{"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/chemical-names-standardization-using-neural","title":"Chemical Names Standardization using Neural Sequence to Sequence Model","arxiv_id":"1901.07003","date":"2019-01-21","proceeding":"ICLR 2019 5","authors":["Junlang Zhan","Hai Zhao"],"abstract":"Chemical information extraction is to convert chemical knowledge in text into\ntrue chemical database, which is a text processing task heavily relying on\nchemical compound name identification and standardization. Once a systematic\nname for a chemical compound is given, it will naturally and much simply\nconvert the name into the eventually required molecular formula. However, for\nmany chemical substances, they have been shown in many other names besides\ntheir systematic names which poses a great challenge for this task. In this\npaper, we propose a framework to do the auto standardization from the\nnon-systematic names to the corresponding systematic names by using the\nspelling error correction, byte pair encoding tokenization and neural sequence\nto sequence model. Our framework is trained end to end and is fully\ndata-driven. Our standardization accuracy on the test dataset achieves 54.04%\nwhich has a great improvement compared to previous state-of-the-art result.","url_abs":"http://arxiv.org/abs/1901.07003v1","url_pdf":"http://arxiv.org/pdf/1901.07003v1.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":"chemical-names-standardization-using-neural","repo_url":"https://github.com/zhanjunlang/Neural_Chemical_Name_Standardization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","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}