Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset

Asahi Yoshida, Yoshihide Kato, Shigeki Matsubara

Abstract
Negation scope resolution is the task that identifies the part of a sentence affected by the negation cue. The three major corpora used for this task, the BioScope corpus, the SFU review corpus and the Sherlock dataset, have different annotation schemes for negation scope. Due to the different annotations, the negation scope resolution models based on pre-trained language models (PLMs) perform worse when fine-tuned on the simply combined dataset consisting of the three corpora. To address this issue, we propose a method for automatically converting the scopes of BioScope and SFU to those of Sherlock and merge them into a unified dataset. To verify the effectiveness of the proposed method, we conducted experiments using the unified dataset for fine-tuning PLM-based models. The experimental results demonstrate that the performances of the models increase when fine-tuned on the unified dataset unlike the simply combined one. In the token-level metric, the model fine-tuned on the unified dataset archived the state-of-the-art performance on the Sherlock dataset.
Anthology ID:
2024.lrec-main.1057
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
12093–12099
Language:
URL:
https://aclanthology.org/2024.lrec-main.1057
DOI:
Bibkey:
Cite (ACL):
Asahi Yoshida, Yoshihide Kato, and Shigeki Matsubara. 2024. Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12093–12099, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset (Yoshida et al., LREC-COLING 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1057.pdf
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