{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T21:50:47Z","timestamp":1774129847109,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030983048","type":"print"},{"value":"9783030983055","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-98305-5_10","type":"book-chapter","created":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T08:05:37Z","timestamp":1647504337000},"page":"101-109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["PetroBERT: A Domain Adaptation Language Model for Oil and Gas Applications in Portuguese"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8776-4529","authenticated-orcid":false,"given":"Rafael B. M.","family":"Rodrigues","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0567-4082","authenticated-orcid":false,"given":"Pedro I. M.","family":"Privatto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8407-4901","authenticated-orcid":false,"given":"Gustavo Jos\u00e9","family":"de Sousa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael P.","family":"Murari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5543-3896","authenticated-orcid":false,"given":"Luis C. S.","family":"Afonso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6494-7514","authenticated-orcid":false,"given":"Jo\u00e3o P.","family":"Papa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2867-4838","authenticated-orcid":false,"given":"Daniel C. G.","family":"Pedronette","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3610-3779","authenticated-orcid":false,"given":"Ivan R.","family":"Guilherme","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephan R.","family":"Perrout","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aliel F.","family":"Riente","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","unstructured":"Alzubi, J.A., Jain, R., Singh, A., Parwekar, P., Gupta, M.: COBERT: Covid-19 question answering system using Bert. Arabian J. Sci. Eng. 1\u201311 (2021). https:\/\/doi.org\/10.1007\/s13369-021-05810-5","DOI":"10.1007\/s13369-021-05810-5"},{"key":"10_CR2","unstructured":"Amaral, D.O.F.: Reconhecimento de entidades nomeadas na \u00e1rea da geologia: bacias sedimentares brasileiras. Ph.D. thesis, Programa de P\u00f3s-Gradua\u00e7\u00e3o em Ci\u00eancia da Computa\u00e7\u00e3o (2017). http:\/\/tede2.pucrs.br\/tede2\/handle\/tede\/8035, escola Polit\u00e9cnica"},{"key":"10_CR3","unstructured":"Araci, D.: Finbert: Financial sentiment analysis with pre-trained language models (2019). http:\/\/arxiv.org\/abs\/1908.10063"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3615\u20133620. Association for Computational Linguistics, Hong Kong, China (2019)","DOI":"10.18653\/v1\/D19-1371"},{"key":"10_CR5","doi-asserted-by":"publisher","first-page":"108939","DOI":"10.1016\/j.petrol.2021.108939","volume":"205","author":"LP Cinelli","year":"2021","unstructured":"Cinelli, L.P.: Automatic event identification and extraction from daily drilling reports using an expert system and artificial intelligence. J. Petrol. Sci. Eng. 205, 108939 (2021)","journal-title":"J. Petrol. Sci. Eng."},{"key":"10_CR6","unstructured":"Consoli, B., Santos, J., Gomes, D., Cordeiro, F., Vieira, R., Moreira, V.: Embeddings for named entity recognition in geoscience Portuguese literature. In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 4625\u20134630. European Language Resources Association, Marseille, France, May 2020"},{"key":"10_CR7","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Hoffimann, J., Mao, Y., Wesley, A., Taylor, A.: Sequence mining and pattern analysis in drilling reports with deep natural language processing. In: SPE Annual Technical Conference and Exhibition, vol. Day 3 Wed, 26 September 2018 (2018)","DOI":"10.2118\/191505-MS"},{"key":"10_CR9","unstructured":"Huang, K., Altosaar, J., Ranganath, R.: ClinicalBERT: modeling clinical notes and predicting hospital readmission (2020)"},{"issue":"4","key":"10_CR10","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2019","unstructured":"Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2019)","journal-title":"Bioinformatics"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Huang, D., Huang, K., Li, Z., Zhao, J.: FinBERT: a pre-trained financial language representation model for financial text mining. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 4513\u20134519. International Joint Conferences on Artificial Intelligence Organization (2020)","DOI":"10.24963\/ijcai.2020\/622"},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"106846","DOI":"10.1016\/j.petrol.2019.106846","volume":"187","author":"LC Ribeiro","year":"2020","unstructured":"Ribeiro, L.C., Afonso, L.C., Colombo, D., Guilherme, I.R., Papa, J.P.: Evolving neural conditional random fields for drilling report classification. J. Petrol. Sci. Eng. 187, 106846 (2020)","journal-title":"J. Petrol. Sci. Eng."},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Schneider, E.T.R., et al.: BioBERTpt - a Portuguese neural language model for clinical named entity recognition. In: Proceedings of the 3rd Clinical Natural Language Processing Workshop, pp. 65\u201372. Association for Computational Linguistics, November 2020","DOI":"10.18653\/v1\/2020.clinicalnlp-1.7"},{"key":"10_CR14","unstructured":"Shen, J.T., Yamashita, M., Prihar, E., Heffernan, N., Wu, X., Graff, B., Lee, D.: MathBERT: a pre-trained language model for general NLP tasks in mathematics education (2021)"},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"103347","DOI":"10.1016\/j.compind.2020.103347","volume":"124","author":"D da Silva Magalh\u00e3es Gomes","year":"2021","unstructured":"da Silva Magalh\u00e3es Gomes, D., et al.: Portuguese word embeddings for the oil and gas industry: development and evaluation. Comput. Indus. 124, 103347 (2021)","journal-title":"Comput. Indus."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Sousa, G.J., et al.: Pattern analysis in drilling reports using optimum-path forest. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2018)","DOI":"10.1109\/IJCNN.2018.8489232"},{"key":"10_CR17","unstructured":"Wu, Y., et al.: Google\u2019s neural machine translation system: bridging the gap between human and machine translation. CoRR abs\/1609.08144 (2016). http:\/\/arxiv.org\/abs\/1609.08144"}],"container-title":["Lecture Notes in Computer Science","Computational Processing of the Portuguese Language"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-98305-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T10:09:23Z","timestamp":1674986963000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98305-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030983048","9783030983055"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98305-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PROPOR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Processing of the Portuguese Language","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 March 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 March 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"propor2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.universidadedefortaleza.com\/propor2022\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"88","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":"36","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":"4","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":"41% - 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":"3.16","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":"2.87","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"A Scientific Review Committee of 97 researchers reviewed all papers. Conference was held online due to COVID-19.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}