{"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-boundary-regressing-model-for-nested-named","title":"A Boundary Regression Model for Nested Named Entity Recognition","arxiv_id":"2011.14330","date":"2020-11-29","proceeding":null,"authors":["Yanping Chen","Lefei Wu","Qinghua Zheng","Ruizhang Huang","Jun Liu","Liyuan Deng","Junhui Yu","Yongbin Qing","Bo Dong","Ping Chen"],"abstract":"Recognizing named entities (NEs) is commonly conducted as a classification problem that predicts a class tag for a word or a NE candidate in a sentence. In shallow structures, categorized features are weighted to support the prediction. Recent developments in neural networks have adopted deep structures that map categorized features into continuous representations. This approach unfolds a dense space saturated with high-order abstract semantic information, where the prediction is based on distributed feature representations. In this paper, positions of NEs in a sentence are represented as continuous values. Then, a regression operation is introduced to regress boundaries of NEs in a sentence. Based on boundary regression, we design a boundary regression model to support nested NE recognition. It is a multiobjective learning framework, which simultaneously predicts the classification score of a NE candidate and refine its spatial location in a sentence. It has the advantage to resolve nested NEs and support boundary regression for locating NEs in a sntence. By sharing parameters for predicting and locating, this model enables more potent nonlinear function approximators to enhance model discriminability. Experiments demonstrate state-of-the-art performance for nested NE recognition\\footnote{Our codes to implement the BR model are available at: \\url{https://github.com/wuyuefei3/BR}.}.","url_abs":"https://arxiv.org/abs/2011.14330v3","url_pdf":"https://arxiv.org/pdf/2011.14330v3.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-boundary-regressing-model-for-nested-named","repo_url":"https://github.com/wuyuefei3/BR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"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":"sentence","task_name":"Sentence"},{"task_slug":"tag","task_name":"TAG"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}