PACRR: A Position-Aware Neural IR Model for Relevance Matching
Information Retrieval
2017-07-25 v3 Computation and Language
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.
Cite
@article{arxiv.1704.03940,
title = {PACRR: A Position-Aware Neural IR Model for Relevance Matching},
author = {Kai Hui and Andrew Yates and Klaus Berberich and Gerard de Melo},
journal= {arXiv preprint arXiv:1704.03940},
year = {2017}
}
Comments
To appear in EMNLP2017