@InProceedings{hui-EtAl:2017:EMNLP2017,
  author    = {Hui, Kai  and  Yates, Andrew  and  Berberich, Klaus  and  de Melo, Gerard},
  title     = {PACRR: A Position-Aware Neural IR Model for Relevance Matching},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1049--1058},
  abstract  = {In order to adopt deep learning for information retrieval, models are needed
	that can capture all relevant information required to assess the relevance of a
	document to a given
	user query. While previous works have successfully captured unigram term
	matches, how to fully employ position-dependent information such as proximity
	and term dependencies has been insufficiently explored. In this work, we
	propose a novel neural IR model named PACRR
	aiming at better modeling position-dependent interactions between a query and a
	document.
	Extensive experiments on six years' TREC Web Track data confirm that the
	proposed model yields better results under multiple benchmarks.},
  url       = {https://www.aclweb.org/anthology/D17-1110}
}