@InProceedings{dellibovi-raganato:2017:SemEval,
  author    = {{Delli Bovi}, Claudio  and  Raganato, Alessandro},
  title     = {Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched Wikipedia},
  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     = {252--257},
  abstract  = {This paper describes Sew-Embed, our language-independent approach to
	multilingual and cross-lingual semantic word similarity as part of the
	SemEval-2017 Task 2. We leverage the Wikipedia-based concept representations
	developed by Raganato et al. (2016), and propose an embedded augmentation of
	their explicit high-dimensional vectors, which we obtain by plugging in an
	arbitrary word (or sense) embedding representation, and computing a weighted
	average in the continuous vector space. We evaluate Sew-Embed with two
	different off-the-shelf embedding representations, and report their
	performances across all monolingual and cross-lingual benchmarks available for
	the task. Despite its simplicity, especially compared with supervised or overly
	tuned approaches, Sew-Embed achieves competitive results in the cross-lingual
	setting (3rd best result in the global ranking of subtask 2, score 0.56).},
  url       = {http://www.aclweb.org/anthology/S17-2041}
}