@inproceedings{hui-etal-2017-pacrr,
    title = "{PACRR}: A Position-Aware Neural {IR} Model for Relevance Matching",
    author = "Hui, Kai  and
      Yates, Andrew  and
      Berberich, Klaus  and
      de Melo, Gerard",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/D17-1110/",
    doi = "10.18653/v1/D17-1110",
    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."
}