@InProceedings{duma-menzel:2017:SemEval,
  author    = {Duma, Mirela-Stefania  and  Menzel, Wolfgang},
  title     = {SEF$@$UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector},
  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     = {170--174},
  abstract  = {This paper describes our unsupervised knowledge-free approach to the
	SemEval-2017 Task 1 Competition. The proposed method makes use of Paragraph
	Vector for assessing the semantic similarity between pairs of sentences. We
	experimented with various dimensions of the vector and three state-of-the-art
	similarity metrics. Given a cross-lingual task, we trained models corresponding
	to its two languages and combined the models by averaging the similarity
	scores. The results of our submitted runs are above the median scores for five
	out of seven test sets by means of Pearson Correlation. Moreover, one of our
	system runs performed best on the Spanish-English-WMT test set ranking first
	out of 53 runs submitted in total by all participants.},
  url       = {http://www.aclweb.org/anthology/S17-2024}
}