@inproceedings{teodorescu-etal-2022-black,
    title = "{UA}lberta at {LSCD}iscovery: Lexical Semantic Change Detection via Word Sense Disambiguation",
    author = "Teodorescu, Daniela  and
      von der Ohe, Spencer  and
      Kondrak, Grzegorz",
    editor = "Tahmasebi, Nina  and
      Montariol, Syrielle  and
      Kutuzov, Andrey  and
      Hengchen, Simon  and
      Dubossarsky, Haim  and
      Borin, Lars",
    booktitle = "Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2022.lchange-1.19/",
    doi = "10.18653/v1/2022.lchange-1.19",
    pages = "180--186",
    abstract = {We describe our two systems for the shared task on Lexical Semantic Change Discovery in Spanish. For binary change detection, we frame the task as a word sense disambiguation (WSD) problem. We derive sense frequency distributions for target words in both old and modern corpora. We assume that the word semantics have changed if a sense is observed in only one of the two corpora, or the relative change for any sense exceeds a tuned threshold. For graded change discovery, we follow the design of CIRCE (P{\"o}msl and Lyapin, 2020) by combining both static and contextual embeddings. For contextual embeddings, we use XLM-RoBERTa instead of BERT, and train the model to predict a masked token instead of the time period. Our language-independent methods achieve results that are close to the best-performing systems in the shared task.}
}