@online{DBLP:journals/corr/HuiYBM17, TITLE = {{PACRR}: A Position-Aware Neural {IR} Model for Relevance Matching}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1704.03940}, EPRINT = {1704.03940}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, 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 (Position-Aware Convolutional-Recurrent Relevance), aiming at better modeling position-dependent interactions between a query and a document via convolutional layers as well as recurrent layers. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under different benchmarks.}, }