ABSTRACT
In this paper we present how the automatic extraction of events from text can be used to both classify narrative texts according to plot quality and produce advice in an interactive learning environment intended to help students with story writing. We focus on the story rewriting task, in which an exemplar story is read to the students and the students rewrite the story in their own words. The system automatically extracts events from the raw text, formalized as a sequence of temporally ordered predicate-arguments. These events are given to a machine-learner that produces a coarse-grained rating of the story. The results of the machine-learner and the extracted events are then used to generate fine-grained advice for the students.
- Steven Abney. 1995. Chunks and dependencies: Bringing processing evidence to bear on syntax. In Jennifer Cole, Georgia Green, and Jerry Morgan, editors, Computational Linguistics and the Foundations of Linguistic Theory, pages 145--164.Google Scholar
- Breck Baldwin. 1997. CogNIAC: A High Precision Pronoun Resolution Engine.Google Scholar
- F. C. Bartlett. 1932. Remembering. Cambridge University Press, Cambridge.Google Scholar
- Johan Bos, Stephen Clark, Mark Steedman, James Curran, and Julia Hockenmaier. 2004. Wide-coverage semantic representations from a CCG parser. In In Proceedings of the 20th International Conference on Computational Linguistics (COLING '04). Geneva, Switzerland. Google ScholarDigital Library
- Jill Burstein, Daniel Marcu, and Kevin Knight. 2003. Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays. IEEE Intelligent Systems, pages 32--39. Google ScholarDigital Library
- S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R Harshman. 1990. Indexing by Latent Semantic Analysis. Journal of the American Society For Information Science, (41):391--407.Google ScholarCross Ref
- Christine Fellbaum. 1998. WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA.Google Scholar
- A. Graesser, P. Wiemer-Hastings, K. Wiemer-Hastings, D. Harter, and N. Person. 2000. Using latent semantic analysis to evaluate the contributions of students in autotutor. Interactive Learning Environments, 8:149--169.Google ScholarCross Ref
- Claire Grover, Colin Matheson, Andrei Mikheev, and Marc Moens. 2000. LT TTT - A Flexible Tokenisation Tool. In Proceedings of the Second Language Resources and Evaluation Conference.Google Scholar
- Harry Halpin, Johanna Moore, and Judy Robertson. 2004. Automatic analysis of plot for story rewriting. In In Proceedings of Empirical Methods in Natural Language Processing, Barcelona, Spain.Google Scholar
- Maya Hickmann. 2003. Children's Discourse: person, space and time across language. Cambridge University Press, Cambridge, UK.Google Scholar
- Hans Kamp and Uwe Reyle. 1993. From Discourse to Logic. Kluwer Academic.Google Scholar
- Thomas. Landauer and Susan Dumais. 1997. A solution to Plato's problem: The Latent Semantic Analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review.Google Scholar
- B. Lemaire, S. Mandin, P. Dessus, and G. Denhire. 2005. Computational cognitive models of summarization assessment skills. In In Proceedings of the 27th Annual Meeting of the Cognitive Science Society, Stressa, Italy.Google Scholar
- Fiona McNeill, Harry Halpin, Ewan Klein, and Alan Bundy. 2006. Merging stories with shallow semantics. In Proceedings of the Knowledge Representation and Reasoning for Language Processing Workshop at the European Association for Computational Linguistics, Genoa, Italy. Google ScholarDigital Library
- Erik T. Mueller. 2003. Story understanding through multi-representation model construction. In Graeme Hirst and Sergei Nirenburg, editors, Text Meaning: Proceedings of the HLT-NAACL 2003 Workshop, pages 46--53, East Stroudsburg, PA. Association for Computational Linguistics. Google ScholarDigital Library
- Ani Nenkova and Rebecca Passonneau. 2004. Evaluating content selection in summarization: The pyramid method. In In Proceedings of the Joint Conference of the North American Association for Computational Linguistics and Human Language Technologies. Boston, USA.Google Scholar
- E. Riloff. 1999. Information extraction as a stepping stone toward story understanding. In Ashwin Ram and Kenneth Moorman, editors, Computational Models of Reading and Understanding. MIT Press. Google ScholarDigital Library
- Judy Robertson and Beth Cross. 2003. Children's perceptions about writing with their teacher and the StoryStation learning environment. Narrative and Interactive Learning Environments: Special Issue of International Journal of Continuing Engineering Education and Life-long Learning.Google Scholar
- Judy Robertson and Peter Wiemer-Hastings. 2002. Feedback on children's stories via multiple interface agents. In International Conference on Intelligent Tutoring Systems, Biarritz, France. Google ScholarDigital Library
- C. Rose, D. Bhembe, A. Roque, S. Siler, R. Srivastava, and K. VanLehn. 2002. A hybrid language understanding approach for robust selection of tutoring goals. In International Conference on Intelligent Tutoring Systems, Biarritz, France. Google ScholarDigital Library
- Event extraction in a plot advice agent
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