A Discriminative Graph-Based Parser for the Abstract Meaning Representation

被引：0
|
作者：
Flanigan, Jeffrey [1 ]
Thomson, Sam [1 ]
Carbonell, Jaime [1 ]
Dyer, Chris [1 ]
Smith, Noah A. [1 ]
机构：
[1] Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA
来源：
PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 | 2014年
关键词：
INFERENCE;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Meaning Representation (AMR) is a semantic formalism for which a growing set of annotated examples is available. We introduce the first approach to parse sentences into this representation, providing a strong baseline for future improvement. The method is based on a novel algorithm for finding a maximum spanning, connected subgraph, embedded within a Lagrangian relaxation of an optimization problem that imposes linguistically inspired constraints. Our approach is described in the general framework of structured prediction, allowing future incorporation of additional features and constraints, and may extend to other formalisms as well. Our open-source system, JAMR, is available at: http://github.com/jflanigan/jamr
引用
收藏
页码：1426 / 1436
页数：11
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