Predicting the Focus of Negation: Model and Error Analysis

Md Mosharaf Hossain, Kathleen Hamilton, Alexis Palmer, Eduardo Blanco


Abstract
The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.
Anthology ID:
2020.acl-main.743
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8389–8401
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.743
DOI:
10.18653/v1/2020.acl-main.743
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.743.pdf
Video:
 http://slideslive.com/38929211