The Logic of AMR: Practical, Unified, Graph-Based Sentence Semantics for NLP

Nathan Schneider, Jeffrey Flanigan, Tim O'Gorman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The Abstract Meaning Representation formalism is rapidly emerging as an important practical form of structured sentence semantics which, thanks to the availability of largescale annotated corpora, has potential as a convergence point for NLP research. This tutorial unmasks the design philosophy, data creation process, and existing algorithms for AMR semantics. It is intended for anyone interested in working with AMR data, including parsing text into AMRs, generating text from AMRs, and applying AMRs to tasks such as machine translation and summarization. The goals of this tutorial are twofold. First, it will describe the nature and design principles behind the representation, and demonstrate that it can be practical for annotation. In Part I: The AMR Formalism, participants will be coached in the basics of annotation so that, when working with AMR data in the future, they will appreciate the benefits and limitations of the process by which it was created. Second, the tutorial will survey the state of the art for computation with AMRs. Part II: Algorithms and Applications will focus on the task of parsing English text into AMR graphs, which requires algorithms for alignment, for structured prediction, and for statistical learning. The tutorial will also address graph grammar formalisms that have been recently developed, and future applications such as AMR-based machine translation and summarization. Participants with laptops are encouraged to bring them to the tutorial.
Original languageEnglish
Title of host publicationNAACL HLT 2015, The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, USA, May 31 - June 5, 2015
Number of pages2
Publication statusPublished - 2015


Dive into the research topics of 'The Logic of AMR: Practical, Unified, Graph-Based Sentence Semantics for NLP'. Together they form a unique fingerprint.

Cite this