@InProceedings{nagoudi-schwab:2017:W17-13,
  author    = {Nagoudi, El Moatez Billah  and  Schwab, Didier},
  title     = {Semantic Similarity of Arabic Sentences with Word Embeddings},
  booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {18--24},
  abstract  = {Semantic textual similarity is the basis of
	countless applications and plays an important
	role in diverse areas, such as information
	retrieval, plagiarism detection, information
	extraction and machine translation.
	This article proposes an innovative
	word embedding-based system devoted to
	calculate the semantic similarity in Arabic
	sentences. The main idea is to exploit vectors
	as word representations in a multidimensional
	space in order 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. The
	performance of our proposed system is
	confirmed through the Pearson correlation
	between our assigned semantic similarity
	scores and human judgments.},
  url       = {http://www.aclweb.org/anthology/W17-1303}
}