Learning Negation Scope from Syntactic Structure

Nick McKenna, Mark Steedman


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.
Anthology ID:
2020.starsem-1.15
Volume:
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venues:
*SEM | COLING | SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
137–142
Language:
URL:
https://aclanthology.org/2020.starsem-1.15
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Learning Negation Scope from Syntactic Structure (McKenna & Steedman, *SEM 2020)
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PDF:
https://aclanthology.org/2020.starsem-1.15.pdf