@inproceedings{szymanski-2017-temporal,
    title = "Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings",
    author = "Szymanski, Terrence",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P17-2071/",
    doi = "10.18653/v1/P17-2071",
    pages = "448--453",
    abstract = "This paper introduces the concept of temporal word analogies: pairs of words which occupy the same semantic space at different points in time. One well-known property of word embeddings is that they are able to effectively model traditional word analogies ({``}word $w_1$ is to word $w_2$ as word $w_3$ is to word $w_4$'') through vector addition. Here, I show that temporal word analogies ({``}word $w_1$ at time $t_\alpha$ is like word $w_2$ at time $t_\beta$'') can effectively be modeled with diachronic word embeddings, provided that the independent embedding spaces from each time period are appropriately transformed into a common vector space. When applied to a diachronic corpus of news articles, this method is able to identify temporal word analogies such as ``Ronald Reagan in 1987 is like Bill Clinton in 1997'', or ``Walkman in 1987 is like iPod in 2007''."
}