{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pacrr-a-position-aware-neural-ir-model-for","title":"PACRR: A Position-Aware Neural IR Model for Relevance Matching","arxiv_id":"1704.03940","date":"2017-04-12","proceeding":"EMNLP 2017 9","authors":["Kai Hui","Andrew Yates","Klaus Berberich","Gerard de Melo"],"abstract":"In order to adopt deep learning for information retrieval, models are needed\nthat can capture all relevant information required to assess the relevance of a\ndocument to a given user query. While previous works have successfully captured\nunigram term matches, how to fully employ position-dependent information such\nas proximity and term dependencies has been insufficiently explored. In this\nwork, we propose a novel neural IR model named PACRR aiming at better modeling\nposition-dependent interactions between a query and a document. Extensive\nexperiments on six years' TREC Web Track data confirm that the proposed model\nyields better results under multiple benchmarks.","url_abs":"http://arxiv.org/abs/1704.03940v3","url_pdf":"http://arxiv.org/pdf/1704.03940v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pacrr-a-position-aware-neural-ir-model-for","repo_url":"https://github.com/MatanRad/Neural-IR-Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"pacrr-a-position-aware-neural-ir-model-for","repo_url":"https://github.com/khui/copacrr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"pacrr-a-position-aware-neural-ir-model-for","repo_url":"https://github.com/khui/repacrr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"ad-hoc-information-retrieval","task_name":"Ad-Hoc Information Retrieval"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":null,"task_name":"Position"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.03940","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}