Abstract
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 language | English |
|---|---|
| Title of host publication | NAACL 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 |
| Pages | 4-5 |
| Number of pages | 2 |
| Publication status | Published - 2015 |