Refining Implicit Argument Annotation for UCCA

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Refining Implicit Argument Annotation for UCCA. / Cui, Ruixiang; Hershcovich, Daniel.

Proceedings of the Second International Workshop on Designing Meaning Representations. Association for Computational Linguistics, 2020. p. 41-52.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Cui, R & Hershcovich, D 2020, Refining Implicit Argument Annotation for UCCA. in Proceedings of the Second International Workshop on Designing Meaning Representations. Association for Computational Linguistics, pp. 41-52, 2nd International Workshop on Designing Meaning Representations, Barcelona (Online), Spain, 13/12/2020.

APA

Cui, R., & Hershcovich, D. (2020). Refining Implicit Argument Annotation for UCCA. In Proceedings of the Second International Workshop on Designing Meaning Representations (pp. 41-52). Association for Computational Linguistics.

Vancouver

Cui R, Hershcovich D. Refining Implicit Argument Annotation for UCCA. In Proceedings of the Second International Workshop on Designing Meaning Representations. Association for Computational Linguistics. 2020. p. 41-52

Author

Cui, Ruixiang ; Hershcovich, Daniel. / Refining Implicit Argument Annotation for UCCA. Proceedings of the Second International Workshop on Designing Meaning Representations. Association for Computational Linguistics, 2020. pp. 41-52

Bibtex

@inproceedings{dba25cf427ca4bf1b7a7b15b34aa2221,
title = "Refining Implicit Argument Annotation for UCCA",
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{\textquoteright}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.",
author = "Ruixiang Cui and Daniel Hershcovich",
year = "2020",
language = "English",
pages = "41--52",
booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
publisher = "Association for Computational Linguistics",
note = "2nd International Workshop on Designing Meaning Representations ; Conference date: 13-12-2020 Through 13-12-2020",

}

RIS

TY - GEN

T1 - Refining Implicit Argument Annotation for UCCA

AU - Cui, Ruixiang

AU - Hershcovich, Daniel

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

M3 - Article in proceedings

SP - 41

EP - 52

BT - Proceedings of the Second International Workshop on Designing Meaning Representations

PB - Association for Computational Linguistics

T2 - 2nd International Workshop on Designing Meaning Representations

Y2 - 13 December 2020 through 13 December 2020

ER -

ID: 254672062