@InProceedings{lee-EtAl:2017:SemEval,
  author    = {Lee, I-Ta  and  Goindani, Mahak  and  Li, Chang  and  Jin, Di  and  Johnson, Kristen Marie  and  Zhang, Xiao  and  Pacheco, Maria Leonor  and  Goldwasser, Dan},
  title     = {PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings},
  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     = {198--202},
  abstract  = {This paper describes our proposed solution for SemEval 2017 Task 1: Semantic
	Textual Similarity \cite{semeval2017}. The task aims at measuring the degree of
	equivalence between sentences given in English. Performance is evaluated by
	computing Pearson Correlation scores between the predicted scores and human
	judgements. Our proposed system consists of two subsystems and one regression
	model for predicting STS scores. The two subsystems are designed to learn
	Paraphrase and Event Embeddings that can take the consideration of paraphrasing
	characteristics and sentence structures into our system. The regression model
	associates these embeddings to make the final predictions. The experimental
	result shows that our system acquires 0.8 of Pearson Correlation Scores in this
	task.},
  url       = {http://www.aclweb.org/anthology/S17-2029}
}