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A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

  • Xi, Yaoyi (Zhengzhou Information Science and Technology Institute) ;
  • Li, Bicheng (Zhengzhou Information Science and Technology Institute) ;
  • Liu, Yang (Zhengzhou Information Science and Technology Institute)
  • Received : 2015.03.02
  • Accepted : 2015.05.15
  • Published : 2015.06.30

Abstract

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

References

  1. L. Huang and L. Huang, "Optimized event storyline generation based on mixture-event-aspect model," in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, WA, 2013, pp. 726-735.
  2. A. Ahmed and E. P. Xing, "Timeline: a dynamic hierarchi-cal Dirichlet process model for recovering birth/death and evolution of topics in text stream," in Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI2010), Catalina Island, CA, 2010.
  3. J. H. Lau, N. collier, & T. baldwin, "on-line trend analysis with topic models: twitter trends detection topic model online," in Proceedings of COLING 2012: Technical Papers, Mumbai, India, 2012, pp. 1519-1534.
  4. P. Lee, L. V. Lakshmanan, & E. E. Milios, "Event evolution tracking from streaming social posts," http://arxiv.org/pdf/1311.5978v1.pdf.
  5. A. Feng and J. Allan, "Finding and linking incidents in news," in Proceedings of the 16th ACM Conference on Information and Knowledge Management (CIKM'07), Lisbon, Portugal, 2007, pp. 821-830.
  6. A. Feng and J. Allan, "Incident threading for news passages," in Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM'09), Hong Kong, China, 2009, pp. 1307-1316.
  7. R. Nallapati, A. Feng, F. Peng, and J. Allan, "Event threading within news topics," in Proceedings of the 13th ACM International Conference on Information and Knowledge Management (CIKM'04), Washington, DC, 2004, pp. 446-453.
  8. C. C. Yang, X. Shi, and C. P. Wei, "Discovering event evolution graphs from news corpora," IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 39, no. 4, pp. 850-863, 2009. https://doi.org/10.1109/TSMCA.2009.2015885
  9. D. Luo, J. Yang, M. Krstajic, W. Ribarsky, and D. Keim, "EventRiver: visually exploring text collections with temporal references," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 1, pp. 93-105, 2012. https://doi.org/10.1109/TVCG.2010.225
  10. P. D. Turney and P. Pantel, "From frequency to meaning: vector space models of semantics," Journal of Artificial Intelligence Research, vol. 37, no. 1, pp. 141-188, 2010.
  11. D. M. Blei, A. Y. Ng, & M. I. Jordan, "Latent Dirichlet allocation," Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
  12. J. Y. Delort and E. Alfonseca, "DualSum: a topic-model based approach for update summarization," in Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Avignon, France, 2012, pp. 214-223.
  13. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical Dirichlet processes," Journal of the American Statistical Association, vol. 101, no. 476, pp. 1566-1581, 2006. https://doi.org/10.1198/016214506000000302
  14. K. N. Vavliakis, A. L. Symeonidis, and P. A. Mitkas, "Event identification in web social media through named entity recognition and topic modeling," Data & Knowledge Engineering, vol. 88, pp. 1-24, 2013. https://doi.org/10.1016/j.datak.2013.08.006
  15. J. Allan, Topic Detection and Tracking: Event-Based Information Organization. Boston, MA: Kluwer Academic Publishers, 2002.
  16. W. Ding and C. Chen, "Dynamic topic detection and tracking: a comparison of HDP, C-word, and cocitation methods," Journal of the Association for Information Science and Technology, vol. 65, no. 10, pp. 2084-2097, 2014. https://doi.org/10.1002/asi.23134
  17. A. Feng and J. Allan, "Hierarchical topic detection in TDT-2004," Center for Intelligent Information Retrieval Technical Report, 2005.
  18. Z. Kozareva, "Cause-effect relation learning," in Workshop Proceedings of TextGraphs-7 on Graph-Based Methods for Natural Language Processing, Jeju Island, Korea, 2012, pp. 39-43.
  19. J. Fu, Z. Liu, W. Liu, and Q. Guo, "Using dual-layer CRFs for event causal relation extraction," IEICE Electronics Express, vol. 8, no. 5, pp. 306-310, 2011. https://doi.org/10.1587/elex.8.306
  20. S. A. Mirroshandel, M. Khayyamian, and G. Ghassem-Sani, "Syntactic tree kernels for event-time temporal relation learning," in Human Language Technology: Challenges for Computer Science and Linguistics. Heidelberg: Springer, pp. 213-223, 2011.
  21. S. A. Mirroshandel and G. Ghassem-Sani, "Towards unsupervised learning of temporal relations between events," Journal of Artificial Intelligence Research, vol. 45, pp. 125-163, 2012.
  22. National Institute of Standards and Technology, "The 2004 Topic Detection and Tracking (TDT2004) task definition and evaluation plan," http://www.itl.nist.gov/iad/mig/tests/tdt/2004/TDT04.Eval.Plan.v1.2.compare.1.1c.pdf.