@inproceedings{cui-hershcovich-2020-refining,
title = "Refining Implicit Argument Annotation for {UCCA}",
author = "Cui, Ruixiang and
Hershcovich, Daniel",
editor = "Xue, Nianwen and
Bos, Johan and
Croft, William and
Haji{\v{c}}, Jan and
Huang, Chu-Ren and
Oepen, Stephan and
Palmer, Martha and
Pustejovsky, James",
booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
month = dec,
year = "2020",
address = "Barcelona Spain (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.dmr-1.5",
pages = "41--52",
abstract = "Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation{'}s foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.",
}
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<abstract>Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation’s foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.</abstract>
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%0 Conference Proceedings
%T Refining Implicit Argument Annotation for UCCA
%A Cui, Ruixiang
%A Hershcovich, Daniel
%Y Xue, Nianwen
%Y Bos, Johan
%Y Croft, William
%Y Hajič, Jan
%Y Huang, Chu-Ren
%Y Oepen, Stephan
%Y Palmer, Martha
%Y Pustejovsky, James
%S Proceedings of the Second International Workshop on Designing Meaning Representations
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona Spain (online)
%F cui-hershcovich-2020-refining
%X Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation’s foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.
%U https://aclanthology.org/2020.dmr-1.5
%P 41-52
Markdown (Informal)
[Refining Implicit Argument Annotation for UCCA](https://aclanthology.org/2020.dmr-1.5) (Cui & Hershcovich, DMR 2020)
ACL
- Ruixiang Cui and Daniel Hershcovich. 2020. Refining Implicit Argument Annotation for UCCA. In Proceedings of the Second International Workshop on Designing Meaning Representations, pages 41–52, Barcelona Spain (online). Association for Computational Linguistics.