@InProceedings{andy-EtAl:2017:WNUT,
  author    = {Andy, Anietie  and  Dredze, Mark  and  Rwebangira, Mugizi  and  Callison-Burch, Chris},
  title     = {Constructing an Alias List for Named Entities during an Event},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {40--44},
  abstract  = {In certain fields, real-time knowledge from events can help in making informed
	decisions. In order to extract pertinent real-time knowledge related to an
	event, it is important to identify the named entities and their corresponding
	aliases related to the event. The problem of identifying aliases of named
	entities that spike has remained unexplored. In this paper, we introduce an
	algorithm, EntitySpike, that identifies entities that spike in popularity in
	tweets from a given time period, and constructs an alias list for these spiked
	entities. EntitySpike uses a temporal heuristic to identify named entities with
	similar context that occur in the same time period (within minutes) during an
	event. Each entity is encoded as a vector using this temporal heuristic. We
	show how these entity-vectors can be used to create a named entity alias list. 
	We evaluated our algorithm on a dataset of temporally ordered tweets from a
	single event, the 2013 Grammy Awards show. We carried out various experiments
	on tweets that were published in the same time period and show that our
	algorithm identifies most entity name aliases and outperforms a competitive
	baseline.},
  url       = {http://www.aclweb.org/anthology/W17-4405}
}