{"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/a-neural-transition-based-model-for-nested","title":"A Neural Transition-based Model for Nested Mention Recognition","arxiv_id":"1810.01808","date":"2018-10-03","proceeding":"EMNLP 2018 10","authors":["Bailin Wang","Wei Lu","Yu Wang","Hongxia Jin"],"abstract":"It is common that entity mentions can contain other mentions recursively.\nThis paper introduces a scalable transition-based method to model the nested\nstructure of mentions. We first map a sentence with nested mentions to a\ndesignated forest where each mention corresponds to a constituent of the\nforest. Our shift-reduce based system then learns to construct the forest\nstructure in a bottom-up manner through an action sequence whose maximal length\nis guaranteed to be three times of the sentence length. Based on Stack-LSTM\nwhich is employed to efficiently and effectively represent the states of the\nsystem in a continuous space, our system is further incorporated with a\ncharacter-based component to capture letter-level patterns. Our model achieves\nthe state-of-the-art results on ACE datasets, showing its effectiveness in\ndetecting nested mentions.","url_abs":"http://arxiv.org/abs/1810.01808v1","url_pdf":"http://arxiv.org/pdf/1810.01808v1.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":"a-neural-transition-based-model-for-nested","repo_url":"https://github.com/berlino/nest-trans-em18","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"nested-mention-recognition","task_name":"Nested Mention Recognition"},{"task_slug":"nested-named-entity-recognition","task_name":"Nested Named Entity Recognition"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/named-entity-recognition-on-ace-2004","task":"Named Entity Recognition (NER)","dataset":"ACE 2004","model":"Neural transition-based model","rank_in_archive_order":9,"of":9,"metrics":{"F1":"73.3","Multi-Task Supervision":"n"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-ace-2005","task":"Named Entity Recognition (NER)","dataset":"ACE 2005","model":"Neural transition-based model","rank_in_archive_order":19,"of":20,"metrics":{"F1":"73.0"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-genia","task":"Named Entity Recognition (NER)","dataset":"GENIA","model":"Neural transition-based model","rank_in_archive_order":13,"of":14,"metrics":{"F1":"73.9"},"uses_additional_data":false},{"leaderboard":"/sota/nested-mention-recognition-on-ace-2004","task":"Nested Mention Recognition","dataset":"ACE 2004","model":"Neural transition-based model","rank_in_archive_order":7,"of":7,"metrics":{"F1":"73.1"},"uses_additional_data":false},{"leaderboard":"/sota/nested-mention-recognition-on-ace-2005","task":"Nested Mention Recognition","dataset":"ACE 2005","model":"Neural transition-based model","rank_in_archive_order":9,"of":10,"metrics":{"F1":"73.0"},"uses_additional_data":false},{"leaderboard":"/sota/nested-named-entity-recognition-on-ace-2004","task":"Nested Named Entity Recognition","dataset":"ACE 2004","model":"Neural transition-based model","rank_in_archive_order":24,"of":24,"metrics":{"F1":"73.3"},"uses_additional_data":false},{"leaderboard":"/sota/nested-named-entity-recognition-on-ace-2005","task":"Nested Named Entity Recognition","dataset":"ACE 2005","model":"neural transition-based model","rank_in_archive_order":24,"of":25,"metrics":{"F1":"73.0"},"uses_additional_data":false},{"leaderboard":"/sota/nested-named-entity-recognition-on-genia","task":"Nested Named Entity Recognition","dataset":"GENIA","model":"Neural transition-based model","rank_in_archive_order":26,"of":26,"metrics":{"F1":"73.9"},"uses_additional_data":false},{"leaderboard":"/sota/nested-named-entity-recognition-on-nne","task":"Nested Named Entity Recognition","dataset":"NNE","model":"Neural Transition-based Model","rank_in_archive_order":6,"of":6,"metrics":{"Micro F1":"73.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.01808","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}