{"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/neural-collective-entity-linking","title":"Neural Collective Entity Linking","arxiv_id":"1811.08603","date":"2018-11-21","proceeding":"COLING 2018 8","authors":["Yixin Cao","Lei Hou","Juanzi Li","Zhiyuan Liu"],"abstract":"Entity Linking aims to link entity mentions in texts to knowledge bases, and\nneural models have achieved recent success in this task. However, most existing\nmethods rely on local contexts to resolve entities independently, which may\nusually fail due to the data sparsity of local information. To address this\nissue, we propose a novel neural model for collective entity linking, named as\nNCEL. NCEL applies Graph Convolutional Network to integrate both local\ncontextual features and global coherence information for entity linking. To\nimprove the computation efficiency, we approximately perform graph convolution\non a subgraph of adjacent entity mentions instead of those in the entire text.\nWe further introduce an attention scheme to improve the robustness of NCEL to\ndata noise and train the model on Wikipedia hyperlinks to avoid overfitting and\ndomain bias. In experiments, we evaluate NCEL on five publicly available\ndatasets to verify the linking performance as well as generalization ability.\nWe also conduct an extensive analysis of time complexity, the impact of key\nmodules, and qualitative results, which demonstrate the effectiveness and\nefficiency of our proposed method.","url_abs":"http://arxiv.org/abs/1811.08603v1","url_pdf":"http://arxiv.org/pdf/1811.08603v1.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":"neural-collective-entity-linking","repo_url":"https://github.com/TaoMiner/NCEL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.08603","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}