{"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/methods-for-recognizing-nested-terms","title":"Methods for Recognizing Nested Terms","arxiv_id":"2504.16007","date":"2025-04-22","proceeding":null,"authors":["Igor Rozhkov","Natalia Loukachevitch"],"abstract":"In this paper, we describe our participation in the RuTermEval competition devoted to extracting nested terms. We apply the Binder model, which was previously successfully applied to the recognition of nested named entities, to extract nested terms. We obtained the best results of term recognition in all three tracks of the RuTermEval competition. In addition, we study the new task of recognition of nested terms from flat training data annotated with terms without nestedness. We can conclude that several approaches we proposed in this work are viable enough to retrieve nested terms effectively without nested labeling of them.","url_abs":"https://arxiv.org/abs/2504.16007v2","url_pdf":"https://arxiv.org/pdf/2504.16007v2.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":"methods-for-recognizing-nested-terms","repo_url":"https://github.com/fulstock/Methods-for-Recognizing-Nested-Terms","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dialogue-evaluation","task_name":"Dialogue Evaluation"},{"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":"nested-named-entity-recognition","task_name":"Nested Named Entity Recognition"},{"task_slug":"nested-term-extraction","task_name":"Nested Term Extraction"},{"task_slug":"nested-term-recognition","task_name":"Nested Term Recognition"},{"task_slug":"nested-term-recognition-from-flat-supervision","task_name":"Nested Term Recognition from Flat Supervision"},{"task_slug":"term-extraction","task_name":"Term Extraction"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/nested-term-extraction-on-rutermeval-track-1","task":"Nested Term Extraction","dataset":"RuTermEval (Track 1)","model":"full nested","rank_in_archive_order":1,"of":1,"metrics":{"Scoreboard F1":"0.7940"},"uses_additional_data":false},{"leaderboard":"/sota/nested-term-extraction-on-rutermeval-track-2","task":"Nested Term Extraction","dataset":"RuTermEval (Track 2)","model":"full nested","rank_in_archive_order":1,"of":1,"metrics":{"Scoreboard Class-agnostic F1":"0.78","Scoreboard Weighted F1":"0.6997"},"uses_additional_data":false},{"leaderboard":"/sota/nested-term-extraction-on-rutermeval-track-3","task":"Nested Term Extraction","dataset":"RuTermEval (Track 3)","model":"full nested","rank_in_archive_order":1,"of":1,"metrics":{"Scoreboard Class-agnostic F1":"0.60","Scoreboard Weighted F1":"0.4823"},"uses_additional_data":false},{"leaderboard":"/sota/nested-term-recognition-from-flat-supervision","task":"Nested Term Recognition from Flat Supervision","dataset":"RuTermEval (Track 1)","model":"lemm. inc. + early dmg","rank_in_archive_order":1,"of":1,"metrics":{"Scoreboard F1":"0.7281"},"uses_additional_data":false},{"leaderboard":"/sota/nested-term-recognition-from-flat-supervision-1","task":"Nested Term Recognition from Flat Supervision","dataset":"RuTermEval (Track 2)","model":"lemm. inc. + early dmg","rank_in_archive_order":1,"of":1,"metrics":{"Scoreboard Class-agnostic F1":"0.7337","Scoreboard Weighted F1":"0.631"},"uses_additional_data":false},{"leaderboard":"/sota/nested-term-recognition-from-flat-supervision-2","task":"Nested Term Recognition from Flat Supervision","dataset":"RuTermEval (Track 3)","model":"lemm. inc. + early dmg","rank_in_archive_order":1,"of":1,"metrics":{"Scoreboard Class-agnostic F1":"0.5875","Scoreboard Weighted F1":"0.4547"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}