An Incremental Parser for Abstract Meaning Representation

Marco Damonte, Shay B. Cohen, Giorgio Satta

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

Abstract

Abstract Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences leftto-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.
Original languageEnglish
Title of host publicationProceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Place of PublicationValencia, Spain
PublisherAssociation for Computational Linguistics (ACL)
Pages536-546
Number of pages11
ISBN (Print)978-1-945626-34-0
Publication statusPublished - 7 Apr 2017
Event15th EACL 2017 Software Demonstrations - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017
http://eacl2017.org/
http://eacl2017.org/index.php

Conference

Conference15th EACL 2017 Software Demonstrations
Abbreviated titleEACL 2017
Country/TerritorySpain
CityValencia
Period3/04/177/04/17
Internet address

Fingerprint

Dive into the research topics of 'An Incremental Parser for Abstract Meaning Representation'. Together they form a unique fingerprint.

Cite this