{"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/learning-to-match-using-local-and-distributed-1","title":"Learning to Match Using Local and Distributed Representations of Text for Web Search","arxiv_id":"1610.08136","date":"2016-10-26","proceeding":"Proceedings of the 26th International Conference on World Wide Web, WWW '17 2017 4","authors":["Bhaskar Mitra","Fernando Diaz","Nick Craswell"],"abstract":"Models such as latent semantic analysis and those based on neural embeddings\nlearn distributed representations of text, and match the query against the\ndocument in the latent semantic space. In traditional information retrieval\nmodels, on the other hand, terms have discrete or local representations, and\nthe relevance of a document is determined by the exact matches of query terms\nin the body text. We hypothesize that matching with distributed representations\ncomplements matching with traditional local representations, and that a\ncombination of the two is favorable. We propose a novel document ranking model\ncomposed of two separate deep neural networks, one that matches the query and\nthe document using a local representation, and another that matches the query\nand the document using learned distributed representations. The two networks\nare jointly trained as part of a single neural network. We show that this\ncombination or `duet' performs significantly better than either neural network\nindividually on a Web page ranking task, and also significantly outperforms\ntraditional baselines and other recently proposed models based on neural\nnetworks.","url_abs":"http://arxiv.org/abs/1610.08136v1","url_pdf":"http://arxiv.org/pdf/1610.08136v1.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":"learning-to-match-using-local-and-distributed-1","repo_url":"https://github.com/bmitra-msft/NDRM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"document-ranking","task_name":"Document Ranking"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1610.08136","atlas_url":"https://app.syntology.ai/?focus=1610.08136","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}