@inproceedings{asgari-etal-2020-emblexchange,
    title = "{E}mb{L}ex{C}hange at {S}em{E}val-2020 Task 1: Unsupervised Embedding-based Detection of Lexical Semantic Changes",
    author = {Asgari, Ehsaneddin  and
      Ringlstetter, Christoph  and
      Sch{\"u}tze, Hinrich},
    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/landing_page/2020.semeval-1.24/",
    doi = "10.18653/v1/2020.semeval-1.24",
    pages = "201--207",
    abstract = "This paper describes EmbLexChange, a system introduced by the {\textquotedblleft}Life-Language{\textquotedblright} team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes. EmbLexChange is defined as the divergence between the embedding based profiles of word w (calculated with respect to a set of reference words) in the source and the target domains (source and target domains can be simply two time frames t{\_}1 and t{\_}2). The underlying assumption is that the lexical-semantic change of word $w$ would affect its co-occurring words and subsequently alters the neighborhoods in the embedding spaces. We show that using a resampling framework for the selection of reference words (with conserved senses), we can more reliably detect lexical-semantic changes in English, German, Swedish, and Latin. EmbLexChange achieved second place in the binary detection of semantic changes in the SemEval-2020."
}