Story understanding resources

Meetings

• November 11-13, 2010, Arlington, VA: AAAI Fall Symposium on Computational Models of Narrative http://narrative.csail.mit.edu/fs10/
• November 11-13, 2010, Arlington, VA: AAAI Fall Symposium on Commonsense Knowledge http://csk.media.mit.edu/
• June 18, 2010, Monterey, CA: Third Workshop on Intelligent Narrative Technologies http://cs.ucsc.edu/~jhala/int3/
• March 23-25, 2009, Stanford, CA: AAAI Spring Symposium on Intelligent Narrative Technologies II http://www.cc.gatech.edu/conferences/aaai-int2/
• March 23-25, 2009, Stanford, CA: AAAI Spring Symposium on Learning by Reading and Learning to Read http://www.coral-lab.org/~oates/aaai2009ss/
• February 8, 2009, Sanibel Island, FL: Workshop on Common Sense and Intelligent User Interfaces: Story Understanding and Generation for Context-Aware Interface Design http://csc.media.mit.edu/iuiStories/
• October 31, 2008, Vancouver, BC, Canada: Second ACM Workshop on Story Representation, Mechanism and Context, ACM International Conference on Multimedia 2008 http://ame2.asu.edu/SRMC08/
• November 8-11, 2007, Washington, DC: AAAI Fall Symposium on Intelligent Narrative Technologies http://gel.msu.edu/aaai-fs07-int/
• March 26-28, 2007, Stanford, CA: AAAI Spring Symposium on Machine Reading http://www.cs.washington.edu/homes/pjallen/aaaiss07/index.htm
• October 15, 2004, New York, NY: First ACM Workshop on Story Representation, Mechanism, and Context, 12th ACM International Conference on Multimedia http://www.mm2004.org/acm_mm04_call4workshops.htm
• March 25-27, 2002, Stanford, CA: AAAI Spring Symposium on Mining Answers from Texts and Knowledge Bases http://www.cs.utdallas.edu/~sanda/stanford.html
• August 24, 2000, Baltimore, MD: Johns Hopkins University CLSP/NSF Reading Comprehension Workshop http://www.clsp.jhu.edu/ws2000/groups/reading/index.shtml
• May 4, 2000, Seattle, WA: ANLP/NAACL 2000 Workshop on Reading Comprehension Tests as Evaluation for Computer-Based Language Understanding Systems
  • Charniak, Eugene et al. (2000). Reading comprehension programs in a statistical language processing class.
  • Palmer, David D. (2000). Some challenges of developing fully-automated systems for taking audio comprehension exams.
  • Riloff, Ellen, & Thelen, Michael (2000). A rule-based question answering system for reading comprehension tests.
  • Schwitter, Rolf, Mollá, Diego, Fournier, Rachel, & Hess, Michael (2000). Answer extraction: Towards better evaluations of NLP Systems. http://www.comp.mq.edu.au/~rolfs/papers/rc_final.pdf
  • Wang, W. et al. (2000). A question answering system developed as a project in a natural language processing course.
• November 5-7, 1999, North Falmouth, MA: AAAI 1999 Fall Symposium on Narrative Intelligence http://www-2.cs.cmu.edu/~michaelm/NISchedule.html

Story understanding programs

1. Story comprehension model (1972; MIT; demons)
   • Charniak, Eugene (1972). Toward a model of children's story comprehension (Technical Report AITR-266). Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA. ftp://publications.ai.mit.edu/ai-publications/pdf/AITR-266.pdf
2. Ms. Malaprop (1977; University of Geneva/Yale; frames)
   • Charniak, Eugene (1977). Ms. Malaprop, a language comprehension program. In Proceedings of the Fifth International Conference on Artificial Intelligence.
   • Charniak, Eugene (1977). A framed PAINTING: The representation of a common sense knowledge fragment. Cognitive Science, 1, 355-394.
3. QUALM (1977; Yale; question answering theory and program, used by SAM)
   • Lehnert, Wendy G. (1978). The process of question answering. Hillsdale, NJ: Erlbaum.
4. News article comprehension model (1977; MIT; frames, linking, mapping)
   • Rosenberg, Steven T. (1977). Frame-based text processing (Technical Report AIM-431). Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA. ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-431.pdf
5. SAM (1978; Yale; scripts)
   • Cullingford, Richard E. (1978). Script application: Computer understanding of newspaper stories (Technical Report YALE/DCS/tr116). New Haven, CT: Computer Science Department, Yale University.
   • Cullingford, Richard E. (1981). SAM and Micro SAM. In Roger C. Schank, & Christopher K. Riesbeck (Eds.), Inside computer understanding. Hillsdale, NJ: Erlbaum.
   • Micro SAM code: http://www-cgi.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/areas/nlp/nlu/icu/0.html
6. PAM (1978; Yale; plans, goals)
   • Wilensky, Robert (1978). Understanding goal-based stories (Technical Report YALE/DCS/tr140). New Haven, CT: Computer Science Department, Yale University.
   • Wilensky, Robert (1981). PAM and Micro PAM. In Roger C. Schank, & Christopher K. Riesbeck (Eds.), Inside computer understanding. Hillsdale, NJ: Erlbaum.
   • Wilensky, Robert (1982). Points: A theory of the structure of stories in memory. In Wendy G. Lehnert & Martin H. Ringle (Eds.), Strategies for natural language processing (pp. 345-374). Hillsdale, NJ: Erlbaum.
7. CYRUS (1978; Yale; episodic memory)
   Kolodner, Janet L. (1984). Retrieval and organizational strategies in conceptual memory: A computer model. Hillsdale, NJ: Erlbaum.
8. POLITICS (1979; Yale; political ideology)
   • Carbonell, Jaime (1979). Subjective understanding: Computer models of belief systems (Technical Report YALE/DCS/tr150). New Haven, CT: Computer Science Department, Yale University.
9. FRUMP (1979; Yale; news story skimming)
   • DeJong, Gerald F. (1979). Skimming stories in real time: An experiment in integrated understanding (Technical Report YALE/DCS/tr158). New Haven, CT: Computer Science Department, Yale University.
10. IPP (1980; Yale; generalization, episodic memory, integrated parsing)
    • Lebowitz, Michael (1980). Generalization and memory in an integrated understanding system (Technical Report YALE/DCS/tr186). New Haven, CT: Computer Science Department, Yale University.
11. BORIS (1982; Yale; scripts, plans, goals, emotions, themes, integrated parsing)
    • Dyer, Michael G. (1983) In-depth understanding. Cambridge, MA: MIT Press.
    • Lehnert, Wendy G. (1982). Plot units: A narrative summarization strategy. In Wendy G. Lehnert & Martin H. Ringle (Eds.), Strategies for natural language processing (pp. 375-412). Hillsdale, NJ: Erlbaum.
    • Lehnert, Wendy G., Dyer, Michael G., Johnson, Peter N., Yang, C. J., & Harley, Steve (1983). BORIS-An experiment in in-depth understanding of narratives. Artificial Intelligence, 20(1), 15-62.
    • McDYPAR code: http://www-cgi.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/areas/nlp/nlu/mcdypar/0.html
12. Wimp (1986; Brown; marker passing)
    • Charniak, Eugene (1983). Passing markers: A theory of contextual influence in language comprehension. Cognitive Science, 7, 171-190.
    • Charniak, Eugene (1986). A neat theory of marker passing. In Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 584-588). Menlo Park, CA: AAAI Press.
13. FAUSTUS (1987; UC Berkeley; marker passing)
    • Norvig, Peter (1987). A unified theory of inference for text understanding (Technical Report CSD-87-339). Berkeley, CA: Computer Science Division, University of California, Berkeley. ftp://sunsite.berkeley.edu/pub/techreps/CSD-87-339.html
    • Norvig, Peter (1989). Marker passing as a weak method for text inferencing. Cognitive Science, 13, 569-620.
14. CRAM (1989; UCLA; connectionism, morals)
    • Dolan, Charles (1989). Tensor manipulation networks: Connectionist and symbolic approaches to comprehension, learning, and planning (Technical Report 890030). Los Angeles, CA: Computer Science Department, University of California, Los Angeles. ftp://ftp.cs.ucla.edu/tech-report/198_-reports/890030.pdf
15. OpEd (1989; UCLA; editorial comprehension)
    • Alvarado, Sergio J. (1990). Understanding editorial text: A computer model of argument comprehension. Boston: Kluwer. ftp://ftp.cs.ucla.edu/tech-report/198_-reports/890045.pdf
16. AQUA (1989; Yale; asking and answering questions while reading, explanation)
    • Ram, Ashwin (1989). Question-driven understanding: An integrated theory of story understanding, memory, and learning (Technical Report YALE/DCS/tr710). New Haven, CT: Computer Science Department, Yale University.
    • Ram, Ashwin (1994). AQUA: Questions that drive the explanation process. In Roger C. Schank, Alex Kass, & Christopher K. Riesbeck (Eds.), Inside case-based explanation (pp. 207-261). Hillsdale, NJ: Erlbaum.
17. ROBIN (1989; UCLA; connectionism)
    • Lange, Trent E., & Dyer, Michael G. (1989). High-level inferencing in a connectionist network. Connection Science, 1, 181-217. ftp://ftp.cs.ucla.edu/tech-report/198_-reports/890063.pdf
18. EL/Epilog/Ecologic (1989; University of Alberta, University of Rochester, Boeing; natural-language-like logical representation)
    • Schubert, Lenhart K., & Hwang, Chung Hee (1989). An episodic knowledge representation for narrative texts. In Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning (pp. 444-458). San Mateo, CA: Morgan Kaufmann.
    • Schubert, Lenhart K., & Hwang, Chung Hee (2000). Episodic logic meets Little Red Riding Hood: A comprehensive natural representation for language understanding. In Lucja M. Iwanska and Stuart C. Shapiro (Eds.), Natural language processing and knowledge representation (pp. 111-174). Cambridge, MA: MIT Press. http://www.cs.rochester.edu/~schubert/papers/el-meets-lrrh.ps
    • Program code: http://www.cs.rochester.edu/research/epilog/
19. KT (1989; IBM, University of Western Ontario; logic, commonsense reasoning)
    • Dahlgren, Kathleen, McDowell, Joyce, & Stabler, Jr., Edward P. (1989). Knowledge representation for commonsense reasoning with text. Computational Linguistics, 15(3), 149-170. http://acl.ldc.upenn.edu/J/J89/J89-3002.pdf
20. SWALE (1990; Yale; explanation)
    • Schank, Roger C. (1986). Explanation patterns: Understanding mechanically and creatively. Hillsdale, NJ: Erlbaum.
    • Schank, Roger C., Kass, Alex, & Riesbeck, Christopher K. (Eds.). (1994). Inside case-based explanation. Hillsdale, NJ: Erlbaum.
    • Micro SWALE code: http://www.cs.indiana.edu/~leake/cbr/code/
21. Retriever/Anon (1990; Yale; indexing and retrieving planning knowledge)
    • Owens, Chris C. (1990). Indexing and retrieving abstract planning knowledge (Doctoral dissertation). New Haven, CT: Computer Science Department, Yale University.
    • Owens, Chris C. (1994). Retriever and Anon: Retrieving structures from memory. In Roger C. Schank, Alex Kass, & Christopher K. Riesbeck (Eds.), Inside case-based explanation (pp. 89-126). Hillsdale, NJ: Erlbaum.
22. Accepter (1990; Yale; evaluating explanations)
    • Leake, David B. (1992). Evaluating explanations: A content theory. Hillsdale, NJ: Erlbaum.
    • Leake, David B. (1994). Accepter: Evaluating explanations. In Roger C. Schank, Alex Kass, & Christopher K. Riesbeck (Eds.), Inside case-based explanation (pp. 167-206). Hillsdale, NJ: Erlbaum.
23. Tweaker/ABE (1990; Yale; adapting explanations)
    • Kass, Alex M. (1990). Developing creative hypotheses by adapting explanations (Doctoral dissertation). New Haven, CT: Computer Science Department, Yale University.
    • Kass, Alex M. (1994). Tweaker: Adapting old explanations to new situations. In Roger C. Schank, Alex Kass, & Christopher K. Riesbeck (Eds.), Inside case-based explanation (pp. 263-295). Hillsdale, NJ: Erlbaum.
24. TACITUS (1990; SRI; logic, weighted abduction)
    • Hobbs, Jerry R., Stickel, Mark E., Appelt, Douglas, E., & Martin, Paul (1993). Interpretation as abduction. In Fernando C. N. Pereira & Barbara J. Grosz (Eds.), Natural language processing (pp. 69-142). Cambridge, MA: MIT Press. http://www.ai.sri.com/~hobbs/interp-abduct-ai.ps
    • Hobbs, Jerry R. (forthcoming). Discourse and inference. http://www.isi.edu/~hobbs/disinf-tc.html
25. Wimp3 (1990; Brown; probabilistic account of marker passing)
    • Goldman, Robert P. (1990). A probabilistic approach to language understanding (Technical Report CS-90-34). Providence, RI: Computer Science Department, Brown University.
    • Carroll, Glenn, & Charniak, Eugene (1991). A probabilistic analysis of marker-passing techniques for plan recognition (Technical Report CS-91-44). Providence, RI: Computer Science Department, Brown University. http://www.cs.brown.edu/publications/techreports/reports/CS-91-44.html
26. DISCERN (1990; UCLA; scripts, connectionism)
    • Miikkulainen, Risto, & Dyer, Michael G. (1991). Natural language processing with modular PDP networks and distributed lexicon. Cognitive Science, 15, 343-400.
    • Miikkulainen, Risto (1993). Subsymbolic natural language processing. Cambridge, MA: MIT Press. ftp://ftp.cs.utexas.edu/pub/neural-nets/papers/miikkulainen.diss.tar
    • Program code: ftp://ftp.cs.utexas.edu/pub/neural-nets/software/
27. THUNDER (1991; UCLA; irony, ethics)
    • Reeves, John (1991). Computational morality: A process model of belief conflict and resolution for story understanding (Technical report 910017). Los Angeles, CA: Computer Science Department, University of California, Los Angeles. ftp://ftp.cs.ucla.edu/tech-report/1991-reports/910017.pdf
28. ARIEL (1991; UCLA; analogy, editorial comprehension)
    • August, Stephanie (1991). ARIEL: An approach to understanding analogies in arguments (Technical Report 910051). Los Angeles, CA: Computer Science Department, University of California, Los Angeles. ftp://ftp.cs.ucla.edu/tech-report/1991-reports/910051.pdf
29. Construction-integration model (1992; University of Colorado at Boulder; construction-integration)
    • Mross, Ernest F., & Roberts, Jonathan O. (1992). The construction-integration model: A program and manual (Technical Report 92-14). Boulder, CO: Institute of Cognitive Science, University of Colorado at Boulder. http://ics.colorado.edu/techpubs/pdf/92-14.pdf
30. Automatic inference (1992; UC Berkeley; probabilistic account of marker passing)
    • Wu, Dekai (1992). Automatic inference: A probabilistic basis for natural language interpretation (Technical Report CSD-92-692). Berkeley, CA: Computer Science Division, University of California, Berkeley. ftp://sunsite.berkeley.edu/pub/techreps/CSD-92-692.html
31. Story Gestalt model (1992; CMU; scripts, connectionism)
    • St. John, Mark F. (1992). The story gestalt: A model of knowledge-intensive processes in text comprehension. Cognitive Science, 16, 271-306.
32. REMIND (1992; UCLA; spreading activation, memory retrieval)
    • Lange, Trent E., & Wharton, Charles M. (1992). Remind: Retrieval from episodic memory by inferencing and disambiguation (Technical report 920047). Los Angeles, CA: Computer Science Department, University of California, Los Angeles. ftp://ftp.cs.ucla.edu/tech-report/1992-reports/920047.pdf
33. KERNEL (1993; Unisys; complex interaction among system modules)
    • Palmer, Martha Stone, Passonneau, Rebecca J., Weir, Carl, & Finin, Timothy W. (1993). The KERNEL text understanding system. Artificial Intelligence, 63(1-2), 17-68.
34. Golden and Rumelhart model (1993; UT Dallas, Stanford; situation-state space)
    • Golden, R. M., & Rumelhart, D. E. (1993). A parallel distributed processing model of story comprehension and recall. Discourse Processes, 16, 203-237.
35. AbMaL (1994; UC Irvine/Northwestern; emotions, situation calculus)
    • O'Rorke, Paul, & Ortony, Andrew (1994). Explaining emotions. Cognitive Science, 18, 283-323.
36. COMPERE (1995; Georgia Tech; integrated syntax-semantics)
    • Mahesh, Kavi (1995). Syntax-semantics interaction in sentence understanding (Technical Report GIT-CC-95-10). Atlanta, GA: College of Computing, Georgia Institute of Technology. ftp://ftp.cc.gatech.edu/pub/tech_reports/95/GIT-CC-95-10.ps.Z
37. SNePS/Cassie (1995; SUNY Buffalo; propositional semantic networks, beliefs)
    • Shapiro, Stuart C., & Rapaport, William J. (1995). An introduction to a computational reader of narratives. In Judith F. Duchan, Gail A. Bruder, Lynne E. Hewitt (Eds.), Deixis in narrative (pp. 79-105). Hillsdale, NJ: Erlbaum.
    • Program code: ftp://ftp.cse.buffalo.edu/pub/sneps/
38. Modified construction-integration model (1995; University of Chicago; connectionism, construction-integration model)
    • Langston, Mark C., Trabasso, Tom, & Magliano, Joseph P. (1999). A connectionist model of narrative comprehension. In Ashwin Ram, & Kenneth Moorman (Eds.), Understanding Language Understanding (pp. 181-226). Cambridge, MA: MIT Press.
39. Meta-AQUA (1996; Georgia Tech; learning, empirical study on TALE-SPIN stories)
    • Cox, Michael T. (1996). Introspective multistrategy learning: Constructing a learning strategy under reasoning failure (Technical Report GIT-CC-96-06). Atlanta, GA: College of Computing, Georgia Institute of Technology.
40. ThoughtTreasure (1996; Signiform; plans, goals, emotions, grids, simulation)
    • Mueller, Erik T. (1998). Natural language processing with ThoughtTreasure. New York: Signiform.
    • Mueller, Erik T. (1999). A database and lexicon of scripts for ThoughtTreasure. CogPrints cog00000555.
41. NKRL (1996; CNRS; language for representing content of narratives)
    • Zarri, Gian Piero (1996). NKRL, a knowledge representation language for narrative natural language processing. Proceedings of COLING 1996 (pp. 1032-1035). http://acl.ldc.upenn.edu/C/C96/C96-2181.pdf
42. ISAAC (1997; Georgia Tech; creative reading)
    • Moorman, Kenneth (1997). A functional theory of creative reading: Process, knowledge, and evaluation (Doctoral dissertation). Atlanta, GA: College of Computing, Georgia Institute of Technology.
    • Moorman, Kenneth, & Ram, Ashwin (1994). Integrating creativity and reading: A functional approach. In Sixteenth Annual Conference of the Cognitive Science Society
43. KARMA (1997; UC Berkeley; x-schemas, metaphor, aspect)
    • Narayanan, Srinivas S. (1997). Knowledge-based action representations for metaphor and aspect (KARMA) (Doctoral dissertation). University of California, Berkeley. http://www.icsi.berkeley.edu/~snarayan/thesis.pdf
44. Deep Read (1999; MITRE; reading comprehension)
    • Hirschman, Lynette, Light, Marc, Breck, Eric, & Burger, John D. (1999). Deep Read: A reading comprehension system. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics.
    • Hirschman, Lynette (1999). Reading comprehension: A grand challenge for human language technology (presentation).
    • Hirschman, Lynette (1999). Deep Read (project plan).
    • Hirschman, Lynette (2000). Deep Read: Natural language understanding grand challenge application (presentation).
45. Spatio-Temporal Model (2000; LIMSI-CNRS; physical model)
    • Gerard, S., & Sansonnet, Jean-Paul (2000). A spatio-temporal model for the representation of situations described in narrative texts. Proceedings of NLP 2000 (pp. 176-184).
46. Aquareas (2000; DSO National Laboratories, Singapore; reading comprehension)
    • Ng, Hwee Tou, Teo, Leong Hwee, & Kwan, Jennifer Lai Pheng (2000). A machine learning approach to answering questions for reading comprehension tests. In Proceedings of the 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000) (pp. 124-132). http://www.comp.nus.edu.sg/~nght/pubs/emnlp_vlc00.pdf.gz
47. Reading comprehension programs (2000; Brown University; reading comprehension)
    • Charniak, Eugene et al. (2000). Reading comprehension programs in a statistical language processing class. In Proceedings of the ANLP/NAACL 2000 Workshop on Reading Comprehension Tests as Evaluation for Computer-Based Language Understanding Systems.
48. Quarc (2000; University of Utah; reading comprehension)
    • Riloff, Ellen, & Thelen, Michael (2000). A rule-based question answering system for reading comprehension tests. In Proceedings of the ANLP/NAACL 2000 Workshop on Reading Comprehension Tests as Evaluation for Computer-Based Language Understanding Systems.
49. Story understanding model (2003; New Mexico State University; commonsense aspects)
    • Chavez, Jr., Nemecio R. (2003). Commonsense aspects of story understanding. New Mexico Alliance for Graduate Education and the Professoriate 3rd Annual Conference (poster presentation). Socorro, NM.
50. Model-based story understanding program (2003; IBM Research; event calculus, satisfiability)
    • Mueller, Erik T. (2003). Story understanding through multi-representation model construction. In Graeme Hirst & Sergei Nirenburg (Eds.), Text Meaning: Proceedings of the HLT-NAACL 2003 Workshop (pp. 46-53). East Stroudsburg, PA: Association for Computational Linguistics.
    • Mueller, Erik T. (2004). Understanding script-based stories using commonsense reasoning. Cognitive Systems Research, 5(4), 307-340.
    • Mueller, Erik T. (2004). Event calculus reasoning through satisfiability. Journal of Logic and Computation, 14(5), 703-730.
    • Mueller, Erik T. (2006). Commonsense reasoning. San Francisco: Morgan Kaufmann. http://www.amazon.com/exec/obidos/ASIN/0123693888/
    • Mueller, Erik T. (2007). Modelling space and time in narratives about restaurants. Literary and Linguistic Computing, 22(1), 67-84. http://dx.doi.org/10.1093/llc/fql014
51. Distributed Situation Space (DSS) model (2003; Tilburg, Nijmegen, Max Planck Institute for Psycholinguistics; microworld, situation-state space, self-organizing maps)
    • Frank, Stefan L., Koppen, Mathieu, Noordman, Leo G. M., Vonk, Wietske (2003). Modeling knowledge-based inferences in story comprehension. Cognitive Science, 27, 875-910.
52. Semantic Stories (2003; University of Edinburgh; plot analysis, latent semantic analysis [LSA], event calculus)
    • Halpin, Harry Reeves (2003) The plots of children and machines: The statistical and symbolic semantic analysis of narratives (MSc. thesis). School of Informatics, University of Edinburgh. http://www.semanticstories.org/thesis/mscthesis.pdf
    • Halpin, Harry, & Moore, Johanna D. (2006). Event Extraction in a Plot Advice Agent. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics.
53. CPL (2005; Boeing; computer-processable language)
    • Clark, Peter, Harrison, Philip, Jenkins, Thomas, Thompson, John, & Wojcik, Richard H. (2005). Acquiring and using world knowledge using a restricted subset of English. Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference (pp. 506-511).
54. STORY (2005; University of Maryland and University of Naples "Federico II"; extracting stories from text and other data sources)
    • Fayzullin, Marat, Subrahmanian, V.S., Albanese, Massimiliano, Cesarano, Carmine, & Picariello, Antonio (in press). Story creation from heterogeneous data sources. Multimedia Tools and Applications.

Early natural language understanding programs

• SHRDLU (1971; MIT; procedural representation, blocks world)
  • Winograd, Terry (1972). Understanding natural language. New York: Academic Press.
  • Program code: http://hci.stanford.edu/~winograd/shrdlu/
• MARGIE (1973; Stanford; conceptual dependency [CD], paraphrase, inference)
  • Schank, Roger C., Goldman, Neil, Rieger, Chuck, & Riesbeck, Christopher K. (1973). MARGIE: Memory, analysis, response generation and inference in English. In Proceedings of the Third International Joint Conference on Artificial Intelligence (pp. 255-261).
  • Schank, Roger C., Goldman, Neil, Rieger, Chuck, & Riesbeck, Christopher K. (1975). Inference and paraphrase by computer. Journal of the ACM, 22(3), 309-328.

Story understanding bibliography

• Duchan, Judith F., Bruder, Gail A., & Hewitt, Lynne E. (Eds.). (1995). Deixis in narrative. Hillsdale, NJ: Erlbaum.
• Kautz, Henry A. (1991). A formal theory of plan recognition and its implementation. In James F. Allen, Henry A. Kautz, Richard N. Pelavin, & Josh D. Tenenberg (Eds.), Reasoning about plans (pp. 69-125). San Mateo, CA: Morgan Kaufmann.
• Lehnert, Wendy G. (1994). Cognition, computers, and car bombs: How Yale prepared me for the 1990s. In Roger C. Schank, & Ellen Langer (Eds.), Beliefs, reasoning, and decision making (pp. 143-173). Hillsdale, NJ: Erlbaum.
• McCarthy, John (1990). An example for natural language understanding and the AI problems it raises. In John McCarthy, Formalizing common sense (pp. 70-76). Norwood, NJ: Ablex. http://www-formal.stanford.edu/jmc/mrhug.html
• Minsky, Marvin (2002). The emotion machine (Part 6). New York: Pantheon. http://web.media.mit.edu/~minsky/E6/eb6.html
• Mueller, Erik T. (1999). Prospects for in-depth story understanding by computer. CogPrints cog00000554. http://web.media.mit.edu/~mueller/papers/storyund.html
• Mueller, Erik T. (2002). Story understanding. In Nature Encyclopedia of Cognitive Science. London: Nature Publishing Group.
• Norvig, Peter, & Wilensky, Robert (1990). A critical evaluation of commensurable abduction models for semantic interpretation. In Proceedings of the 13th COLING (Volume 3, pp. 225-230). http://www.norvig.com/coling.ps
• Nossum, Rolf (2003). A contextual approach to the logic of fiction. In Modeling and Using Context, 4th International and Interdisciplinary Conference (CONTEXT 2003) (pp. 233-244).
• Ram, Ashwin, & Moorman, Kenneth (1999). Understanding Language Understanding. Cambridge, MA: MIT Press.
• Rumelhart, David E. (1975). Notes on a schema for stories. In Daniel G. Bobrow, & Allan Collins (Eds.), Representation and understanding: Studies in cognitive science (pp. 211-236). New York: Academic Press.
• Sanders, Kathryn E. (1989). A logic for emotions: A basis for reasoning about commonsense psychological knowledge (Technical Report CS-89-23). Providence, RI: Computer Science Department, Brown University.
• Schank, Roger C., & Abelson, Robert P. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Erlbaum.
• Schank, Roger C., & Riesbeck, Christopher K. (Eds.). (1981). Inside computer understanding. Hillsdale, NJ: Erlbaum.
• Schank, Roger C. (1982). Dynamic memory. New York: Cambridge University Press.
• ter Meulen, Alice G. B. (1995). Representing time in natural language. Cambridge, MA: MIT Press.

Story understanding and psychology

• Beeman, Mark, & Chiarello, Christine (Eds.). (1998). Right hemisphere language comprehension. Mahwah, NJ: Erlbaum.
• Clark, Herbert H. (1977). Bridging. In Philip N. Johnson-Laird, & P. Wason (Eds.), Thinking: Readings in Cognitive Science. Cambridge University Press.
• Emmott, Catherine (1997). Narrative comprehension: A discourse perspective. Oxford: Oxford University Press.
• Goldman, Susan R., Graesser, Arthur C., & van den Broek, Paul (1999). Narrative comprehension, causality, and coherence. Mahwah, NJ: Erlbaum.
• Graesser, Arthur C., Singer, Murray, and Trabasso, Tom (1994). Constructing inferences during narrative text comprehension. Psychological Review, 101(3), 371-395.
• Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87(4), 329-354.
• Kintsch, Walter (1988). The role of knowledge in discourse comprehension: A construction-integration model. Psychological Review, 95, 163-182.
• Kintsch, Walter (1998). Comprehension: A paradigm for cognition. Cambridge University Press.
• Kintsch, W., & van Dijk, Teun A. (1978). Toward a model of text comprehension and production. Psychological Review, 85, 363-394.
• McKoon, Gail, & Ratcliff, Roger (1992). Inference during reading. Psychological Review, 99(3), 440-466.
• McKoon, Gail, & Ratcliff, Roger (1986). Inferences about predictable events. Journal of Experimental Psychology: Learning, Memory, and Cognition, 12(1), 82-91.
• Slobin, Dan I. (2003). The many ways to search for a frog: Linguistic typology and the expression of motion events. S. Strömqvist & L. Verhoeven (Eds.), Relating events in narrative: Typological and contextual perspectives. Mahwah, NJ: Erlbaum. http://ihd.berkeley.edu/slobin-frog.pdf
• St. George, Marie, Mannes, Suzanne, and Hoffman, James E. (1997). Individual differences in inference generation: An ERP analysis. Journal of Cognitive Neuroscience, 9(6), 776-787.
• Tanenhaus, Michael K., Spivey-Knowlton, Michael J., Eberhard, Kathleen M., & Sedivy, Julie C. (1995). Integration of visual and linguistic information in spoken language comprehension. Science, 268, 1632-1634.
• van Dijk, Teun A., & Kintsch, Walter (1983). Strategies of discourse comprehension. New York: Academic Press.
• Van Petten, Cyma, & Kutas, Marta (1990). Interactions between sentence context and word frequency in event-related brain potentials. Memory & Cognition, 18(4), 380-393.
• Zwaan, Rolf A., & Radvansky, Gabriel A. (1998). Situation models in language and memory. Psychological Bulletin, 123(2), 162-185. http://freud.psy.fsu.edu/~zwaan/pb.pdf

Story understanding and literary theory

• Prince, Gerald (1982). Narratology: The form and functioning of narrative Berlin: Mouton.
• Rimmon-Kenan, Shlomith (2002). Narrative fiction: Contemporary poetics (2nd edition). London: Routledge.
• Ryan, Marie-Laure (1991). Possible worlds, artificial intelligence and narrative theory. Bloomington, IN: Indiana University Press.

Story generation programs

1. TALE-SPIN (1976; Yale; simulation, goals, plans)
   • Meehan, James (1976). The metanovel: Writing stories by computer (Technical Report YALE/DCS/tr074). New Haven, CT: Computer Science Department, Yale University.
   • Meehan, James (1981). TALE-SPIN and Micro TALE-SPIN. In Roger C. Schank, & Christopher K. Riesbeck (Eds.), Inside computer understanding. Hillsdale, NJ: Erlbaum.
   • Sack, Warren (1992). Micro-Talespin: A story generator. (A translation of Micro TALE-SPIN into Common Lisp.)
   • Clark, Peter (1999). Story generation and aviation incident representation. (Simple Prolog version of TALE-SPIN applied to the generation of aviation incident stories.) http://www.cs.utexas.edu/users/pclark/working_notes/014.pdf http://www.cs.utexas.edu/users/pclark/working_notes/talespin.pl
2. UNIVERSE (1983; Columbia; person frames, stereotypes, past events)
   • Lebowitz, Michael (1983). Creating a story-telling universe. In Proceedings of the 8th International Joint Conference on Artificial Intelligence (pp. 63-65).
3. ROALD (1983; University of Exeter; goals, simulation)
   • Yazdani, Masoud (1983). Generating events in a fictional world of stories (Research Report R-113). Department of Computer Science, University of Exeter.
4. Racter (1984; William Chamberlain and Thomas Etter; syntax directives)
   • Racter (1984). The Policeman's Beard is Half Constructed. New York: Warner.
   • Racter FAQ: http://www.robotwisdom.com/ai/racterfaq.html
5. Daydreamer (1987; UCLA; daydreaming, planning, episodic memory, emotions)
   • Mueller, Erik T., & Dyer, Michael G. (1985). Towards a computational theory of human daydreaming. Proceedings of the Seventh Annual Conference of the Cognitive Science Society (pp. 120-129). Hillsdale, NJ: Lawrence Erlbaum.
   • Mueller, Erik T. (1987). Daydreaming and computation: A computer model of everyday creativity, learning, and emotions in the human stream of thought (Technical Report CSD-870017, UCLA-AI-87-8). Doctoral dissertation, Computer Science Department, University of California, Los Angeles. Ann Arbor, MI: UMI.
   • Mueller, Erik T. (1990). Daydreaming in humans and machines: A computer model of the stream of thought. Norwood, NJ: Ablex.
6. ALIBI (1987-1990, BGU, Israel; planner decomposing an accusation and proposing a self-exonerating explanation)
   • Kuflik, Tsvi,  Nissan, Ephraim,  & Puni, Gilad (1989). Finding excuses with ALIBI: Alternative plans that are deontically more defensible. In Proceedings of the International Symposium on Communication, Meaning, and Knowledge vs. Information Technology, Lisbon.
   • Fakher-Eldeen, Fadel,  Kuflik, Tsvi,  Nissan, Ephraim,  Puni, Gilad,  Salfati, Roni,  Shaul, Yuval,  & Spanioli, Auni (1993). Interpretation of imputed behaviour in ALIBI (1 to 3) and SKILL. Informatica e Diritto, 19(2(1/2)), 213-242.
   • Nissan, Ephraim,  & Rousseau, Daniel (1997). Towards AI formalisms for legal evidence. In Zbigniew W. Ras, & Andrzej Skowron (Eds.), Foundations of Intelligent Systems: Proceedings of the 10th International Symposium (pp. 328-337). Berlin: Springer Verlag.
7. Author (1989; Yale; reconstructive and dynamic memory)
   • Dehn, Natalie (1981). Memory in story invention. In Proceedings of the Third Annual Conference of the Cognitive Science Society.
   • Dehn, Natalie (1989). Computer story-writing: The role of reconstructive and dynamic memory (Technical Report YALE/DCS/tr712). New Haven, CT: Computer Science Department, Yale University.
8. Oz (1989; CMU; interactive drama, goals, emotions)
   • Smith, Sean, & Bates, Joseph (1989). Towards a theory of narrative for interactive fiction (Technical Report CMU-CS-89-121). School of Computer Science, Carnegie Mellon University, Pittsburgh, PA. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/oz/web/papers/CMU-CS-89-121.ps
   • Bates, Joseph, Loyall, A. Bryan, & Reilly, W. Scott (1992). Integrating reactivity, goals, and emotion in a broad agent. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/oz/web/papers/CMU-CS-92-142.ps
   • Reilly, W. Scott Neal (1996). Believable social and emotional agents (Technical Report CMU-CS-96-138). School of Computer Science, Carnegie Mellon University, Pittsburgh, PA. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/oz/web/papers/CMU-CS-96-138-1sided.ps
   • Loyall, A. Bryan (1997). Believable agents: Building interactive personalities (Technical Report CMU-CS-97-123). School of Computer Science, Carnegie Mellon University, Pittsburgh, PA. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/oz/web/papers/CMU-CS-97-123.ps
9. TAILOR (1991; University of Calgary; story space, planning)
   • Smith, Tony C., & Witten, Ian H. (1991). A planning mechanism for generating story text (Technical Report 1991-431-15). Calgary, Canada: Department of Computer Science, University of Calgary. http://pharos.cpsc.ucalgary.ca/Dienst/Repository/2.0/Body/ncstrl.ucalgary_cs/1991-431-15/pdf
10. MINSTREL (1992; UCLA; planning advice themes, case-based reasoning)
    • Turner, Scott R. (1994). The creative process: A computer model of storytelling and creativity. Hillsdale, NJ: Erlbaum. ftp://ftp.cs.ucla.edu/tech-report/1992-reports/920057.pdf
11. Dramatica (1993; Melanie Anne Phillips and Chris Huntley; Dramatica theory)
    • Phillips, Melanie Anne, & Huntley, Chris (1993). Dramatica: A new theory of story. http://www.dramatica.com/theory/theory_book/dtb.html
    • Dramatica web site: http://www.dramatica.com/
12. Scenario Synthesizer (1998; Rutgers; formal scenario)
    • Galitsky, Boris (1998). A formal scenario and metalanguage support means to reason about it (Technical report 98-28). New Brunswick, NJ: DIMACS. ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/1998/98-28.ps.gz
    • Galitsky, Boris (1999). Narrative generation for the control of buyer's impression.
13. Agent Stories (1999; MIT; authoring, story pieces, story agents)
    • Brooks, Kevin Michael (1999). Metalinear cinematic narrative: Theory, process, and tool (Doctoral dissertation). Cambridge, MA: Program in Media Arts and Sciences, School of Architecture and Planning, Massachusetts Institute of Technology http://xenia.media.mit.edu/~brooks/dissertation.html
14. BRUTUS (2000; RPI; story grammars, frames)
    • Bringsjord, Selmer, & Ferrucci, David A. (2000). Artificial intelligence and literary creativity. Mahwah, NJ: Erlbaum.
15. Author/StoryBook (2000; North Carolina State University; sentence planner, functional systemic grammar)
    • Callaway, Charles (2000). Narrative prose generation (Ph.D. thesis). North Carolina State University. http://tcc.itc.it/people/callaway/pubs.html
16. MEXICA (2001; Universidad Nacional Autonoma de Mexico, University of Birmingham; engagement-reflection)
    • Pérez y Pérez, Rafael & Sharples, Mike (2001) MEXICA: A computer model of a cognitive account of creative writing. Journal of Experimental and Theoretical Artificial Intelligence, 13(2), 119-139. http://www.eee.bham.ac.uk/sharplem/Papers/mexica_jetai.pdf
17. MAKEBELIEVE (2002; MIT; Open Mind Common Sense)
    • Liu, Hugo, & Singh, Push (2002). MAKEBELIEVE: Using commonsense knowledge to generate stories. In Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence (pp. 957-958). http://web.media.mit.edu/~hugo/publications/papers/AAAI2002-makebelieve.pdf
18. IDtension (2004; LINC, University of Paris 8; graph-based narrative structures)
    • Szilas, Nicolas, & Rety, Jean-Hugues (2004). Minimal structure for stories. In Proceedings of the First ACM Workshop on Story Representation, Mechanism, and Context, 12th ACM International Conference on Multimedia (pp. 25-32). New York: ACM.
19. Fabulist (2004; North Carolina State University; planning, plot coherence, character believability)
    • Riedl, Mark Owen (2004). Narrative generation: Balancing plot and character (Doctoral dissertation). Raleigh, NC: Department of Computer Science, North Carolina State University. http://people.ict.usc.edu/~riedl/pubs/dissertation.pdf
20. System for automatic story generation (2005; Universidad Complutense de Madrid; CBR)
    • Gervás, Pablo, Díaz-Agudo, Belén, Peinado, Federico, & Hervás, Raquel (2005). Story plot generation based on CBR. Knowledge-Based Systems, 18(4-5), 235-242.

Reading comprehension tests

• CTBS (Comprehensive Tests of Basic Skills)
• ITBS (Riverside Publishing Iowa Tests of Basic Skills Forms K and L [1993], Form M [1996], Form A [2001])
• Master Skills Series (American Education Publishing)
• NAEP (National Assessment of Educational Progress)
• Remedia (used by MITRE and ANLP/NAACL 2000 Workshop)
• SAT/9 (Stanford Achievement Test Series 9)
• WRCT (Wisconsin Reading Comprehension Test)

Technical report/paper libraries

• Georgia Tech technical reports: ftp://ftp.cc.gatech.edu/pub/tech_reports/
• MIT CSAIL technical reports: http://www.ai.mit.edu/research/publications/
• UC Berkeley technical reports: ftp://sunsite.berkeley.edu/pub/techreps/
• UCLA technical reports: http://www.cs.ucla.edu/csd/pubs/all-reports.html
• Yale technical reports: ftp://ftp.cs.yale.edu/pub/TR/LISTING
• MUC evaluations (information extraction): http://www.itl.nist.gov/iaui/894.02/related_projects/muc/
• TREC-8 QA (question answering): http://trec.nist.gov/pubs/trec8/index.track.html#qa
• TREC-9 QA: http://trec.nist.gov/pubs/trec9/index.track.html#qa
• TREC-10 QA: http://trec.nist.gov/pubs/trec10/t10_proceedings.html
• TREC-11 QA: http://trec.nist.gov/pubs/trec11/t11_proceedings.html

Related bibliographies

• AnswerBus bibliography in automated question answering systems http://answerbus.de/bibliography/index.shtml
• Commonsense bibliography: http://wiki.mako.cc/CommonsenseBibliography
• Information extraction: http://www.isi.edu/info-agents/RISE/
• Natural language understanding systems: http://www.cs.dartmouth.edu/~brd/Teaching/AI/Lectures/Summaries/natlang.html#Natural Language Understanding Systems
• Story generation systems: http://www1.uni-hamburg.de/story-generators/story_generator_systems.html

Commonsense/linguistic databases

• CHILDES: http://childes.psy.cmu.edu/
• Commonsense activities (scripts): http://www.ict.usc.edu/~gordon/carp.html
• Commonsense psychology: http://www.ict.usc.edu/~gordon/cprp.html
• Component Library: http://www.cs.utexas.edu/users/mfkb/RKF/clib.html
• ConceptNet: http://web.media.mit.edu/~hugo/conceptnet/
• FrameNet: http://framenet.icsi.berkeley.edu/
• LCS (Lexical Conceptual Structures) database: http://www.umiacs.umd.edu/~bonnie/LCS_Database_Documentation.html
• OpenCyc: http://www.opencyc.org/
• Open Mind Common Sense: http://openmind.media.mit.edu/
• PropBank: http://www.cs.rochester.edu/~gildea/PropBank/
• Unified Verb Index: http://www.cs.rochester.edu/~gildea/Verbs/
• WordNet: http://wordnet.princeton.edu/

Episodic formulae in narrative analysis

• Nissan, Ephraim (2003). Identification and doing without it. Cybernetics and Systems, 34(4/5), 317-380; 34(6/7), 467-549.
• Nissan, Ephraim, Hall, David Lobina, Emanuele, De La Motte, Robin (2004). A formalism for a case study in the WaterTime project: The city water system in Grenoble, from privatization to remunicipalization. Applied Artificial Intelligence, 18(3/4), 305-366.
• Nissan, Ephraim (2007). Goals, arguments, and deception: A formal representation from the AURANGZEB project. Journal of Intelligent and Fuzzy Systems, 18(3), 281-327.
• Nissan, Ephraim (2008). Nested beliefs, goals, duties, and agents reasoning about their own or each other's body in the TIMUR model: A formalism for the narrative of Tamerlane and the Three Painters. Journal of Intelligent and Robotic Systems, 52(3/4), 515-582.

The logic and games formalization of stories as models of dynamic epistemic logic

• Lowe, Benedikt, & Pacuit, Eric (2008). An abstract approach to reasoning about games with mistaken and changing beliefs. Australasian Journal of Logic, 6, 162-181.
• Lowe, Benedikt, Pacuit, Eric, & Saraf, Sanchit (2009). Identifying the structure of a narrative via an agent-based logic of preferences and beliefs: Formalizations of episodes from CSI: Crime Scene Investigation. In Michael Duvigneau, & Daniel Moldt (Eds.), Proceedings of the Fifth International Workshop on Modelling of Objects, Components, and Agents (pp. 45-63).