@inproceedings{zhou-li-2020-temporalteller,
    title = "{T}emporal{T}eller at {S}em{E}val-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing",
    author = "Zhou, Jinan  and
      Li, Jiaxin",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.semeval-1.27/",
    doi = "10.18653/v1/2020.semeval-1.27",
    pages = "222--231",
    abstract = "This paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection. We develop a unified framework for the common semantic change detection pipelines including preprocessing, learning word embeddings, calculating vector distances and determining threshold. We also propose Gamma Quantile Threshold to distinguish between changed and stable words. Based on our system, we conduct a comprehensive comparison among BERT, Skip-gram, Temporal Referencing and alignment-based methods. Evaluation results show that Skip-gram with Temporal Referencing achieves the best performance of 66.5{\%} classification accuracy and 51.8{\%} Spearman{'}s Ranking Correlation."
}