Journal article
Predicting Proppian Narrative Functions from Stories in Natural Language
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, v 12(1), pp 107-113
25 Jun 2021
Abstract
Computational narrative systems usually require knowledge about the story world and narrative theory to be encoded in some form of structured knowledge representation formalism, a notoriously time-consuming task requiring expertise in both storytelling and knowledge engineering. In this paper we present an approach that combines supervised machine learning with narrative domain knowledge toward automatically extracting such knowledge from natural language stories, focusing specifically on predicting Proppian narrative functions. Our experiments on a dataset of Russian fairy tales show that our system outperforms an informed baseline and that combining top-down narrative theory and bottom-up statistical models inferred from an annotated dataset increases prediction accuracy with respect to using them in isolation.
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Details
- Title
- Predicting Proppian Narrative Functions from Stories in Natural Language
- Creators
- Josep Valls-Vargas - Drexel UniversityJichen Zhu - Drexel UniversitySantiago Ontañón - Drexel University
- Publication Details
- Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, v 12(1), pp 107-113
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Other Identifier
- 991021859435104721