@inproceedings{pradhan-etal-2022-propbank,
title = "{P}rop{B}ank Comes of {A}ge{---}{L}arger, Smarter, and more Diverse",
author = "Pradhan, Sameer and
Bonn, Julia and
Myers, Skatje and
Conger, Kathryn and
O{'}gorman, Tim and
Gung, James and
Wright-bettner, Kristin and
Palmer, Martha",
editor = "Nastase, Vivi and
Pavlick, Ellie and
Pilehvar, Mohammad Taher and
Camacho-Collados, Jose and
Raganato, Alessandro",
booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics",
month = jul,
year = "2022",
address = "Seattle, Washington",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.starsem-1.24",
doi = "10.18653/v1/2022.starsem-1.24",
pages = "278--288",
abstract = "This paper describes the evolution of the PropBank approach to semantic role labeling over the last two decades. During this time the PropBank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions. The number of domains, genres and languages that have been PropBanked has also expanded greatly, creating an opportunity for much more challenging and robust testing of the generalization capabilities of PropBank semantic role labeling systems. We also describe the substantial effort that has gone into ensuring the consistency and reliability of the various annotated datasets and resources, to better support the training and evaluation of such systems",
}
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<abstract>This paper describes the evolution of the PropBank approach to semantic role labeling over the last two decades. During this time the PropBank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions. The number of domains, genres and languages that have been PropBanked has also expanded greatly, creating an opportunity for much more challenging and robust testing of the generalization capabilities of PropBank semantic role labeling systems. We also describe the substantial effort that has gone into ensuring the consistency and reliability of the various annotated datasets and resources, to better support the training and evaluation of such systems</abstract>
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%0 Conference Proceedings
%T PropBank Comes of Age—Larger, Smarter, and more Diverse
%A Pradhan, Sameer
%A Bonn, Julia
%A Myers, Skatje
%A Conger, Kathryn
%A O’gorman, Tim
%A Gung, James
%A Wright-bettner, Kristin
%A Palmer, Martha
%Y Nastase, Vivi
%Y Pavlick, Ellie
%Y Pilehvar, Mohammad Taher
%Y Camacho-Collados, Jose
%Y Raganato, Alessandro
%S Proceedings of the 11th Joint Conference on Lexical and Computational Semantics
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F pradhan-etal-2022-propbank
%X This paper describes the evolution of the PropBank approach to semantic role labeling over the last two decades. During this time the PropBank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions. The number of domains, genres and languages that have been PropBanked has also expanded greatly, creating an opportunity for much more challenging and robust testing of the generalization capabilities of PropBank semantic role labeling systems. We also describe the substantial effort that has gone into ensuring the consistency and reliability of the various annotated datasets and resources, to better support the training and evaluation of such systems
%R 10.18653/v1/2022.starsem-1.24
%U https://aclanthology.org/2022.starsem-1.24
%U https://doi.org/10.18653/v1/2022.starsem-1.24
%P 278-288
Markdown (Informal)
[PropBank Comes of Age—Larger, Smarter, and more Diverse](https://aclanthology.org/2022.starsem-1.24) (Pradhan et al., *SEM 2022)
ACL
- Sameer Pradhan, Julia Bonn, Skatje Myers, Kathryn Conger, Tim O’gorman, James Gung, Kristin Wright-bettner, and Martha Palmer. 2022. PropBank Comes of Age—Larger, Smarter, and more Diverse. In Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, pages 278–288, Seattle, Washington. Association for Computational Linguistics.