{"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/improving-named-entity-recognition-with","title":"Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information","arxiv_id":"2010.15466","date":"2020-10-29","proceeding":"Findings of the Association for Computational Linguistics 2020","authors":["Yuyang Nie","Yuanhe Tian","Yan Song","Xiang Ao","Xiang Wan"],"abstract":"Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. To model such properties, one could rely on existing resources to providing helpful knowledge to the NER task; some existing studies proved the effectiveness of doing so, and yet are limited in appropriately leveraging the knowledge such as distinguishing the important ones for particular context. In this paper, we improve NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactic information, respectively. Experimental results on six English and Chinese benchmark datasets suggest the effectiveness of the proposed model and show that it outperforms previous studies on all experiment datasets.","url_abs":"https://arxiv.org/abs/2010.15466v1","url_pdf":"https://arxiv.org/pdf/2010.15466v1.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":"improving-named-entity-recognition-with","repo_url":"https://github.com/cuhksz-nlp/AESINER","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"chinese-named-entity-recognition","task_name":"Chinese Named Entity Recognition"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/chinese-named-entity-recognition-on-ontonotes","task":"Chinese Named Entity Recognition","dataset":"OntoNotes 4","model":"AESINER","rank_in_archive_order":7,"of":15,"metrics":{"F1":"81.18"},"uses_additional_data":false},{"leaderboard":"/sota/chinese-named-entity-recognition-on-resume","task":"Chinese Named Entity Recognition","dataset":"Resume NER","model":"AESINER","rank_in_archive_order":4,"of":13,"metrics":{"F1":"96.62"},"uses_additional_data":false},{"leaderboard":"/sota/chinese-named-entity-recognition-on-weibo-ner","task":"Chinese Named Entity Recognition","dataset":"Weibo NER","model":"AESINER","rank_in_archive_order":6,"of":18,"metrics":{"F1":"69.78"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-ontonotes-v5","task":"Named Entity Recognition (NER)","dataset":"Ontonotes v5 (English)","model":"AESINER","rank_in_archive_order":12,"of":28,"metrics":{"F1":"90.32"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-wnut-2016","task":"Named Entity Recognition (NER)","dataset":"WNUT 2016","model":"AESINER","rank_in_archive_order":3,"of":7,"metrics":{"F1":"55.14"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-wnut-2017","task":"Named Entity Recognition (NER)","dataset":"WNUT 2017","model":"AESINER","rank_in_archive_order":13,"of":23,"metrics":{"F1":"50.68"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2010.15466","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}