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.- Anthology ID:
- D17-1110
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1049–1058
- Language:
- URL:
- https://aclanthology.org/D17-1110
- DOI:
- 10.18653/v1/D17-1110
- Cite (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.
- Cite (Informal):
- PACRR: A Position-Aware Neural IR Model for Relevance Matching (Hui et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D17-1110.pdf
- Code
- additional community code