@InProceedings{nagoudi-ferrero-schwab:2017:SemEval,
  author    = {NAGOUDI, El Moatez Billah  and  Ferrero, J\'{e}r\'{e}my  and  Schwab, Didier},
  title     = {LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {134--138},
  abstract  = {This article describes our proposed system  named LIM-LIG. This system is
	designed for SemEval 2017 Task1: Semantic  Textual  Similarity (Track1).
	LIM-LIG  proposes  an  innovative  enhancement to word embedding-based model 
	devoted  to    measure                                            the  semantic 
	similarity
	     in 
	Arabic 
	sentences.
	The  main  idea  is  to  exploit    the  word representations  as  vectors  in 
	a  multidimensional  space    to    capture  the  semantic  and  syntactic 
	properties  of                                              words.                       
	IDF 
	weighting   
	and 
	Part-of-Speech                                tagging            
	    are 
	applied  on  the  examined  sentences  to  support    the  identification  of 
	words  that  are  highly  descriptive  in  each  sentence.  LIM-LIG system
	achieves a Pearson\'s correlation of 0.74633, ranking 2nd among all
	participants in the   Arabic monolingual pairs                                       
	   
	STS
	task
	organized
	within
	the
	SemEval 2017 evaluation campaign},
  url       = {http://www.aclweb.org/anthology/S17-2017}
}