notes in Computer Science 8171, pp. 354-363, Halifax, Canada, 2013. [13] Maas, A., Ng, A.: A Probabilistic Model for Semantic Word Vectors. In: Deep Learning and Unsupervised Feature Learning Workshop NIPS 2010. vol. 10 (2010) [14] MacKay, D. and Bauman Peto, L.: A hierarchical Dirichlet language model. Natural Language Engineering, Vol 1, Issue 3 pp 289-308. Cambridge University Press (1995) [15] Hofmann, T.: Unsupervised Learning by Probabilistic Latent Semantic Analysis. In: Machine Learning Journal, 42, 177-196, 2001. [16] Lobanova, A., Spenader, J., Van de Cruys, T., Van der Kleij, T. and Tjong Kim Sang, E.: Automatic Relation Extraction - Can Synonym Extraction Benefit from Antonym Knowledge? In: NODALIDA 2009 workshop WordNets and other Lexical Semantic Resources - between Lexical Semantics, Lexicography, Terminology and Formal Ontologies, Odense, Denmark. [17] Z. Liu, M. Li, Y. Liu and M. Ponraj, Performance Evaluation of Latent Dirichlet Allocation in Text Mining, Proc. of IEEE pp. 2761-2764. [18] Hoffman, M., Blei, D., Paisley, J. and Wang, C.: Stochastic variational inference. Journal of Machine Learning Research, 14:1303-1347, 2013. [19] Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. pp. 50–57. SIGIR ’99 (1999) [20] Jahiruddin, Abulaish M, Dey L: A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora. J Biomed Inform. 2010 Dec; 43(6):1020-35. [21] Blei, D.: Probabilistic topic models. Communications of the ACM, 55(4):77–84, 2012. [22] Salton, G. and McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, 1983. [23] Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Journal of Machine Learning Research 39(2-3), 103–134 (May 2000). [24] Denning, P.J., Denning, D.E.: Discussing cyber attack. Communications of the ACM 53(9), 29–31 (Sep 2010) [25] Goel, S.: Cyberwarfare: connecting the dots in cyber intelligence. Commun. ACM 54(8), 132–140 (Aug 2011) [26] Blei, D., Lafferty, J.: Dynamic Topic Models. In: Proceedings of the 23rd international Conference on Machine Learning. ICML '06, 113- 120.