Negation-Instance Based Evaluation of End-to-End Negation Resolution

Elizaveta Sineva, Stefan Grünewald, Annemarie Friedrich, Jonas Kuhn


Abstract
In this paper, we revisit the task of negation resolution, which includes the subtasks of cue detection (e.g. “not”, “never”) and scope resolution. In the context of previous shared tasks, a variety of evaluation metrics have been proposed. Subsequent works usually use different subsets of these, including variations and custom implementations, rendering meaningful comparisons between systems difficult. Examining the problem both from a linguistic perspective and from a downstream viewpoint, we here argue for a negation-instance based approach to evaluating negation resolution. Our proposed metrics correspond to expectations over per-instance scores and hence are intuitively interpretable. To render research comparable and to foster future work, we provide results for a set of current state-of-the-art systems for negation resolution on three English corpora, and make our implementation of the evaluation scripts publicly available.
Anthology ID:
2021.conll-1.41
Volume:
Proceedings of the 25th Conference on Computational Natural Language Learning
Month:
November
Year:
2021
Address:
Online
Venues:
CoNLL | EMNLP
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
528–543
Language:
URL:
https://aclanthology.org/2021.conll-1.41
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.conll-1.41.pdf
Code
 boschresearch/negation_resolution_evaluation_conll2021