Abstract
We report improved AMR parsing results by adding a new action to a transitionbased AMR parser to infer abstract concepts and by incorporating richer features produced by auxiliary analyzers such as a semantic role labeler and a coreference resolver. We report final AMR parsing results that show an improvement of 7% absolute in F1 score over the best previously reported result. Our parser is available at: https://github.com/Juicechuan/AMRParsing.
Cite
CITATION STYLE
Wang, C., Xue, N., & Pradhan, S. (2015). Boosting transition-based AMR parsing with refined actions and auxiliary analyzers. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (Vol. 2, pp. 857–862). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p15-2141
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
