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arXiv · 1704.03940

PACRR: A Position-Aware Neural IR Model for Relevance Matching

Kai Hui·Andrew Yates·Klaus Berberich·Gerard de Melo
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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.

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Kai Hui, Andrew Yates, Klaus Berberich, Gerard de Melo. 2017-04-12. PACRR: A Position-Aware Neural IR Model for Relevance Matching. https://arxiv.org/abs/1704.03940

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