{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:33:55Z","timestamp":1743042835049,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030591366"},{"type":"electronic","value":"9783030591373"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59137-3_9","type":"book-chapter","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T12:03:04Z","timestamp":1601035384000},"page":"89-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Divide to Better Classify"],"prefix":"10.1007","author":[{"given":"Yves","family":"Mercadier","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00e9r\u00f4me","family":"Az\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandra","family":"Bringay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,26]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","unstructured":"Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) EMNLP-IJCNLP 2019, Hong Kong, China, 2019. pp. 3613\u20133618. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1371","DOI":"10.18653\/v1\/D19-1371"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Dernoncourt, F., Lee, J.Y.: Pubmed 200k RCT: a dataset for sequential sentence classification in medical abstracts. In: Kondrak, G., Watanabe, T. (eds.) IJCNLP 2017. Volume 2: Short Papers, Taipei, Taiwan, 2017, pp. 308\u2013313. Asian Federation of Natural Language Processing (2017)","DOI":"10.18653\/v1\/E17-2110"},{"key":"9_CR3","doi-asserted-by":"publisher","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.) NAACL-HLT 2019. Volume 1 (Long and Short Papers), Minneapolis, MN, USA, 2019, pp. 4171\u20134186, Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Edunov, S., Ott, M., Auli, M., Grangier, D.: Understanding back-translation at scale. CoRR abs\/1808.09381 (2018)","DOI":"10.18653\/v1\/D18-1045"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Gr\u00e4\u00dfer, F., Kallumadi, S., Malberg, H., Zaunseder, S.: Aspect-based sentiment analysis of drug reviews applying cross-domain and cross-data learning. In: Kostkova, P., Grasso, F., Castillo, C., Mejova, Y., Bosman, A., Edelstein, M. (eds.) Digital Health, DH 2018, pp. 121\u2013125. ACM, Lyon (2018). https:\/\/doi.org\/10.1145\/3194658.3194677","DOI":"10.1145\/3194658.3194677"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Gupta, R.: Data augmentation for low resource sentiment analysis using generative adversarial networks. CoRR abs\/1902.06818 (2019)","DOI":"10.1109\/ICASSP.2019.8682544"},{"key":"9_CR7","doi-asserted-by":"publisher","unstructured":"Kobayashi, S.: Contextual augmentation: Data augmentation by words with paradigmatic relations. In: Walker, M.A., Ji, H., Stent, A. (eds.) NAACL-HLT, Volume 2 (Short Papers), New Orleans, Louisiana, USA, 1\u20136 June 2018, pp. 452\u2013457. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/n18-2072","DOI":"10.18653\/v1\/n18-2072"},{"key":"9_CR8","unstructured":"Lan, Z.Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: A lite BERT for self-supervised learning of language representations. http:\/\/arxiv.org\/abs\/1909.11942 (2019)"},{"key":"9_CR9","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019)"},{"key":"9_CR10","doi-asserted-by":"publisher","first-page":"103265","DOI":"10.1016\/j.jbi.2019.103265","volume":"98","author":"R Maldonado","year":"2019","unstructured":"Maldonado, R., Harabagiu, S.M.: Active deep learning for the identification of concepts and relations in electroencephalography reports. J. Biomed. Inform. 98, 103265 (2019). https:\/\/doi.org\/10.1016\/j.jbi.2019.103265","journal-title":"J. Biomed. Inform."},{"key":"9_CR11","unstructured":"Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. CoRR abs\/1712.04621 (2017)"},{"key":"9_CR12","unstructured":"Ragheb, W., Moulahi, B., Az\u00e9, J., Bringay, S., Servajean, M.: Temporal mood variation: at the CLEF eRisk-2018 tasks for early risk detection on the Internet. In: Cappellato, L., Ferro, N., Nie, J., Soulier, L. (eds.) Working Notes of CLEF 2018, Avignon, France, 10\u201314 September 2018, vol. 2125. CEUR-WS.org (2018)"},{"key":"9_CR13","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter https:\/\/arxiv.org\/abs\/1910.01108 (2019)"},{"key":"9_CR14","unstructured":"Shleifer, S.: Low resource text classification with ULMFit and backtranslation. CoRR abs\/1903.09244 (2019)"},{"issue":"1","key":"9_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1\u201348 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J. Big Data"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Wei, J.W., Zou, K.: EDA: easy data augmentation techniques for boosting performance on text classification tasks. CoRR abs\/1901.11196 (2019)","DOI":"10.18653\/v1\/D19-1670"},{"key":"9_CR17","unstructured":"Xie, Q., Dai, Z., Hovy, E.H., Luong, M., Le, Q.V.: Unsupervised data augmentation. CoRR abs\/1904.12848 (2019)"},{"key":"9_CR18","unstructured":"Xie, Z., et al.: Data noising as smoothing in neural network language models. In: ICLR Proceedings of the Conference on Track 2017, OpenReview.net, Toulon (2017)"},{"key":"9_CR19","unstructured":"Zhang, X., LeCun, Y.: Text understanding from scratch. CoRR abs\/1502.01710 (2015)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59137-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:25:15Z","timestamp":1710339915000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59137-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030591366","9783030591373"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59137-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"26 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Minneapolis, MN","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/aime20.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"103","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":"1","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","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}