@inproceedings{nikolaev-etal-2023-adverbs,
    title = "Adverbs, Surprisingly",
    author = "Nikolaev, Dmitry  and
      Baker, Collin  and
      Petruck, Miriam R. L.  and
      Pad{\'o}, Sebastian",
    editor = "Palmer, Alexis  and
      Camacho-collados, Jose",
    booktitle = "Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.starsem-1.44",
    doi = "10.18653/v1/2023.starsem-1.44",
    pages = "512--526",
    abstract = "This paper begins with the premise that adverbs are neglected in computational linguistics. This view derives from two analyses: a literature review and a novel adverb dataset to probe a state-of-the-art language model, thereby uncovering systematic gaps in accounts for adverb meaning. We suggest that using Frame Semantics for characterizing word meaning, as in FrameNet, provides a promising approach to adverb analysis, given its ability to describe ambiguity, semantic roles, and null instantiation.",
}

This paper begins with the premise that adverbs are neglected in computational linguistics. This view derives from two analyses: a literature review and a novel adverb dataset to probe a state-of-the-art language model, thereby uncovering systematic gaps in accounts for adverb meaning. We suggest that using Frame Semantics for characterizing word meaning, as in FrameNet, provides a promising approach to adverb analysis, given its ability to describe ambiguity, semantic roles, and null instantiation.
Nikolaev, Dmitry Baker, Collin Petruck, Miriam R. L. Pad{\'o}, Sebastian
Adverbs, Surprisingly