{"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/co-pacrr-a-context-aware-neural-ir-model-for","title":"Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval","arxiv_id":"1706.10192","date":"2017-06-30","proceeding":null,"authors":["Kai Hui","Andrew Yates","Klaus Berberich","Gerard de Melo"],"abstract":"Neural IR models, such as DRMM and PACRR, have achieved strong results by\nsuccessfully capturing relevance matching signals. We argue that the context of\nthese matching signals is also important. Intuitively, when extracting,\nmodeling, and combining matching signals, one would like to consider the\nsurrounding text (local context) as well as other signals from the same\ndocument that can contribute to the overall relevance score. In this work, we\nhighlight three potential shortcomings caused by not considering context\ninformation and propose three neural ingredients to address them: a\ndisambiguation component, cascade k-max pooling, and a shuffling combination\nlayer. Incorporating these components into the PACRR model yields Co-PACRR, a\nnovel context-aware neural IR model. Extensive comparisons with established\nmodels on Trec Web Track data confirm that the proposed model can achieve\nsuperior search results. In addition, an ablation analysis is conducted to gain\ninsights into the impact of and interactions between different components. We\nrelease our code to enable future comparisons.","url_abs":"http://arxiv.org/abs/1706.10192v3","url_pdf":"http://arxiv.org/pdf/1706.10192v3.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":"co-pacrr-a-context-aware-neural-ir-model-for","repo_url":"https://github.com/khui/copacrr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"co-pacrr-a-context-aware-neural-ir-model-for","repo_url":"https://github.com/khui/repacrr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"co-pacrr-a-context-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}],"tasks":[{"task_slug":"ad-hoc-information-retrieval","task_name":"Ad-Hoc Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}