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Fact, Conjecture, Hearsay and Lies: Issues of Uncertainty in Natural Language Communications

  • Kellyn ReinEmail author
Chapter
Part of the Information Fusion and Data Science book series (IFDS)

Abstract

Humans are very important sources of information for intelligence purposes. They are multi-modal: they see, hear, smell, and feel. However, the information which they relay is not simply that which they personally experience. They may pass on hearsay, they form opinions, they analyze and interpret what they hear or see or feel. Sometimes they pass on ambiguous, vague, misleading or even false information, whether intentional or not. However, whether imprecise or vague, when humans communicate information, they often embed clues in the form of lexical elements in that which they pass on that allows the receiver to interpret where the informational content originated, how strongly the speaker herself believes in the veracity of that information. In this chapter, we look at the ways in which human communications are uncertain, both within the content and about the content. We illustrate a methodology which helps us to make an initial evaluation of the evidential quality of information based upon lexical clues.

Keywords

Opinions Quality of information Lies Uncertain information Natural language 

References

  1. 1.
    J.A. Gans Jr, “‘This is 50-50’: Behind Obama’s decision to kill Bin Laden”, The Atlantic, Oct 10, 2012, https://www.theatlantic.com/international/archive/2012/10/this-is-50-50-behind-obamas-decision-to-kill-bin-laden/263449/
  2. 2.
    R. de Gourmont, Philosophic Nights in Paris (J.W. Luce, Boston, 1920), p. 127Google Scholar
  3. 3.
    V. Dragos, K. Rein, “What’s in a message? Exploring dimensions of trust in reported information”, Proceedings of Fusion 2016, IEEE, 2016Google Scholar
  4. 4.
    M. Bednarek, Evaluation in Media Discourse: Analysis of a Newspaper Corpus (Continuum, London, 2006)Google Scholar
  5. 5.
    G.A. Gross, R. Nagi, D. R. KedarSambhoos S.C. Schlegel G.T. Shapiro, Towards Hard+Soft Data Fusion: Processing Architecture and Implementation for the Joint Fusion and Analysis of Hard and Soft Intelligence Data. Proceedings of Fusion 2012, pp. 955–962Google Scholar
  6. 6.
  7. 7.
    D. Claeser, D. Felske, S. Kent, Token level code-switching detection using Wikipedia as a lexical resource, in Language Technologies for the Challenges of the Digital Age. GSCL 2017. Lecture Notes in Computer Science, ed. by G. Rehm, T. Declerck, vol. 10713, (Springer, Cham, 2018)Google Scholar
  8. 8.
    K. Sherman, “Words of Estimative Probability“, Studies in Intelligence, Fall 1964, Central Intelligence Agency, https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/sherman-kent-and-the-board-of-national-estimates-collected-essays/6words.html, (1964)
  9. 9.
    R.J. Heuer Jr., “Psychology of intelligence analysis”, Center for the Study of Intelligence (1999)Google Scholar
  10. 10.
    S. Rieber, “Communicating Uncertainty in Intelligence Analysis”, http://citation.allacademic.com/meta/p100689_index.htmlf (2006)
  11. 11.
    G. Lakoff, Hedges: A study in meaning criteria and the logic of Fuzzy concepts. J. Philos. Logic 2, 458–508 (1973). D. Reidel Publishing Co., Dordrecht, HollandMathSciNetCrossRefGoogle Scholar
  12. 12.
    K. Rein, I believe it’s possible it might be so.... exploiting lexical clues for the automatic generation of evidentiality weights for information extracted from English text. Universitäts- und Landesbibliothek Bonn, (2016). http://hss.ulb.uni-bonn.de/2016/4471/4471.htm
  13. 13.
    Z. Frajzyngier, Truth and the indicative sentence. Stud. Lang. 9(2), 243–254 (1985)CrossRefGoogle Scholar
  14. 14.
    B. Goujon, Uncertainty detection for information extraction, International Conference RANLP 2009, Borovets, Bulgaria, (2009), pp. 118–122Google Scholar
  15. 15.
    J.I. Marin-Arrese, “Epistemic Legitimizing Strategies, Commitment and Accountability in Discourse”, {Discourse Studies}, vol 13 (Sage Publications, 2011). https://doi.org/10.1177/1461445611421360c
  16. 16.
    E.D. Liddy, N. Kando, V.L. Rubin, “Certainty Categorization Model”, The AAAI Symposium on Exploring Attitude and Affect in Text AAAI-EAAT, vol 2004 (American Association for Artificial Intelligence, Stanford, 2004)Google Scholar
  17. 17.
    K.H. Hyland, Hedging in Scientific Research Articles (John Benjamins, Amsterdam/Philadelphia, 1998)CrossRefGoogle Scholar
  18. 18.
    Russell, Bertrand, “Am I an Atheist or an Agnostic?”, Literary Guide Rationalist Rev. 64, 7, July, 1949, pp. 115–116Google Scholar
  19. 19.
    K.H. Teigen, W. Brun, Yes, but it is uncertain: Directions and communcativeitention of verbal probabilistic terms. ActaPsychologica 88, 233–258., Elsevier Science B.V. (1995)Google Scholar
  20. 20.
    W. Brun, K.H. Teigen, Verbal probabilities: Ambiguous, context-dependent, or both? Organ. Behav. Hum. Decis. Process. 41(3), 390–404 (1988)CrossRefGoogle Scholar
  21. 21.
    S. Renooij, C.L.M. Witteman, Talking probabilities: Communicating probabilistic information with words and numbers. Int. J. Approximate Reason. 22(3), 169–195. Elsevier (1999)CrossRefGoogle Scholar
  22. 22.
    C.L.M. Witteman, S. Renooij, P. Koele, BMC Med. Inform. Decis. Mak. 7(13) (2007). BioMed Central Ltd, http://www.biomedcentral.com/1472-6947/7/13
  23. 23.
    B.M. Ayyub, G.J. Klir, Uncertainty Modeling and Analysis in Engineering and the Sciences (J. Chapman and Hall/CRC, Boca Raton, 2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Fraunhofer FKIEWachtbergGermany

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