Abstract
Rapid proliferation of the World Wide Web led to an enormous increase in the availability of textual corpora. In this paper, the problem of topic detection and tracking is considered with application to news items. The proposed approach explores two algorithms (Non-Negative Matrix Factorization and a dynamic version of Latent Dirichlet Allocation (DLDA)) over discrete time steps and makes it possible to identify topics within storylines as they appear and track them through time. Moreover, emphasis is given to the visualization and interaction with the results through the implementation of a graphical tool (regardless the approach). Experimental analysis on Reuters RCV1 corpus and the Reuters 2015 archive reveals that explored approaches can be effectively used as tools for identifying topic appearances and their evolutions while at the same time allowing for an efficient visualization.
Authors contributed equally to the manuscript, thus appear in alphabetical order.
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References
Ahmed, A., Xing, E.P.: Timeline: a dynamic hierarchical dirichlet process model for recovering birth/death and evolution of topics in text stream. arXiv preprint arXiv:1203.3463 (2012)
Allan, J., Carbonell, J.G., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study final report (1998)
Banerjee, A., Basu, S.: Topic models over text streams: a study of batch and online unsupervised learning. In: SDM, vol. 7, pp. 437–442. SIAM (2007)
Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 113–120. ACM, New York (2006)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Cao, B., Shen, D., Sun, J.-T., Wang, X., Yang, Q., Chen, Z.: Detect and track latent factors with online nonnegative matrix factorization. In: IJCAI, pp. 2689–2694 (2007)
Dubey, A., Hefny, A., Williamson, S., Xing, E.P.: A nonparametric mixture model for topic modeling over time. In: SDM, pp. 530–538. SIAM (2013)
Fiscus, J.G. Doddington, G.R.: Topic detection and tracking evaluation overview. In: Topic Detection and Tracking, pp. 17–31. Kluwer Academic Publishers, Norwell (2002)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. 101(suppl 1), 5228–5235 (2004)
Hong, L., Dom, B., Gurumurthy, S., Tsioutsiouliklis, K.: A time-dependent topic model for multiple text streams. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 832–840. ACM (2011)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014)
Paul, M., Girju, R.: Cross-cultural analysis of blogs and forums with mixed-collection topic models. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 3, pp. 1408–1417. Association for Computational Linguistics (2009)
Piantadosi, S.T.: Zipfs word frequency law in natural language: a critical review and future directions. Psychon. Bull. Rev. 21(5), 1112–1130 (2014)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Saha, A., Sindhwani, V.: Learning evolving and emerging topics in social media: a dynamic NMF approach with temporal regularization. In: Proceedings of the fifth ACM International conference on Web Search and Data Mining, pp. 693–702 (2012)
Sra, S., Dhillon, I.S.: Generalized nonnegative matrix approximations with bregman divergences. In: Advances in Neural Information Processing Systems, pp. 283–290 (2005)
Tannenbaum, M., Fischer, A., Scholtes, J.C.: Dynamic topic detection and tracking using non-negative matrix factorization. In: Proceedings of the 27th Benelux Artificial Intelligence Conference (BNAIC). BNAIC (2015)
Wang, C., Blei, D.M., Heckerman, D.: Continuous time dynamic topic models. In: McAllester, D.A., Myllymki, P. (eds.), UAI, pp. 579–586. AUAI Press (2008)
Wang, F., Li, P., Christian König, A.: Efficient document clustering via online nonnegative matrix factorizations. In: SDM, vol. 11, pp. 908–919. SIAM (2011)
Wang, X., McCallum, A.: Topics over time: a non-markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424–433. ACM (2006)
Wei, X., Sun, J., Wang, X.: Dynamic mixture models for multiple time-series. In: Veloso, M.M. (ed.) IJCAI, pp. 2909–2914 (2007)
Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 177–186. ACM (2011)
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Brüggermann, D., Hermey, Y., Orth, C., Schneider, D., Selzer, S., Spanakis, G. (2017). Towards a Topic Discovery and Tracking System with Application to News Items. In: Quesada, J., Martín Mateos , FJ., López Soto, T. (eds) Future and Emerging Trends in Language Technology. Machine Learning and Big Data. FETLT 2016. Lecture Notes in Computer Science(), vol 10341. Springer, Cham. https://doi.org/10.1007/978-3-319-69365-1_15
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