{"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/an-attention-based-bi-gru-capsnet-model-for","title":"An attention-based Bi-GRU-CapsNet model for hypernymy detection between compound entities","arxiv_id":"1805.04827","date":"2018-05-13","proceeding":null,"authors":["Qi. Wang","Chenming Xu","Yangming Zhou","Tong Ruan","Daqi Gao","Ping He"],"abstract":"Named entities are usually composable and extensible. Typical examples are\nnames of symptoms and diseases in medical areas. To distinguish these entities\nfrom general entities, we name them \\textit{compound entities}. In this paper,\nwe present an attention-based Bi-GRU-CapsNet model to detect hypernymy\nrelationship between compound entities. Our model consists of several important\ncomponents. To avoid the out-of-vocabulary problem, English words or Chinese\ncharacters in compound entities are fed into the bidirectional gated recurrent\nunits. An attention mechanism is designed to focus on the differences between\nthe two compound entities. Since there are some different cases in hypernymy\nrelationship between compound entities, capsule network is finally employed to\ndecide whether the hypernymy relationship exists or not. Experimental results\ndemonstrate","url_abs":"http://arxiv.org/abs/1805.04827v3","url_pdf":"http://arxiv.org/pdf/1805.04827v3.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":"an-attention-based-bi-gru-capsnet-model-for","repo_url":"https://github.com/ECUST-NLP-Lab/medicalHypernymy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"fixcaps","method_name":"Capsule Network"}],"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}