Exploring the Role of Named Entities for Uncertainty Recognition in Event Detection

Masnizah Mohd, Kiyoaki Shirai

2015

Abstract

Ambiguous information contributes to the uncertainty issue. Type of information such as using named entities has been proved to provide significant information to the user compared to the ‘bag-of-words’ in identifying an event. So what else could contribute to the uncertainty in an event detection? We propose to answer this question by analysing the distribution of named entities across topics, and explore the potential of named entities in a user experiment. We construct an event detection task with 20 users and use news dataset from Topic Detection and Tracking (TDT) corpus, under the Sports and Politics categories. We analyse the results from five uncertainty dimensions: too little information, too much information, complex information, ambiguous information and conflicting information. These dimensions are categorise as two factors; amount and type of information. There was no statistical significance difference in the amount of information given with the number of successful event detected. However, with little information and high named entities has contributes in reducing uncertainty. In addition, the amount of information and information quality are mutually independent. Our results suggest that uncertainty vary substantially between the amount of information and type of information in event detection.

References

  1. TDT Corpus. https://catalog.ldc.upenn.edu/LDC98T25.
  2. TDT Annotation Manual. https://catalog.ldc.upenn.edu/ docs/LDC2006T19/TDT2004V1.2.pdf.
  3. Mohd, M and Mabrook, O., 2014. Investigating the Combination of Bag of Words and Named Entities Approach in Tracking and Detection Tasks among Journalists. Journal of Information Science Theory and Practice. 2(4), pp 31-38.
  4. Hurley, R. J. 2011. Uncertain about cancer? so is online news. Communication Currents, 6 (5). http://www.natcom.org/CommCurrentsArticle.aspx?id =1703.
  5. Chowdhury, S., Gibb, F., & Landoni, M. 2011. Uncertainty in information seeking and retrieval: A study in an academic environment. Inf. Process. Manage. 47, 2 (March 2011), pp. 157-175. DOI=http://dx.doi.org/ 10.1016/j.ipm.2010.09.006.
  6. Ingwersen, P. 1992. Information Retrieval Interaction, Taylor Graham, London.
  7. Goodman, N. D., Vikash, K., Mansinghka, D. R., Bonawitz, K., and Tenenbaum, J.B. 2008. Church: A language for generative models. In Proceedings of the TwentyFourth Conference Annual Conference on Uncertainty in Artificial Intelligence, Corvallis, Oregon, pp. 220- 229.
  8. Topka, L. V., 2013. Situation of uncertainty: pragmatic, semantic, and syntactic aspects of investigation. European Scientific Journal. 9 (Sept. 2013), pp. 60-69.
  9. Rubin, V. L., Liddy, E. D., and Kando, N. 2006. Certainty identification in texts: categorization model and manual tagging results. Springer, Dordrecht, The Netherlands, vol. 20, pp. 61-76.
  10. Goujon, B. 2009. Uncertainty detection for information extraction. In Proceedings of the International Conference RANLP 2009, Borovets, Bulgaria, pp. 118- 122.
  11. Mishel, M. H. 1988. Uncertainty in illness. Image J Nurs Sch, vol. 20, pp. 225-232.
  12. Babrow, A. S., Kasch, C. R., and Ford, L. A. 1998. The many meanings of uncertainty in illness: towards a systematic accounting. Heatlh Communication, vol.10, pp. 1-23.
  13. Chen H. and Ku L. W. 2002. An NLP and IR approach to topic detection. In: Allan J (ed.) Topic detection and tracking: Event-based information organization. Norwell, MA: Kluwer Academic Publishers, pp. 243- 264.
  14. Cunningham, H., Maynard, D., Bontcheva, K., and Tablan, V. 2002. GATE: A framework and graphical development environment for robust NLP tools and applications. In Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics, July 2002, Philadelphia, PA, pp. 168-175.
  15. Sparck-Jones, K., and Willet, P. 1997. Readings in Information Retrieval. San Francisco, CA: Morgan Kaufmann.
Download


Paper Citation


in Harvard Style

Mohd M. and Shirai K. (2015). Exploring the Role of Named Entities for Uncertainty Recognition in Event Detection . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: RDBPM, (IC3K 2015) ISBN 978-989-758-158-8, pages 335-341. DOI: 10.5220/0005609503350341


in Bibtex Style

@conference{rdbpm15,
author={Masnizah Mohd and Kiyoaki Shirai},
title={Exploring the Role of Named Entities for Uncertainty Recognition in Event Detection},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: RDBPM, (IC3K 2015)},
year={2015},
pages={335-341},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005609503350341},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: RDBPM, (IC3K 2015)
TI - Exploring the Role of Named Entities for Uncertainty Recognition in Event Detection
SN - 978-989-758-158-8
AU - Mohd M.
AU - Shirai K.
PY - 2015
SP - 335
EP - 341
DO - 10.5220/0005609503350341