{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T14:12:31Z","timestamp":1750860751614,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319997216"},{"type":"electronic","value":"9783319997223"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-99722-3_9","type":"book-chapter","created":{"date-parts":[[2018,8,25]],"date-time":"2018-08-25T02:35:23Z","timestamp":1535164523000},"page":"83-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Portuguese Named Entity Recognition Using LSTM-CRF"],"prefix":"10.1007","author":[{"given":"Pedro Vitor","family":"Quinta de Castro","sequence":"first","affiliation":[]},{"given":"N\u00e1dia","family":"F\u00e9lix Felipe da Silva","sequence":"additional","affiliation":[]},{"given":"Anderson","family":"da Silva Soares","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,26]]},"reference":[{"key":"9_CR1","unstructured":"How Much Data is Created on the Internet Each Day? https:\/\/blog.microfocus.com\/how-much-data-is-created-on-the-internet-each-day\/. Accessed 19 Mar 2018"},{"key":"9_CR2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-79474-2","volume-title":"Natural Language Processing for the Semantic Web","author":"D Maynard","year":"2017","unstructured":"Maynard, D., Bontcheva, K., Augenstein, I.: Natural Language Processing for the Semantic Web, 1st edn. Morgan and Claypool, San Rafael (2017)","edition":"1"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"dos Santos, C., Guimar\u00e3es, V.: Boosting named entity recognition with neural character embeddings. arXiv preprint arXiv:1505.05008 (2015)","DOI":"10.18653\/v1\/W15-3904"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)","DOI":"10.18653\/v1\/N16-1030"},{"key":"9_CR5","unstructured":"Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. arXiv preprint arxiv:1103.0398 (2011)"},{"key":"9_CR6","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.artint.2012.03.006","volume":"194","author":"Joel Nothman","year":"2013","unstructured":"Nothman, J., Ringland, N., Radford, W., Murphy, T., Curran, J.R.: Learning multilingual named entity recognition from Wikipedia. In: Artificial Intelligence, vol. 194, pp. 151\u2013175. Elsevier Science Publishers Ltd., Essex (2013). https:\/\/doi.org\/10.1016\/j.artint.2012.03.006","journal-title":"Artificial Intelligence"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Chiu, J., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. arXiv preprint arXiv:1511.08308 (2015)","DOI":"10.1162\/tacl_a_00104"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354 (2016)","DOI":"10.18653\/v1\/P16-1101"},{"key":"9_CR9","unstructured":"Reposit\u00f3rio de Word Embeddings do NILC. http:\/\/www.nilc.icmc.usp.br\/nilc\/index.php\/repositorio-de-word-embeddings-do-nilc. Accessed 30 Mar 2018"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)","DOI":"10.1162\/tacl_a_00051"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)","DOI":"10.18653\/v1\/E17-2068"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP-2014), vol. 12, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Ling, W., Dyer, C., Black, A., Trancoso, I.: Two\/too simple adaptations of word2vec for syntax problems. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics (2015)","DOI":"10.3115\/v1\/N15-1142"},{"key":"9_CR14","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arxiv:1301.3781 (2013)"},{"key":"9_CR15","unstructured":"Amaral, D., Vieira, R.: NERP-CRF: a tool for the named entity recognition using conditional random fields. In: Linguam\u00e1tica, vol. 6, pp. 41\u201349 (2014)"},{"key":"9_CR16","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.csi.2012.09.004","volume":"35","author":"M Marrero","year":"2013","unstructured":"Marrero, M., Urbano, J., S\u00e1nchez-Cuadrado, S., Morato, J., G\u00f3mez-Berb\u00eds, J.: Named entity recognition: fallacies, challenges and opportunities. Comput. Stand. Interfaces 35, 482\u2013489 (2013)","journal-title":"Comput. Stand. Interfaces"},{"key":"9_CR17","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5, 157\u2013166 (1994). https:\/\/doi.org\/10.1109\/72.279181","journal-title":"IEEE Trans. Neural Netw."},{"key":"9_CR18","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Sang, E., Veenstra, J.: Representing text chunks. arXiv preprint arxiv:cs\/9907006 (1999)","DOI":"10.3115\/977035.977059"},{"key":"9_CR20","unstructured":"HAREM: Reconhecimento de entidades mencionadas em portugu\u00eas. https:\/\/www.linguateca.pt\/HAREM\/. Accessed 21 Mar 2018"},{"key":"9_CR21","unstructured":"Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Rodrigues, J., Aluisio, S.: Portuguese word embeddings: evaluating on word analogies and natural language tasks. arXiv preprint arXiv:1708.06025 (2017)"}],"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\/978-3-319-99722-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:46:23Z","timestamp":1709811983000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-99722-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319997216","9783319997223"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-99722-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"26 August 2018","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":"Canela","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"propor2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.inf.ufrgs.br\/propor-2018\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"92","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":"42","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":"7","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":"46% - 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":"2","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":"3","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)"}}]}}