@InProceedings{rygl-EtAl:2017:RepL4NLP,
  author    = {Rygl, Jan  and  Pomik\'{a}lek, Jan  and  Řehů\v{r}ek, Radim  and  Rů\v{z}i\v{c}ka, Michal  and  Novotn\'{y}, V\'{i}t  and  Sojka, Petr},
  title     = {Semantic Vector Encoding and Similarity Search Using Fulltext Search Engines},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {81--90},
  abstract  = {Vector representations and vector space modeling (VSM) play a central role in
	modern machine learning. We propose a novel approach to ‘vector similarity
	searching’ over dense semantic representations of words and documents that
	can be deployed on top of traditional inverted-index-based fulltext engines,
	taking advantage of their robustness, stability, scalability and ubiquity.
	We show that this approach allows the indexing and querying of dense vectors in
	text domains. This opens up exciting avenues for major efficiency gains, along
	with simpler deployment, scaling and monitoring.
	The end result is a fast and scalable vector database with a tunable trade-off
	between vector search performance and quality, backed by a standard fulltext
	engine such as Elasticsearch.
	We empirically demonstrate its querying performance and quality by applying
	this solution to the task of semantic searching over a dense vector
	representation of the entire English Wikipedia.},
  url       = {http://www.aclweb.org/anthology/W17-2611}
}