{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:05:10Z","timestamp":1743084310864,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819619061"},{"type":"electronic","value":"9789819619078"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-1907-8_35","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:54:29Z","timestamp":1740426869000},"page":"370-381","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Simplify Prompt Template and Optimum Label-Select for Few-Shot NER"],"prefix":"10.1007","author":[{"given":"Xiao","family":"Qin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijing","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Chun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyou","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunqing","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenji","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Cui, L., Wu, Y., Liu, J., Yang, S., Zhang, Y.: Template-based named entity recognition using BART. In: Proceedings of the Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1835\u20131845 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.161"},{"key":"35_CR2","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (Long and Short Papers), Cambridge, MA, USA, 8\u201311 November 2019; pp. 4171\u20134186 (2019)"},{"key":"35_CR3","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized Bert pretraining approach. arXiv 2019, arXiv:1907.11692 (2019)"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: It\u2019s not just size that matters: small language models are also few-shot learners. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2339\u20132352 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.185"},{"key":"35_CR6","unstructured":"Weischedel, R., et al.: OntoNotes Release 5.0 LDC2013T19. Web Download; Linguistic Data Consortium: Philadelphia, PA, USA (2013)"},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL, Edmonton, AB, Canada, pp. 142\u2013147 (2003)","DOI":"10.3115\/1119176.1119195"},{"key":"35_CR8","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: Proceedings of the International Conference on Learning Representations, Boston, MA, USA (2017)"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Ma, R., et al.: Template-free prompt tuning for few-shot NER. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, WA, USA, 10\u201315 July 2022; pp. 5721\u20135732","DOI":"10.18653\/v1\/2022.naacl-main.420"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Fritzler, A., Logacheva, V., Kretov, M.: Few-shot classification in named entity recognition task. In: Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, Limassol, Cyprus, pp. 993\u20131000 (2019)","DOI":"10.1145\/3297280.3297378"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Hou, Y., et al.: Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1381\u20131393 (2020)","DOI":"10.18653\/v1\/2020.acl-main.128"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Yang, Y., Katiyar, A.: Simple and effective few-shot named entity recognition with structured nearest neighbor learning. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6365\u20136375 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.516"},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Few-shot named entity recognition: An empirical baseline study. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online, 10 May 2021; pp. 10408\u201310423","DOI":"10.18653\/v1\/2021.emnlp-main.813"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Ma, J., et al.: Label semantics for few shot named entity recognition. In: Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, pp. 1956\u20131971 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.155"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Das, S.S.S., Katiyar, A., Passonneau, R., Zhang, R.: CONTaiNER: few-shot named entity recognition via contrastive learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, pp. 6338\u20136353 (2022)","DOI":"10.18653\/v1\/2022.acl-long.439"},{"key":"35_CR16","unstructured":"Wolf, T., et al.: Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38\u201345 (2020)"},{"key":"35_CR17","first-page":"1","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55, 1\u201335 (2023)","journal-title":"ACM Comput. Surv."},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Arbitrary\u00a0shape natural scene text detection method based on soft attention mechanism and dilated convolution. IEEE Access 8, 122685\u2013122694 (2020)","DOI":"10.1109\/ACCESS.2020.3007351"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Chinese cursive character detection method. J. Eng. 2020(13), 626\u2013629 (2020)","DOI":"10.1049\/joe.2019.1208"},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Necklace: a novel long text detection model. J. Eng. 2020(13), 416\u2013421 (2020)","DOI":"10.1049\/joe.2019.1176"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Wu, Q., et al.: Enhanced meta-learning for cross-lingual named entity recognition with minimal resources. In: Proceedings of the AAAI Conference on Artificial Intelligence, Online, 7\u201312 February 2020; vol. 34, pp. 9274\u20139281","DOI":"10.1609\/aaai.v34i05.6466"}],"container-title":["Communications in Computer and Information Science","Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1907-8_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:54:41Z","timestamp":1740426881000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1907-8_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819619061","9789819619078"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1907-8_35","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhenzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icai.org.cn\/2024\/Organization.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}