@InProceedings{kohail-salama-biemann:2017:SemEval,
  author    = {Kohail, Sarah  and  Salama, Amr Rekaby  and  Biemann, Chris},
  title     = {STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble},
  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     = {175--179},
  abstract  = {This paper reports the STS-UHH participation in the SemEval 2017 shared
	Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs
	covering
	monolingual and cross-lingual STS tracks. Our participation involves two
	approaches:
	unsupervised approach, which estimates a word alignment-based similarity
	score, and supervised approach, which combines dependency graph similarity
	and coverage features with lexical similarity measures using regression
	methods. We also present a way on ensembling both models. Out of 84 submitted
	runs, our team best multi-lingual run has been ranked 12th in overall
	performance
	with correlation of 0.61, 7th among 31 participating teams.},
  url       = {http://www.aclweb.org/anthology/S17-2025}
}