{"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/character-level-deep-conflation-for-business","title":"Character-level Deep Conflation for Business Data Analytics","arxiv_id":"1702.02640","date":"2017-02-08","proceeding":null,"authors":["Zhe Gan","P. D. Singh","Ameet Joshi","Xiaodong He","Jianshu Chen","Jianfeng Gao","Li Deng"],"abstract":"Connecting different text attributes associated with the same entity\n(conflation) is important in business data analytics since it could help merge\ntwo different tables in a database to provide a more comprehensive profile of\nan entity. However, the conflation task is challenging because two text strings\nthat describe the same entity could be quite different from each other for\nreasons such as misspelling. It is therefore critical to develop a conflation\nmodel that is able to truly understand the semantic meaning of the strings and\nmatch them at the semantic level. To this end, we develop a character-level\ndeep conflation model that encodes the input text strings from character level\ninto finite dimension feature vectors, which are then used to compute the\ncosine similarity between the text strings. The model is trained in an\nend-to-end manner using back propagation and stochastic gradient descent to\nmaximize the likelihood of the correct association. Specifically, we propose\ntwo variants of the deep conflation model, based on long-short-term memory\n(LSTM) recurrent neural network (RNN) and convolutional neural network (CNN),\nrespectively. Both models perform well on a real-world business analytics\ndataset and significantly outperform the baseline bag-of-character (BoC) model.","url_abs":"http://arxiv.org/abs/1702.02640v1","url_pdf":"http://arxiv.org/pdf/1702.02640v1.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":"character-level-deep-conflation-for-business","repo_url":"https://github.com/AndreHeunisML/Deep-String-Matching","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"character-level-deep-conflation-for-business","repo_url":"https://github.com/zhegan27/Deep_Conflation_Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}