{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:38:00Z","timestamp":1761176280390,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Large Language Models (LLMs) have demonstrated remarkable few-shot learning capabilities on Named Entity Recognition (NER) tasks, particularly through prompt-based approaches that avoid additional fine-tuning. However, despite the powerful generative and reasoning abilities of LLMs, two critical challenges remain: (1) semantic discrepancy of the same label across different datasets, which leads to recognition errors when using general labels to guide model outputs, and (2) contextual homogeneity in in-context examples, which limits the model\u2019s ability to distinguish fine-grained entity types during inference. To address these challenges, we propose LSDNER, a dual-faceted prompt construction strategy that integrates structured label semantic descriptions and promotes context diversity. Specifically, to tackle label semantic inconsistency, we introduce a structured framework that organizes label semantic descriptions into definitions, attributes, relational features, and behavioral characteristics. This representation enables LLMs to better understand the dataset-specific semantic meanings of entity labels. To address contextual monotony, we devise a diversity-driven sampling strategy for selecting in-context demonstrations, thereby expanding semantic coverage and promoting reasoning capabilities. Experiments on three general and four domain-specific NER datasets demonstrate that our approach surpasses prompt-based methods and achieves competitive results with supervised baselines. Our code is available at: https:\/\/github.com\/hui68633\/LSDNER.<\/jats:p>","DOI":"10.3233\/faia251357","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:59:14Z","timestamp":1761127154000},"source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Few Shot Named Entity Recognition via Label Semantic Description and Diversity Text"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8595-8471","authenticated-orcid":false,"given":"Hui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Ningxia University, China"},{"name":"Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, China"},{"name":"Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence, Co-founded by Ningxia Municipality and Ministry of Education, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8770-9757","authenticated-orcid":false,"given":"Fang","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Ningxia University, China"},{"name":"Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, China"},{"name":"Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence, Co-founded by Ningxia Municipality and Ministry of Education, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9311-169X","authenticated-orcid":false,"given":"Jiakun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Ningxia University, China"},{"name":"Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, China"},{"name":"Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence, Co-founded by Ningxia Municipality and Ministry of Education, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9702-6677","authenticated-orcid":false,"given":"Yu","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Ningxia University, China"},{"name":"Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, China"},{"name":"Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence, Co-founded by Ningxia Municipality and Ministry of Education, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251357","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:59:15Z","timestamp":1761127155000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251357","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}