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Applying Qualitative Research Methods to Narrative Knowledge Engineering

Authors Brian O'Neill, Mark Riedl

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Brian O'Neill
Mark Riedl

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Brian O'Neill and Mark Riedl. Applying Qualitative Research Methods to Narrative Knowledge Engineering. In 2014 Workshop on Computational Models of Narrative. Open Access Series in Informatics (OASIcs), Volume 41, pp. 139-153, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)
https://doi.org/10.4230/OASIcs.CMN.2014.139

Abstract

We propose a methodology for knowledge engineering for narrative intelligence systems, based on techniques used to elicit themes in qualitative methods research. Our methodology uses coding techniques to identify actions in natural language corpora, and uses these actions to create planning operators and procedural knowledge, such as scripts. In an iterative process, coders create a taxonomy of codes relevant to the corpus, and apply those codes to each element of that corpus. These codes can then be combined into operators or other narrative knowledge structures. We also describe the use of this methodology in the context of Dramatis, a narrative intelligence system that required STRIPS operators and scripts in order to calculate human suspense responses to stories.
Keywords
  • narrative intelligence
  • qualitative methods
  • coding
  • knowledge engineering

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References

  1. Fatima A. Boujarwah, Gregory D. Abowd, and Rosa I. Arriaga. Socially computed scripts to support social problem solving skills. In Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pages 1987-1996, Austin, Texas, USA, 2012. ACM Press. Google Scholar
  2. Nathanael Chambers and Dan Jurafksy. Unsupervised learning of narrative event chains. In Proceedings of the Forty-Sixth Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 789-797. Association for Computational Linguistics, 2008. Google Scholar
  3. Kathy Charmaz. Constructing grounded theory: A practical guide through qualitative analysis. Sage Publications, Thousand Oaks, California, USA, 2006. Google Scholar
  4. Domenic V. Cicchetti. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6(4):284-290, December 1994. Google Scholar
  5. Richard E. Cullingford. SAM and micro SAM. In Roger Schank and Christopher Riesbeck, editors, Inside Computer Understanding. Erlbaum, Hillsdale, NJ, 1981. Google Scholar
  6. Richard E. Fikes and Nils J. Nilsson. STRIPS: a new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2:189-208, 1971. Google Scholar
  7. Joseph L. Fleiss. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5):378-382, November 1971. Google Scholar
  8. Toshiaki Fujiki, Hidetsugu Nanba, and Manabu Okumura. Automatic acquisition of script knowledge from a text collection. In Proceedings of the Tenth Conference on European chapter of the Association for Computational Linguistics, pages 91-94, Budapest, Hungary, 2003. Association for Computational Linguistics. Google Scholar
  9. Pablo Gervás, Belén Díaz-Agudo, Federico Peinado, and Raquel Hervás. Story plot generation based on CBR. Knowledge-Based Systems, 18(4-5):235-242, August 2005. Google Scholar
  10. Niels Kasch and Tim Oates. Mining script-like structures from the web. In Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, pages 34-42, Los Angeles, California, USA, 2010. Association for Computational Linguisitics. Google Scholar
  11. Klaus Krippendorff. Testing the reliability of content analysis data: What is involved and why. In Klaus Krippendorff and Mary Angela Bock, editors, The Content Analysis Reader, pages 350-357. Sage Publications, Thousand Oaks, CA, USA, 2009. Google Scholar
  12. J. Richard Landis and Gary G. Koch. The measurement of observer agreement for categorical data. Biometrics, 33(1):159-174, March 1977. Google Scholar
  13. Edith Law and Louis von Ahn. Human Computation. Morgan &Claypool, 2011. Google Scholar
  14. Boyang Li, Stephen Lee-Urban, George Johnston, and Mark O. Riedl. Story generation with crowdsourced plot graphs. In Proceedings of the 27th AAAI Conference on Artificial Intelligence, Bellevue, Washington, USA, 2013. AAAI. Google Scholar
  15. James Meehan. TALE-SPIN. In Roger C Schank and Christopher K. Riesbeck, editors, Inside Computer Understanding, pages 197-226. Lawrence Erlbaum Associates, Hillsdale, NJ, 1981. Google Scholar
  16. Matthew B. Miles and A. Michael Huberman. Qualitative data analysis. Sage Publications, Thousand Oaks, California, USA, 2nd edition, 1994. Google Scholar
  17. Brian O'Neill. A Computational Model of Suspense for the Augmentation of Intelligent Story Generation. Ph.D. dissertation, Georgia Institute of Technology, Atlanta, GA, 2013. Google Scholar
  18. Brian O'Neill and Mark Riedl. Dramatis: A computational model of suspense. In Proceedings of the 28th AAAI Conference on Artificial Intelligence, Quebec City, QC, Canada, 2014. AAAI Press. Google Scholar
  19. Mark O. Riedl and R. Michael Young. Narrative planning: Balancing plot and character. Journal of Artificial Intelligence Research, 39(1):217-268, September 2010. Google Scholar
  20. Johnny Saldaña. The Coding Manual for Qualitative Researchers. Sage Publications, Los Angeles, California, USA, 2009. Google Scholar
  21. Roger C. Schank and R.P. Abelson. Scripts, plans, goals and understanding: An inquiry into human knowledge structures, volume 2. Lawrence Erlbaum Associates, Hillsdale, NJ, USA, 1977. Google Scholar
  22. Avirup Sil and Alexander Yates. Extracting STRIPS representations of actions and events. In Proceedings of the Recent Advances in Natural Language Processing, pages 1-8, Hissar, Bulgaria, 2011. Google Scholar
  23. Sigal Sina, Avi Rosenfeld, and Sarit Kraus. Generating content for scenario-based serious-games using CrowdSourcing. In Proceedings of the 28th AAAI Conference on Artificial Intelligence, Quebec City, QC, Canada, 2014. AAAI Press. Google Scholar
  24. Anselm L. Strauss. Qualitative analysis for social scientists. Cambridge University Press, Cambridge, UK, 1987. Google Scholar
  25. Reid Swanson and Andrew Gordon. Say anything: A massively collaborative open domain story writing companion. In Interactive Storytelling, pages 32-40. Springer Verlag, 2008. Google Scholar
  26. Robert Wilensky. PAM and micro PAM. In Roger Schank and Christopher Riesbeck, editors, Inside Computer Understanding. Erlbaum, Hillsdale, NJ, 1981. Google Scholar
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