Refining Implicit Argument Annotation for UCCA
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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
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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