Negation Scope Refinement via Boundary Shift Loss
Yin Wu, Aixin Sun · 2023
Negation in language may affect many NLP applications, e.g., information extraction and sentiment analysis.The key sub-task of negation detection is negation scope resolution which aims to extract the portion of a sentence that is being negated by a negation cue (e.g., keyword "not" and "never") in the sentence.Due to the long spans, existing methods tend to make wrong predictions around the scope boundaries.In this paper, we propose a simple yet effective model named R-BSL which engages the Boundary Shift Loss to refine the predicted boundary.1 On multiple benchmark datasets, we show that the extremely simple R-BSL achieves best results.