@InProceedings{ranjbar-EtAl:2017:SemEval,
  author    = {Ranjbar, Niloofar  and  Mashhadirajab, Fatemeh  and  Shamsfard, Mehrnoush  and  Hosseini pour, Rayeheh  and  Vahid pour, Aryan},
  title     = {Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity},
  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     = {256--260},
  abstract  = {In this paper, we describe our proposed method for measuring semantic
	similarity for a given pair of words at SemEval-2017 monolingual semantic word
	similarity task. We use a combination of knowledge-based and corpus-based
	techniques.
	We use FarsNet, the Persian Word Net, besides deep learning techniques to
	extract the similarity of words. We evaluated our proposed approach on Persian
	(Farsi) test data at SemEval-2017. It outperformed the other participants and
	ranked the first in the challenge.},
  url       = {http://www.aclweb.org/anthology/S17-2040}
}