AMR Dependency Parsing with a Typed Semantic Algebra

被引：0

作者：

Groschwitz, Jonas ^{[1
,2
]}

Lindemann, Matthias ^{[1
]}

Fowlie, Meaghan ^{[1
]}

Johnson, Mark ^{[2
]}

Koller, Alexander ^{[1
]}

机构：

[1] Saarland Univ, Saarbrucken, Germany

[2] Macquarie Univ, Sydney, NSW, Australia

来源：

PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1 | 2018年

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.

引用

页码：1831 / 1841

页数：11

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