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We introduce a novel scheme for parsing a piece of text into its AbstractMeaning Representation (AMR): Graph Spanning based Parsing (GSP). One novelcharacteristic of GSP is that it constructs a parse graph incrementally in atop-down fashion. Starting from the root, at each step, a new node and itsconnections to existing nodes will be jointly predicted. The output graph spansthe nodes by the distance to the root, following the intuition of firstgrasping the main ideas then digging into more details. The \textit{coresemantic first} principle emphasizes capturing the main ideas of a sentence,which is of great interest. We evaluate our model on the latest AMR sembank andachieve the state-of-the-art performance in the sense that no heuristic graphre-categorization is adopted. More importantly, the experiments show that ourparser is especially good at obtaining the core semantics.

Core Semantic First: A Top-down Approach for AMR Parsing