@InProceedings{szymanski:2017:Short,
  author    = {Szymanski, Terrence},
  title     = {Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  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 w1 is to word w2 as word w3 is to
	word w4”) through vector addition. Here, I show that temporal word analogies
	(“word w1 at time tα is like word w2 at time tβ”) 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”.},
  url       = {http://aclweb.org/anthology/P17-2071}
}