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A Subsymbolic Model of Complex Story Understanding (2005)
Peggy Fidelman
,
Risto Miikkulainen
and
Ralph Hoffman
A computational model of story understanding is presented that is able to process stories consisting of multiple scripts. This model is built from subsymbolic neural networks, but unlike previous such models, it can handle stories of variable structure and length. The model can successfully parse and paraphrase script-based stories that share long sequences of common events, with no confusion between the stories. It also exhibits several aspects of human behavior, including robustness to small changes in the sequence of events and emotion priming effects in response to ambiguous cues. It can therefore serve as a foundation for testing theories of normal and impaired story processing in humans.
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Citation:
In
Proceedings of the 27th Annual Meeting of the Cognitive Science Society
, 2005.
Bibtex:
@InProceedings{fidelman:cogsci05, title={A Subsymbolic Model of Complex Story Understanding}, author={Peggy Fidelman and Risto Miikkulainen and Ralph Hoffman}, booktitle={Proceedings of the 27th Annual Meeting of the Cognitive Science Society}, url="http://nn.cs.utexas.edu/?fidelman:cogsci05", year={2005} }
People
Peggy Fidelman
Former Ph.D. Student
peggyf [at] cs utexas edu
Ralph E. Hoffman
Former Collaborator
ralph hoffman [at] yale edu
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Projects
Neural Network Models of Schizophrenic Language
Since 2003
Demos
A Subsymbolic Model of Schizophrenic Language
Uli Grasemann
2010
Areas of Interest
Natural Language Processing (Cognitive)
Cognitive Science