@InProceedings{panagiotou-EtAl:2016:COLING,
  author    = {Panagiotou, Nikolaos  and  Akkaya, Cem  and  Tsioutsiouliklis, Kostas  and  Kalogeraki, Vana  and  Gunopulos, Dimitrios},
  title     = {First Story Detection using Entities and Relations},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3237--3244},
  abstract  = {News portals, such as Yahoo News or Google News, collect large amounts of
	documents from a variety of sources on a daily basis. Only a small portion of
	these documents can be selected and displayed on the homepage. Thus, there is a
	strong preference for major, recent events. In this work, we propose a scalable
	and accurate First Story Detection (FSD) pipeline that identifies fresh news.
	In comparison to other FSD systems, our method relies on relation extraction
	methods exploiting entities and their relations. We evaluate our pipeline using
	two distinct datasets from Yahoo News and Google News. Experimental results 
	demonstrate that our method improves over the state-of-the-art systems on both
	datasets with constant space and time requirements.},
  url       = {http://aclweb.org/anthology/C16-1305}
}