{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:21:49Z","timestamp":1743106909357,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031453915"},{"type":"electronic","value":"9783031453922"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-45392-2_6","type":"book-chapter","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T20:17:29Z","timestamp":1697055449000},"page":"80-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimization Strategies for\u00a0BERT-Based Named Entity Recognition"],"prefix":"10.1007","author":[{"given":"Monique","family":"Monteiro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cleber","family":"Zanchettin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"6_CR1","unstructured":"Brown, T., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 1877\u20131901. Curran Associates, Inc. (2020), https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Chen, Y., Mikkelsen, J., Binder, A., Alt, C., Hennig, L.: A comparative study of pre-trained encoders for low-resource named entity recognition. In: Gella, S., et al (eds.) Proceedings of the 7th Workshop on Representation Learning for NLP, RepL4NLP@ACL 2022, Dublin, Ireland, 26 May 2022, pp. 46\u201359. Association for Computational Linguistics (2022). https:\/\/doi.org\/10.18653\/v1\/2022.repl4nlp-1.6","DOI":"10.18653\/v1\/2022.repl4nlp-1.6"},{"key":"6_CR3","doi-asserted-by":"publisher","unstructured":"Dai, X., Adel, H.: An analysis of simple data augmentation for named entity recognition. In: Scott, D., Bel, N., Zong, C. (eds.) Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), 8-13 December 2020, pp. 3861\u20133867. International Committee on Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.343","DOI":"10.18653\/v1\/2020.coling-main.343"},{"key":"6_CR4","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR arXiv: 1810.04805"},{"key":"6_CR5","doi-asserted-by":"publisher","unstructured":"Fu, J., Liu, P., Neubig, G.: Interpretable multi-dataset evaluation for named entity recognition. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, 16-20 November 2020, pp. 6058\u20136069. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.489","DOI":"10.18653\/v1\/2020.emnlp-main.489"},{"key":"6_CR6","unstructured":"Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research, vol.\u00a097, pp. 2790\u20132799. PMLR (2019), http:\/\/proceedings.mlr.press\/v97\/houlsby19a.html"},{"key":"6_CR7","unstructured":"Monteiro, M.: Extrator de entidades mencionadas em not\u00edcias da m\u00eddia. https:\/\/github.com\/SecexSaudeTCU\/noticias_ner (2021), (Accessed 21 May 2022)"},{"key":"6_CR8","unstructured":"Monteiro, M.: Riskdata brazilian portuguese ner. https:\/\/huggingface.co\/monilouise\/ner_news_portuguese (2021), (Accessed 21 May 2022)"},{"key":"6_CR9","doi-asserted-by":"publisher","unstructured":"Rodrigues, J., et al.: Advancing neural encoding of portuguese with transformer albertina PT-. CoRR https:\/\/doi.org\/10.48550\/arXiv.2305.06721, https:\/\/doi.org\/10.48550\/arXiv.2305.06721 (2023)","DOI":"10.48550\/arXiv.2305.06721"},{"key":"6_CR10","unstructured":"Santos, D., Seco, N., Cardoso, N., Vilela, R.: HAREM: an advanced NER evaluation contest for portuguese. In: Calzolari, N., et al. (eds.) Proceedings of the Fifth International Conference on Language Resources and Evaluation, LREC 2006, Genoa, Italy, 22-28 May 2006, pp. 1986\u20131991. European Language Resources Association (ELRA) (2006), http:\/\/www.lrec-conf.org\/proceedings\/lrec2006\/summaries\/59.html"},{"key":"6_CR11","doi-asserted-by":"publisher","unstructured":"Silva, E.H.M.D., Laterza, J., Silva, M.P.P.D., Ladeira, M.: A proposal to identify stakeholders from news for the institutional relationship management activities of an institution based on named entity recognition using BERT. In: Wani, M.A., Sethi, I.K., Shi, W., Qu, G., Raicu, D.S., Jin, R. (eds.) 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Pasadena, CA, USA, 13\u201316 December 2021, pp. 1569\u20131575. IEEE (2021). https:\/\/doi.org\/10.1109\/ICMLA52953.2021.00251","DOI":"10.1109\/ICMLA52953.2021.00251"},{"key":"6_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/978-3-030-61377-8_28","volume-title":"Intelligent Systems","author":"F Souza","year":"2020","unstructured":"Souza, F., Nogueira, R., Lotufo, R.: BERTimbau: pretrained BERT models for Brazilian Portuguese. In: Cerri, R., Prati, R.C. (eds.) BRACIS 2020. LNCS (LNAI), vol. 12319, pp. 403\u2013417. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61377-8_28"},{"key":"6_CR13","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/978-3-030-32381-3_16","volume-title":"Chinese Computational Linguistics","author":"C Sun","year":"2019","unstructured":"Sun, C., Qiu, X., Xu, Y., Huang, X.: How to fine-tune BERT for text classification? In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds.) CCL 2019. LNCS (LNAI), vol. 11856, pp. 194\u2013206. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32381-3_16"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"T\u00e4nzer, M., Ruder, S., Rei, M.: Memorisation versus generalisation in pre-trained language models. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7564\u20137578. Association for Computational Linguistics, Dublin, Ireland (May 2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.521","DOI":"10.18653\/v1\/2022.acl-long.521"},{"key":"6_CR15","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u., Polosukhin, I.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"6_CR16","unstructured":"Wagner\u00a0Filho, J.A., Wilkens, R., Idiart, M., Villavicencio, A.: The brWaC corpus: a new open resource for Brazilian Portuguese. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan (May 2018). https:\/\/aclanthology.org\/L18-1686"},{"key":"6_CR17","unstructured":"Wortsman, M., et al.: Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. CoRR abs\/2203.05482 (2022)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45392-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:46:25Z","timestamp":1710348385000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45392-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031453915","9783031453922"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45392-2_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 October 2023","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":"Belo Horizonte","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bracis.dcc.ufmg.br","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"242","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"90","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}