PACRR: A Position-Aware Neural IR Model for Relevance Matching
Kai Hui | Andrew Yates | Klaus Berberich | Gerard de Melo
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
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.
Natural Language Questions for the Web of Data
Mohamed Yahya | Klaus Berberich | Shady Elbassuoni | Maya Ramanath | Volker Tresp | Gerhard Weikum
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
- Kai Hui 1
- Andrew Yates 1
- Gerard De Melo 1
- Mohamed Yahya 1
- Shady Elbassuoni 1
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