{"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/named-entity-disambiguation-for-noisy-text","title":"Named Entity Disambiguation for Noisy Text","arxiv_id":"1706.09147","date":"2017-06-28","proceeding":"CONLL 2017 8","authors":["Yotam Eshel","Noam Cohen","Kira Radinsky","Shaul Markovitch","Ikuya Yamada","Omer Levy"],"abstract":"We address the task of Named Entity Disambiguation (NED) for noisy text. We\npresent WikilinksNED, a large-scale NED dataset of text fragments from the web,\nwhich is significantly noisier and more challenging than existing news-based\ndatasets. To capture the limited and noisy local context surrounding each\nmention, we design a neural model and train it with a novel method for sampling\ninformative negative examples. We also describe a new way of initializing word\nand entity embeddings that significantly improves performance. Our model\nsignificantly outperforms existing state-of-the-art methods on WikilinksNED\nwhile achieving comparable performance on a smaller newswire dataset.","url_abs":"http://arxiv.org/abs/1706.09147v2","url_pdf":"http://arxiv.org/pdf/1706.09147v2.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":"named-entity-disambiguation-for-noisy-text","repo_url":"https://github.com/yotam-happy/NEDforNoisyText","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"entity-disambiguation","task_name":"Entity Disambiguation"},{"task_slug":"entity-embeddings","task_name":"Entity Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1706.09147","atlas_url":"https://app.syntology.ai/?focus=1706.09147","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}