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Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers
Conference paper

Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers

Nianwen Xue, Chuan Wang and Sameer Pradhan
53rd Annual Meeting of the Association for Computational Linguistics, 53 (Beijing, China, 07/26/2015 - 07/31/2015)
07/2015

Abstract

Computational Linguistics
We report improved AMR parsing results by adding a new action to a transition-based 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

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