@InProceedings{rosin-adar-radinsky:2017:EMNLP2017,
  author    = {Rosin, Guy D.  and  Adar, Eytan  and  Radinsky, Kira},
  title     = {Learning Word Relatedness over Time},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1168--1178},
  abstract  = {Search systems are often focused on providing relevant results for the "now",
	assuming both corpora and user needs that focus on the present. However, many
	corpora today reflect significant longitudinal collections ranging from 20
	years of the Web to hundreds of years of digitized newspapers and books.
	Understanding the temporal intent of the user and retrieving the most relevant
	historical content has become a significant challenge. Common search features,
	such as query expansion, leverage the relationship between terms but cannot
	function well across all times when relationships vary temporally. In this
	work, we introduce a temporal relationship model that is extracted from
	longitudinal data collections. The model supports the task of identifying,
	given two words, when they relate to each other. We present an algorithmic
	framework for this task and show its application for the task of query
	expansion, achieving high gain.
	Author{3}{Affiliation}},
  url       = {https://www.aclweb.org/anthology/D17-1121}
}