A Unified Bayesian Model of Scripts, Frames and Language
| dc.contributor.author | Ferraro, Francis | |
| dc.contributor.author | Durme, Benjamin Van | |
| dc.date.accessioned | 2018-10-31T17:55:06Z | |
| dc.date.available | 2018-10-31T17:55:06Z | |
| dc.date.issued | 2016-02-12 | |
| dc.description | Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence | en_US |
| dc.description.abstract | We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpus-based induction of scenario definitions. Our model unifies prior efforts in discourse-level modeling with that of Fill-more's related notion of frame, as captured in sentence-level, FrameNet semantic parses; as part of this, we resurrect the coupling among Minsky's frames, Schank's scripts and Fill-more's frames, as originally laid out by those authors. Empirically, our approach yields improved scenario representations, reflected quantitatively in lower surprisal and more coherent latent scenarios. | en_US |
| dc.description.sponsorship | This work was supported by a National Science Foundation Graduate Research Fellowship (Grant No. DGE- 1232825) to F.F., and the Johns Hopkins HLTCOE. We would like to thank members of B.V.D.’s lab, especially Chandler May, Keith Levin, and TravisWolfe, along with Ryan Cotterell, Matthew Gormley, and four anonymous reviewers for their feedback. Any opinions expressed in this work are those of the authors. | en_US |
| dc.description.uri | https://www.google.com/url?q=https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12092/11994&sa=U&ved=0ahUKEwiU64CYlJDeAhVF11kKHZAwAs0QFggEMAA&client=internal-uds-cse&cx=016314354884912110518:gwmynp16xuu&usg=AOvVaw2a2VsXzYorHgfeeGvYILz_ | en_US |
| dc.format.extent | 7 pages | en_US |
| dc.genre | conference papers and proceedings pre-print | en_US |
| dc.identifier | doi:10.13016/M24F1MN73 | |
| dc.identifier.citation | Francis Ferraro and Benjamin Van Durme, A Unified Bayesian Model of Scripts, Frames and Language, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/11805 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | AAAI Press | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | learning | en_US |
| dc.subject | natural language processing | en_US |
| dc.subject | semantics | en_US |
| dc.subject | natural languge | en_US |
| dc.subject | UMBC Ebiquity Research Group | en_US |
| dc.title | A Unified Bayesian Model of Scripts, Frames and Language | en_US |
| dc.type | Text | en_US |