Core Semantic First: A Top-down Approach for AMR Parsing
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation
(AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections
to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The core semantic first principle
emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization
is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics.
出版社接受日期13.08.2019
著者Deng Cai, Wai Lam
會議名稱Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP/IJCNLP)
會議開始日03.11.2019
會議完結日07.11.2019
會議地點Hong Kong
會議國家/地區中國
會議論文集題名Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing
出版年份2019
月份11
頁次3799 - 3809
語言美式英語

上次更新時間 2021-13-01 於 23:26