@InProceedings{liu-EtAl:2017:SemEval1,
  author    = {Liu, Wenjie  and  Sun, Chengjie  and  Lin, Lei  and  Liu, Bingquan},
  title     = {ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing},
  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     = {159--163},
  abstract  = {Semantic Textual Similarity (STS) devotes to measuring the degree of
	equivalence in the underlying semantic of the sentence pair. We proposed a new
	system, ITNLP-AiKF, which applies in the SemEval 2017 Task1 Semantic Textual
	Similarity track 5 English monolingual pairs. In our system, rich features are
	involved, including Ontology based, word embedding based, Corpus based,
	Alignment based and Literal based feature. We leveraged the features to predict
	sentence pair similarity by a Support Vector Regression (SVR) model. In the
	result, a Pearson Correlation of 0.8231 is achieved by our system, which is a
	competitive result in the contest of this track.},
  url       = {http://www.aclweb.org/anthology/S17-2022}
}