{"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/convolutional-gated-recurrent-units-for","title":"Convolutional Gated Recurrent Units for Medical Relation Classification","arxiv_id":"1807.11082","date":"2018-07-29","proceeding":null,"authors":["Bin He","Yi Guan","Rui Dai"],"abstract":"Convolutional neural network (CNN) and recurrent neural network (RNN) models\nhave become the mainstream methods for relation classification. We propose a\nunified architecture, which exploits the advantages of CNN and RNN\nsimultaneously, to identify medical relations in clinical records, with only\nword embedding features. Our model learns phrase-level features through a CNN\nlayer, and these feature representations are directly fed into a bidirectional\ngated recurrent unit (GRU) layer to capture long-term feature dependencies. We\nevaluate our model on two clinical datasets, and experiments demonstrate that\nour model performs significantly better than previous single-model methods on\nboth datasets.","url_abs":"http://arxiv.org/abs/1807.11082v1","url_pdf":"http://arxiv.org/pdf/1807.11082v1.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":"convolutional-gated-recurrent-units-for","repo_url":"https://github.com/MindSpore-scientific-2/code-4/tree/main/pytorch_convgru","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-classification","task_name":"Relation Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}