@InProceedings{wu-EtAl:2017:SemEval1,
  author    = {Wu, Hao  and  Huang, Heyan  and  Jian, Ping  and  Guo, Yuhang  and  Su, Chao},
  title     = {BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual 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     = {77--84},
  abstract  = {This paper presents three systems for semantic textual similarity (STS)
	evaluation at SemEval-2017 STS task. One is an unsupervised system and the
	other two are supervised systems which simply employ the unsupervised one. All
	our systems mainly depend on the (SIS), which is constructed based on the
	semantic hierarchical taxonomy in WordNet, to compute non-overlapping
	information content (IC) of sentences. Our team ranked 2nd among 31
	participating teams by the primary score of Pearson correlation coefficient
	(PCC) mean of 7 tracks and achieved the best performance on Track 1 (AR-AR)
	dataset.},
  url       = {http://www.aclweb.org/anthology/S17-2007}
}