AMR dependency parsing with a typed semantic algebra

Jonas Groschwitz, Matthias Lindemann, Meaghan Fowlie, Mark Johnson, Alexander Koller

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

14 Citations (Scopus)
1 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationProceedings of the Conference, Vol. 1 (Long Papers)
EditorsIryna Gurevych, Yusuke Miyao
Place of PublicationStroudsburg PA
PublisherAssociation for Computational Linguistics (ACL)
Pages1831-1841
Number of pages11
Volume1
ISBN (Electronic)9781948087322
DOIs
Publication statusPublished - 1 Jan 2018
Event56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
CountryAustralia
CityMelbourne
Period15/07/1820/07/18

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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