{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:25:29Z","timestamp":1777389929318,"version":"3.51.4"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159830","type":"print"},{"value":"9783032159847","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-15984-7_10","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:34:24Z","timestamp":1769718864000},"page":"135-149","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Information Retrieval and\u00a0In-Context Learning for\u00a0Low-Resource Named Entity Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4423-9278","authenticated-orcid":false,"given":"Enzo","family":"Shiraishi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6021-747X","authenticated-orcid":false,"given":"Raphael Y.","family":"de Camargo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2207-7522","authenticated-orcid":false,"given":"Henrique L. P.","family":"Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8597-4987","authenticated-orcid":false,"given":"Ronaldo C.","family":"Prati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"10_CR1","unstructured":"Achiam, J., et\u00a0al.: GPT-4 Technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"10_CR2","unstructured":"Ashok, D., Lipton, Z.C.: PromptNER: prompting for named entity recognition. arXiv preprint arXiv:2305.15444 (2023)"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Bai, F., Hassanzadeh, H., Saeedi, A., Dredze, M.: Label-guided in-context learning for named entity recognition. arXiv preprint arXiv:2505.23722 (2025)","DOI":"10.18653\/v1\/2025.emnlp-main.1441"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Bogdanov, S., Constantin, A., Bernard, T., Crabb\u00e9, B., Bernard, E.: NuNER: entity recognition encoder pre-training via LLM-annotated data. arXiv preprint arXiv:2402.15343 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.660"},{"key":"10_CR5","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335\u2013336 (1998)","DOI":"10.1145\/290941.291025"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: BGE M3-embedding: multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprint arXiv:2402.03216 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.137"},{"key":"10_CR8","unstructured":"Chinchor, N., Robinson, P.: MUC-7 named entity task definition. In: Proceedings of the 7th Conference on Message Understanding, vol.\u00a029, pp. 1\u201321 (1997)"},{"issue":"240","key":"10_CR9","first-page":"1","volume":"24","author":"A Chowdhery","year":"2023","unstructured":"Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: PaLM: scaling language modeling with pathways. J. Mach. Learn. Res. 24(240), 1\u2013113 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR10","unstructured":"Cobbe, K., et\u00a0al.: Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168 (2021)"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Cormack, G.V., Clarke, C.L., Buettcher, S.: Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In: ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 758\u2013759 (2009)","DOI":"10.1145\/1571941.1572114"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Cui, L., Wu, Y., Liu, J., Yang, S., Zhang, Y.: Template-based named entity recognition using BART. In: ACL-IJCNLP 2021, pp. 1835\u20131845. ACL, August 2021","DOI":"10.18653\/v1\/2021.findings-acl.161"},{"key":"10_CR13","unstructured":"Dong, Q., et al.: A survey on in-context learning. arXiv preprint arXiv:2301.00234 (2022)"},{"key":"10_CR14","unstructured":"Gao, Y., et al.: Retrieval-augmented generation for large language models: a survey. arXiv preprint arXiv:2312.10997 (2023)"},{"key":"10_CR15","unstructured":"Hendrycks, D., et al.: Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300 (2020)"},{"key":"10_CR16","unstructured":"Honnibal, M., Montani, I., Van\u00a0Landeghem, S., Boyd, A.: spaCy: industrial-strength natural language processing in python (2020), Accessed in May 2025"},{"key":"10_CR17","unstructured":"Huang, J., et al.: Few-shot named entity recognition: a comprehensive study. arXiv preprint arXiv:2012.14978 (2020)"},{"key":"10_CR18","doi-asserted-by":"publisher","first-page":"100017","DOI":"10.1016\/j.nlp.2023.100017","volume":"3","author":"B Jehangir","year":"2023","unstructured":"Jehangir, B., Radhakrishnan, S., Agarwal, R.: A survey on named entity recognition - datasets, tools, and methodologies. Nat. Lang. Process. J. 3, 100017 (2023)","journal-title":"Nat. Lang. Process. J."},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Kamphuis, C., de\u00a0Vries, A.P., Boytsov, L., Lin, J.: Which BM25 do you mean? a large-scale reproducibility study of scoring variants. In: Advances in Information Retrieval, pp. 28\u201334. Springer International Publishing (2020)","DOI":"10.1007\/978-3-030-45442-5_4"},{"key":"10_CR20","unstructured":"Lee, D.H., et al.: Good examples make a faster learner: simple demonstration-based learning for low-resource NER. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2687\u20132700. ACL"},{"issue":"1","key":"10_CR21","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","volume":"34","author":"J Li","year":"2022","unstructured":"Li, J., Sun, A., Han, J., Li, C.: A survey on deep learning for named entity recognition. IEEE Trans. Knowl. Data Eng. 34(1), 50\u201370 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10_CR22","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: CrossNER: evaluating cross-domain named entity recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 13452\u201313460 (2021)","DOI":"10.1609\/aaai.v35i15.17587"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Lu, S., Bigoulaeva, I., Sachdeva, R., Madabushi, H.T., Gurevych, I.: Are emergent abilities in large language models just in-context learning? arXiv preprint arXiv:2309.01809 (2023)","DOI":"10.18653\/v1\/2024.acl-long.279"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Manning, C.D., Raghavan, P., Sch\u00fctze, H.: An Introduction to Information Retrieval. Cambridge University Press (2009)","DOI":"10.1017\/CBO9780511809071"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Min, S., et al.: Rethinking the role of demonstrations: what makes in-context learning work? In: 2022 Conference on Empirical Methods in Natural Language Processing, pp. 11048\u201311064. ACL, December 2022","DOI":"10.18653\/v1\/2022.emnlp-main.759"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Muennighoff, N., Tazi, N., Magne, L., Reimers, N.: MTEB: massive text embedding benchmark. In: 17th Conference of the European Chapter of the ACL, pp. 2014\u20132037. ACL, May 2023","DOI":"10.18653\/v1\/2023.eacl-main.148"},{"key":"10_CR28","unstructured":"Nye, M., et al.: Show your work: scratchpads for intermediate computation with language models (2021), arXiv preprint arXiv: 2112.00114"},{"key":"10_CR29","unstructured":"P\u00e9rez-Iglesias, J., P\u00e9rez-Ag\u00fcera, J.R., Fresno, V., Feinstein, Y.Z.: Integrating the probabilistic models BM25\/BM25F into lucene. arXiv preprint arXiv:0911.5046 (2009)"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: Proceedings of the Conference on Computational Natural Language Learning (CoNLL), June 2009","DOI":"10.3115\/1596374.1596399"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982\u20133992. ACL, November 2019","DOI":"10.18653\/v1\/D19-1410"},{"issue":"4","key":"10_CR32","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1561\/1500000019","volume":"3","author":"S Robertson","year":"2009","unstructured":"Robertson, S., Zaragoza, H., et al.: The probabilistic relevance framework: BM25 and beyond. Found. Trends\u00ae Inf. Retrieval 3(4), 333\u2013389 (2009)","journal-title":"Found. Trends\u00ae Inf. Retrieval"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M.M., Gatford, M., et al.: Okapi at trec-3. Nist Special Publication Sp 109, 109 (1995)","DOI":"10.6028\/NIST.SP.500-225.city"},{"key":"10_CR34","unstructured":"Schaeffer, R., Miranda, B., Koyejo, S.: Are emergent abilities of large language models a mirage? In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"issue":"13s","key":"10_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582688","volume":"55","author":"Y Song","year":"2023","unstructured":"Song, Y., Wang, T., Cai, P., Mondal, S.K., Sahoo, J.P.: A comprehensive survey of few-shot learning: evolution, applications, challenges, and opportunities. ACM Comput. Surv. 55(13s), 1\u201340 (2023)","journal-title":"ACM Comput. Surv."},{"key":"10_CR36","unstructured":"Souza, F., Nogueira, R., Lotufo, R.: Portuguese named entity recognition using BERT-CRF. arXiv preprint arXiv:1909.10649 (2019)"},{"key":"10_CR37","unstructured":"Team, G., et\u00a0al.: Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Tjong Kim\u00a0Sang, E.F., De\u00a0Meulder, 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 2003, pp. 142\u2013147 (2003)","DOI":"10.3115\/1119176.1119195"},{"key":"10_CR39","doi-asserted-by":"crossref","unstructured":"Trotman, A., Puurula, A., Burgess, B.: Improvements to BM25 and Language models examined. In: Proceedings of the 19th Australasian Document Computing Symposium, pp. 58\u201365 (2014)","DOI":"10.1145\/2682862.2682863"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: GPT-NER: named entity recognition via large language models. In: Findings of the Association for Computational Linguistics: NAACL 2025, pp. 4257\u20134275. ACL, April 2025","DOI":"10.18653\/v1\/2025.findings-naacl.239"},{"key":"10_CR41","unstructured":"Wei, J., et\u00a0al.: Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022)"},{"key":"10_CR42","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural. Inf. Process. Syst. 35, 24824\u201324837 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR43","doi-asserted-by":"crossref","unstructured":"Ye, X., Iyer, S., Celikyilmaz, A., Stoyanov, V., Durrett, G., Pasunuru, R.: Complementary explanations for effective In-Context Learning. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 4469\u20134484. ACL, July 2023","DOI":"10.18653\/v1\/2023.findings-acl.273"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Zaratiana, U., Tomeh, N., Holat, P., Charnois, T.: GLiNER: generalist model for named entity recognition using bidirectional transformer. In: Conference of the North American Chapter of the ACL: Human Language Technologies, pp. 5364\u20135376. ACL, June 2024","DOI":"10.18653\/v1\/2024.naacl-long.300"},{"key":"10_CR45","unstructured":"Zhang, Z., Zhang, A., Li, M., Smola, A.: Automatic chain of thought prompting in large language models. In: The Eleventh International Conference on Learning Representations (ICLR 2023) (2023)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15984-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:34:33Z","timestamp":1769718873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15984-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159830","9783032159847"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15984-7_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}