@InProceedings{sneiders:2016:COLING,
  author    = {Sneiders, Eriks},
  title     = {Text Retrieval by Term Co-occurrences in a Query-based Vector Space},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2356--2365},
  abstract  = {Term co-occurrence in a sentence or paragraph is a powerful and often
	overlooked feature for text matching in document retrieval. In our experiments
	with matching email-style query messages to webpages, such term co-occurrence
	helped greatly to filter and rank documents, compared to matching document-size
	bags-of-words. The paper presents the results of the experiments as well as a
	text-matching model where the query shapes the vector space, a document is
	modelled by two or three vectors in this vector space, and the query-document
	similarity score depends on the length of the vectors and the relationships
	between them.},
  url       = {http://aclweb.org/anthology/C16-1222}
}