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.

Negation scope resolution is the process of detecting the negated part of a sentence. Unlike the syntax-based approach employed in previous research, state-of-the-art methods performed better without the explicit use of syntactic structure. This work revisits the syntax-based approach and re-evaluates the effectiveness of syntactic structure in negation scope resolution. We replace the parser utilized in the prior works with state-of-the-art parsers and modify the syntax-based heuristic rules. The experimental results demonstrate that the simple modifications enhance the performance of the prior syntax-based method to the same level as state-of-the-art end-to-end neural-based methods.