@InProceedings{daoud-daoud:2016:CogALex-V,
  author    = {Daoud, Mohammad  and  Daoud, Daoud},
  title     = {Discovering Potential Terminological Relationships from Twitter’s Timed Content},
  booktitle = {Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {134--144},
  abstract  = {This paper presents a method to discover possible terminological relationships
	from tweets. We match the histories of terms (frequency patterns). Similar
	history indicates a possible relationship between terms. For example, if two
	terms (t1, t2) appeared frequently in Twitter at particular days, and there is
	a ‘similarity’ in the frequencies over a period of time, then t1 and t2 can
	be related. Maintaining standard terminological repository with updated
	relationships can be difficult; especially in a dynamic domain such as social
	media where thousands of new terms (neology) are coined every day.  So we
	propose to construct a raw repository of lexical units with unconfirmed
	relationships. We have experimented our method on time-sensitive Arabic terms
	used by the online Arabic community of Twitter. We draw relationships between
	these terms by matching their similar frequency patterns (timelines). We use
	dynamic time warping as a similarity measure. For evaluation, we have selected
	630 possible terms (we call them preterms) and we matched the similarity of
	these terms over a period of 30 days. Around 270 correct relationships were
	discovered with a precision of 0.61. These relationships were extracted without
	considering the textual context of the term.},
  url       = {http://aclweb.org/anthology/W16-5319}
}