@inproceedings{king-cook-2021-now,
    title = "Now, It{'}s Personal : The Need for Personalized Word Sense Disambiguation",
    author = "King, Milton  and
      Cook, Paul",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
    month = sep,
    year = "2021",
    address = "Held Online",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.ranlp-1.79/",
    pages = "692--700",
    abstract = "Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained on multiple authors. Our work finds that WordNet{'}s first senses, the predominant senses of our dataset{'}s genre, and the predominant senses of an author can all be different and therefore, author-agnostic models could perform well over the entire dataset, but poorly on individual authors. In this work, we explore methods for personalizing WSD models by tailoring existing state-of-the-art models toward an individual by exploiting the author{'}s sense distributions. We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author{'}s sense distributions or predominant senses can greatly increase its performance."
}