@InProceedings{torki-hasanain-elsayed:2017:SemEval,
  author    = {Torki, Marwan  and  Hasanain, Maram  and  Elsayed, Tamer},
  title     = {QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums},
  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     = {360--364},
  abstract  = {In this paper we describe our QU-BIGIR system for the Arabic subtask D of the
	SemEval 2017 Task 3. Our approach builds on our participation in the past
	version of the same subtask. This year, our system uses different similarity
	measures that encodes lexical and semantic pairwise similarity of text pairs.
	In addition to well known similarity measures such as cosine similarity, we use
	other measures based on the summary statistics of word embedding
	representation for a given text. To rank a list of candidate question answer
	pairs for a given question, we learn a linear SVM classifier over our
	similarity features. Our best resulting run came second in subtask D with a
	very competitive performance to the first-ranking system.},
  url       = {http://www.aclweb.org/anthology/S17-2059}
}