@inproceedings{wu-sun-2023-negation,
    title = "Negation Scope Refinement via Boundary Shift Loss",
    author = "Wu, Yin  and
      Sun, Aixin",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.379",
    doi = "10.18653/v1/2023.findings-acl.379",
    pages = "6090--6099",
    abstract = "Negation in natural 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. On multiple benchmark datasets, we show that the extremely simple R-BSL achieves best results.",
}

Negation in natural 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. On multiple benchmark datasets, we show that the extremely simple R-BSL achieves best results.
Wu, Yin Sun, Aixin
Negation Scope Refinement via Boundary Shift Loss