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        <title>PACRR: A Position-Aware Neural IR Model for Relevance Matching</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T PACRR: A Position-Aware Neural IR Model for Relevance Matching
%A Hui, Kai
%A Yates, Andrew
%A Berberich, Klaus
%A de Melo, Gerard
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F hui-etal-2017-pacrr
%X 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.
%R 10.18653/v1/D17-1110
%U https://aclanthology.org/D17-1110
%U https://doi.org/10.18653/v1/D17-1110
%P 1049-1058

Markdown (Informal)

[PACRR: A Position-Aware Neural IR Model for Relevance Matching](https://aclanthology.org/D17-1110) (Hui et al., EMNLP 2017)

• PACRR: A Position-Aware Neural IR Model for Relevance Matching (Hui et al., EMNLP 2017)

ACL

• Kai Hui, Andrew Yates, Klaus Berberich, and Gerard de Melo. 2017. PACRR: A Position-Aware Neural IR Model for Relevance Matching. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1049–1058, Copenhagen, Denmark. Association for Computational Linguistics.