@inproceedings{mckenna-steedman-2020-learning,
title = "Learning Negation Scope from Syntactic Structure",
author = "McKenna, Nick and
Steedman, Mark",
editor = "Gurevych, Iryna and
Apidianaki, Marianna and
Faruqui, Manaal",
booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.starsem-1.15",
pages = "137--142",
abstract = "We present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure. Linguistic theory posits that the semantics of negation can be understood purely syntactically, though recent research relies on combining a variety of features including part-of-speech tags, word embeddings, and semantic representations to achieve high task performance. Our simplified model returns to syntactic theory and achieves state-of-the-art performance on the task of Negation Scope Detection while demonstrating the tight relationship between the syntax and semantics of negation.",
}
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%0 Conference Proceedings
%T Learning Negation Scope from Syntactic Structure
%A McKenna, Nick
%A Steedman, Mark
%Y Gurevych, Iryna
%Y Apidianaki, Marianna
%Y Faruqui, Manaal
%S Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F mckenna-steedman-2020-learning
%X We present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure. Linguistic theory posits that the semantics of negation can be understood purely syntactically, though recent research relies on combining a variety of features including part-of-speech tags, word embeddings, and semantic representations to achieve high task performance. Our simplified model returns to syntactic theory and achieves state-of-the-art performance on the task of Negation Scope Detection while demonstrating the tight relationship between the syntax and semantics of negation.
%U https://aclanthology.org/2020.starsem-1.15
%P 137-142
Markdown (Informal)
[Learning Negation Scope from Syntactic Structure](https://aclanthology.org/2020.starsem-1.15) (McKenna & Steedman, *SEM 2020)
ACL
- Nick McKenna and Mark Steedman. 2020. Learning Negation Scope from Syntactic Structure. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, pages 137–142, Barcelona, Spain (Online). Association for Computational Linguistics.