@InProceedings{maharjan-EtAl:2017:SemEval,
  author    = {Maharjan, Nabin  and  Banjade, Rajendra  and  Gautam, Dipesh  and  Tamang, Lasang J.  and  Rus, Vasile},
  title     = {DT\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output},
  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     = {120--124},
  abstract  = {We describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic
	Textual Similarity (STS) challenge for English (Track 5). We developed three
	different models with various features including similarity scores calculated
	using word and chunk alignments, word/sentence embeddings, and Gaussian Mixture
	Model(GMM). The correlation between our system’s output and the human
	judgments were up to 0.8536, which is more than 10% above baseline, and almost
	as good as the best performing system which was at 0.8547 correlation (the
	difference is just about 0.1%). Also, our system produced leading results when
	evaluated with a separate STS benchmark dataset. The word alignment and
	sentence embeddings based features were found to be very effective.},
  url       = {http://www.aclweb.org/anthology/S17-2014}
}