A Risk Assessment System with Automatic Extraction of Event Types

  • Philippe Capet
  • Thomas Delavallade
  • Takuya Nakamura
  • Agnes Sandor
  • Cedric Tarsitano
  • Stavroula Voyatzi
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 288)


In this article we describe the joint effort of experts in linguistics, information extraction and risk assessment to integrate EventSpotter, an automatic event extraction engine, into ADAC, an automated early warning system. By detecting as early as possible weak signals of emerging risks ADAC provides a dynamic synthetic picture of situations involving risk. The ADAC system calculates risk on the basis of fuzzy logic rules operated on a template graph whose leaves are event types. EventSpotter is based on a general purpose natural language dependency parser, XIP, enhanced with domain-specific lexical resources (Lexicon-Grammar). Its role is to automatically feed the leaves with input data.


Event Type Target Concept Event Extraction Light Water Nuclear Reactor Event Description 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [1]
    Ahn, D.: The stages of event extraction. In: Proceedings of the Workshop on Annotations and Reasoning about Time and Events, pp. 1–8. (2006)Google Scholar
  2. [2]
    Ait-Mokhtar, S., Chanod, J-P., Roux, C.: Robustness beyond shallowness: incremental dependency parsing. Natural Language Engineering, 8(2/3) pp. 121–144. (2002)Google Scholar
  3. [3]
    Aone, C., Ramos-Santacruz, M.: REES: A Large-Scale Relation and Event Extraction System. In: Proceedings of the sixth conference on Applied natural language processing, pp. 76–83. Seattle, Washington (2000)Google Scholar
  4. [4]
    Banko, M., Etzioni, O.: The Tradeoffs Between Open and Traditional Relation Extraction. ACL (2008)Google Scholar
  5. [5]
    Briscoe, T., Carroll, J.: Generalised Probabilistic LR Parsing for Unification-Based Grammars. Computational Linguistics, 19(1) (1993)Google Scholar
  6. [6]
    Carroll, J., Fang. A.: The Automatic Acquisition of Verb Subcategorisations and their Impact on the Performance of an HPSG Parser. In: Proceedings of the First International Joint Conference on Natural Language Processing, pp. 107–114. Sanya City (2004)Google Scholar
  7. [7]
    Delavallade, T., Mouillet, L., Bouchon-Meunier, B., Collain, E.: Monitoring Event Flows and Modelling Scenarios for Crisis Prediction: Application to Ethinc Conflict Forecasting. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. (2007)Google Scholar
  8. [8]
    Gardent, C., Guillaume, B., Falk, L, Perrier, G.: Le lexique-grammaire de M. Gross et le traitement automatique des langues. In ATALA (2005)Google Scholar
  9. [9]
    Korhonen, A.: Semantically Motivated Subcategorization Acquisition. In: Proceedings of the ACL Workshop on Unsupervised Lexical Acquisition, 9, pp. 51–58. Philadelphia (2002)Google Scholar
  10. [10]
    Leclere, C.: Organization of the Lexicon-Grammar of French Verbs. Lingvisticae Investigationes, 25(1), pp. 29–48 (2002)CrossRefGoogle Scholar
  11. [11]
    Li, Z., Wang, B., Li, M., Ma, W-Y.: A Probabilistic Model for Retrospective News Event Detection. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 106–113. Salvador (2005)Google Scholar
  12. [12]
    Rebotier, A., Sandor, A., Voyatzi, S., Nakamura, T., Martineau, C., Delevallade, T., Capet, P., Jacquelinet, J.: Intelligent awareness: event extraction, information evaluation & risk assessment. In: 3rd Language & Technology Conference, pp. 539–543. Poznan (2007)Google Scholar
  13. [13]
    Sandor, A., Kaplan, A., Rondeau, G.: Discourse and Citation Analysis with Concept-Matching. In: International Symposium, Discourse and Document, pp. 147–151. Presse Universitaire de Caen, Caen (2006)Google Scholar
  14. [14]
    Schrodt, P., Davis, S., Weddle, J.: Political Science: KEDS-A Program for the Machine Coding of Event Data. Social Science Computer Review. 12, 561–588 (1994)CrossRefGoogle Scholar
  15. [15]
    Xu, F., Uszkoreit, H., Li, H.: Automatic Event and Relation Detection with Seeds of Varying Complexity. AAAI Workshop Event Extraction and Synthesis, Boston (2006)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Philippe Capet
    • 1
  • Thomas Delavallade
    • 1
  • Takuya Nakamura
    • 2
  • Agnes Sandor
    • 3
  • Cedric Tarsitano
    • 3
  • Stavroula Voyatzi
    • 2
  1. 1.THALES Land & Joint SystemsAustralia
  2. 2.Institut Gaspard-MongeUniversite de Marne-la-ValleeMarne-la-Vallee Cedex 2France
  3. 3.Xerox Research Centre EuropeMeylanFrance

Personalised recommendations