{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T22:01:56Z","timestamp":1770069716740,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["RF-B2022002"],"award-info":[{"award-number":["RF-B2022002"]}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["62072410"],"award-info":[{"award-number":["62072410"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["RF-B2022002"],"award-info":[{"award-number":["RF-B2022002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072410"],"award-info":[{"award-number":["62072410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Named entity recognition (NER) in a few-shot setting is an extremely challenging task, and most existing methods fail to account for the gap between NER tasks and pre-trained language models. Although prompt learning has been successfully applied in few-shot classification tasks, adapting to token-level classification similar to the NER task presents challenges in terms of time consumption and efficiency. In this work, we propose a decomposed prompt learning NER framework for few-shot settings, decomposing the NER task into two stages: entity locating and entity typing. In training, the location information of distant labels is used to train the entity locating model. A concise but effective prompt template is built to train the entity typing model. In inference, a pipeline approach is used to handle the entire NER task, which elegantly resolves time-consuming and inefficient problems. Specifically, a well-trained entity locating model is used to predict entity spans for each input. The input is then transformed using prompt templates, and the well-trained entity typing model is used to predict their types in a single step. Experimental results demonstrate that our framework outperforms previous prompt-based methods by an average of 2.3\u201312.9% in F1 score while achieving the best trade-off between accuracy and inference speed.<\/jats:p>","DOI":"10.3390\/info14050262","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T04:36:15Z","timestamp":1682656575000},"page":"262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Decomposed Two-Stage Prompt Learning for Few-Shot Named Entity Recognition"],"prefix":"10.3390","volume":"14","author":[{"given":"Feiyang","family":"Ye","sequence":"first","affiliation":[{"name":"The College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6924-4466","authenticated-orcid":false,"given":"Liang","family":"Huang","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Senjie","family":"Liang","sequence":"additional","affiliation":[{"name":"The College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"KaiKai","family":"Chi","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"ref_1","first-page":"4171","article-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","volume":"Volume 1","author":"Devlin","year":"2019","journal-title":"Chapter of the Association for Computational Linguistics: Human Language Technologies, (Long and Short Papers), Proceedings of the 2019 Conference of the North American, Cambridge, MA, USA, 8\u201311 November 2019"},{"key":"ref_2","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. 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