A Transition-based Algorithm for AMR Parsing


We present a two-stage framework to parse a sentence into its Abstract Meaning Representation (AMR). We first use a dependency parser to generate a dependency tree for the sentence. In the second stage, we design a novel transition-based algorithm that transforms the dependency tree to an AMR graph. There are several advantages with this approach. First, the dependency parser can be trained on a training set much larger than the training set for the tree-to-graph algorithm, resulting in a more accurate AMR parser overall. Our parser yields an improvement of 5% absolute in F-measure over the best previous result. Second, the actions that we design are linguistically intuitive and capture the regularities in the mapping between the dependency structure and the AMR of a sentence. Third, our parser runs in nearly linear time in practice in spite of a worst-case complexity of O(n). The parser is available at: https://github. com/Juicechuan/AMRParsing

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@inproceedings{Wang2015ATA, title={A Transition-based Algorithm for AMR Parsing}, author={Chuan Wang and Nianwen Xue and Sameer Pradhan}, booktitle={HLT-NAACL}, year={2015} }