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    <titleInfo>
        <title>Automatic Enrichment of Abstract Meaning Representations</title>
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        <namePart type="given">Yuxin</namePart>
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            <title>Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022</title>
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            <namePart type="given">Sameer</namePart>
            <namePart type="family">Pradhan</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Automatic Enrichment of Abstract Meaning Representations
%A Ji, Yuxin
%A Williamson, Gregor
%A Choi, Jinho D.
%Y Pradhan, Sameer
%Y Kuebler, Sandra
%S Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F ji-etal-2022-automatic
%X 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.
%U https://aclanthology.org/2022.law-1.19
%P 160-169

Markdown (Informal)

[Automatic Enrichment of Abstract Meaning Representations](https://aclanthology.org/2022.law-1.19) (Ji et al., LAW 2022)

• Automatic Enrichment of Abstract Meaning Representations (Ji et al., LAW 2022)

ACL

• Yuxin Ji, Gregor Williamson, and Jinho D. Choi. 2022. Automatic Enrichment of Abstract Meaning Representations. In Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022, pages 160–169, Marseille, France. European Language Resources Association.