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Automatic Enrichment of Abstract Meaning Representations

Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022

DOI:10.63317/3paw9gg45oju

Abstract

Abstract Meaning Representation (AMR) is a semantic graph framework which inadequately represent a number of important semantic features including number, (in)definiteness, quantifiers, and intensional contexts. Several proposals have been made to improve the representational adequacy of AMR by enriching its graph structure. However, these modifications are rarely added to existing AMR corpora due to the labor costs associated with manual annotation. In this paper, we develop an automated annotation tool which algorithmically enriches AMR graphs to better represent number, (in)definite articles, quantificational determiners, and intensional arguments. We compare our automatically produced annotations to gold-standard manual annotations and show that our automatic annotator achieves impressive results. All code for this paper, including our automatic annotation tool, is made publicly available.

Details

Paper ID
lrec2022-ws-law-19
Pages
pp. 160-169
BibKey
ji-etal-2022-automatic
Editor
N/A
Publisher
European Language Resources Association (ELRA)
ISSN
N/A
ISBN
N/A
Workshop
Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022
Location
undefined, undefined
Date
20 June 2022 25 June 2022

Authors

  • YJ

    Yuxin Ji

  • GW

    Gregor Williamson

  • JC

    Jinho D. Choi

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