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Towards an Entity–Based Automatic Event Validation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8416))

Abstract

Event Detection algorithms infer the occurrence of real–world events from natural language text and always require a ground truth for their validation. However, the lack of an annotated and comprehensive ground truth makes the evaluation onerous for humans, who have to manually search for events inside it. In this paper, we envision to automatize the evaluation process by defining the novel problem of Entity–based Automatic Event Validation. We propose a first approach which validates events by estimating the temporal relationships among their representative entities within documents in the Web. Our approach reached a Kappa Statistic of 0.68 when compared with the evaluation of real–world events done by humans. This and other preliminary results motivate further research effort on this novel problem.

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References

  1. Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: ACM SIGIR (1998)

    Google Scholar 

  2. Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. Comput. Linguist. (1996)

    Google Scholar 

  3. Das Sarma, A., et al.: Dynamic relationship and event discovery. In: WSDM (2011)

    Google Scholar 

  4. Fisichella, M., Stewart, A., Denecke, K., Nejdl, W.: Unsupervised public health event detection for epidemic intelligence. In: CIKM (2010)

    Google Scholar 

  5. Fung, G.P.C., Yu, J.X., Yu, P.S., Lu, H.: Parameter free bursty events detection in text streams. In: VLDB (2005)

    Google Scholar 

  6. Hoffart, J., Suchanek, F., Berberich, K., Weikum, G.: Yago2: A spatially and temporally enhanced knowledge base from Wikipedia. Artificial Intelligence (2012)

    Google Scholar 

  7. Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  8. Li, Z., Wang, B., Li, M., Ma, W.-Y.: A probabilistic model for retrospective news event detection. In: ACM SIGIR (2005)

    Google Scholar 

  9. Shan, D., et al.: Eventsearch: a system for event discovery and retrieval on multi-type historical data. In: SIGKDD 2012 (2012)

    Google Scholar 

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© 2014 Springer International Publishing Switzerland

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Ceroni, A., Fisichella, M. (2014). Towards an Entity–Based Automatic Event Validation. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_64

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  • DOI: https://doi.org/10.1007/978-3-319-06028-6_64

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06027-9

  • Online ISBN: 978-3-319-06028-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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