{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T22:08:00Z","timestamp":1782425280442,"version":"3.54.5"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030573201","type":"print"},{"value":"9783030573218","type":"electronic"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-57321-8_21","type":"book-chapter","created":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T21:03:33Z","timestamp":1597871013000},"page":"385-399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Improving Short Text Classification Through Global Augmentation Methods"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6731-6267","authenticated-orcid":false,"given":"Vukosi","family":"Marivate","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5197-7802","authenticated-orcid":false,"given":"Tshephisho","family":"Sefara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,8,18]]},"reference":[{"issue":"1","key":"21_CR1","first-page":"1","volume":"14","author":"M Aiken","year":"2010","unstructured":"Aiken, M., Park, M.: The efficacy of round-trip translation for MT evaluation. Transl. J. 14(1), 1\u201310 (2010)","journal-title":"Transl. J."},{"key":"21_CR2","unstructured":"Aroyehun, S.T., Gelbukh, A.: Aggression detection in social media: using deep neural networks, data augmentation, and pseudo labeling. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC 2018), pp. 90\u201397 (2018)"},{"key":"21_CR3","volume-title":"Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit","author":"S Bird","year":"2009","unstructured":"Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O\u2019Reilly Media, Inc., Sebastopol (2009)"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018)","DOI":"10.1109\/CVPR.2019.00020"},{"issue":"3","key":"21_CR5","first-page":"18","volume":"16","author":"A Das","year":"2017","unstructured":"Das, A., Ganguly, D., Garain, U.: Named entity recognition with word embeddings and Wikipedia categories for a low-resource language. ACM Trans. Asian Low-Resour. Lang. Inf. Process. (TALLIP) 16(3), 18 (2017)","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process. (TALLIP)"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: Proceedings of the 11th International AAAI Conference on Web and Social Media, pp. 512\u2013515 (2017)","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"21_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Dundar, M., Kou, Q., Zhang, B., He, Y., Rajwa, B.: Simplicity of kmeans versus deepness of deep learning: a case of unsupervised feature learning with limited data. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 883\u2013888. IEEE (2015)","DOI":"10.1109\/ICMLA.2015.78"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Fadaee, M., Bisazza, A., Monz, C.: Data augmentation for low-resource neural machine translation. arXiv preprint arXiv:1705.00440 (2017)","DOI":"10.18653\/v1\/P17-2090"},{"key":"21_CR10","unstructured":"Fedus, W., Goodfellow, I., Dai, A.M.: MaskGAN: better text generation via filling in the $$\\_$$. arXiv preprint arXiv:1801.07736 (2018)"},{"key":"21_CR11","unstructured":"Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, vol. 1, no. 12 (2009)"},{"issue":"8","key":"21_CR12","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(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 328\u2013339 (2018)","DOI":"10.18653\/v1\/P18-1031"},{"issue":"4","key":"21_CR14","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1145\/2771588","volume":"47","author":"M Imran","year":"2015","unstructured":"Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing social media messages in mass emergency: a survey. ACM Comput. Surv. (CSUR) 47(4), 67 (2015)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol. 2, pp. 427\u2013431 (2017)","DOI":"10.18653\/v1\/E17-2068"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Ko, T., Peddinti, V., Povey, D., Khudanpur, S.: Audio augmentation for speech recognition. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)","DOI":"10.21437\/Interspeech.2015-711"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Kobayashi, S.: Contextual augmentation: data augmentation by words with paradigmatic relations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), vol. 2, pp. 452\u2013457 (2018)","DOI":"10.18653\/v1\/N18-2072"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Krishnan, S., Chen, M.: Identifying tweets with fake news. In: 2018 IEEE International Conference on Information Reuse and Integration (IRI), pp. 460\u2013464. IEEE (2018)","DOI":"10.1109\/IRI.2018.00073"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Lau, J.H., Clark, A., Lappin, S.: Unsupervised prediction of acceptability judgements. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China, pp. 1618\u20131628. Association for Computational Linguistics, July 2015","DOI":"10.3115\/v1\/P15-1156"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Li, Y., Cohn, T., Baldwin, T.: Robust training under linguistic adversity. In: EACL 2017, p. 21 (2017)","DOI":"10.18653\/v1\/E17-2004"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 117\u2013122. IEEE (2018)","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"21_CR22","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"issue":"11","key":"21_CR23","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Mueller, J., Thyagarajan, A.: Siamese recurrent architectures for learning sentence similarity. In: AAAI, vol. 16, pp. 2786\u20132792 (2016)","DOI":"10.1609\/aaai.v30i1.10350"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"21_CR26","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)"},{"key":"21_CR27","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding with unsupervised learning. Technical report, OpenAI (2018)"},{"key":"21_CR28","unstructured":"Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 133\u2013142 (2003)"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Ratkiewicz, J., Conover, M., Meiss, M.R., Gon\u00e7alves, B., Flammini, A., Menczer, F.: Detecting and tracking political abuse in social media. In: ICWSM, vol. 11, pp. 297\u2013304 (2011)","DOI":"10.1609\/icwsm.v5i1.14127"},{"key":"21_CR30","unstructured":"\u0158eh\u016f\u0159ek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta, pp. 45\u201350. ELRA, May 2010"},{"issue":"4","key":"21_CR31","first-page":"119","volume":"1","author":"Y Sano","year":"2015","unstructured":"Sano, Y., Yamaguchi, K., Mine, T.: Automatic classification of complaint reports about city park. Inf. Eng. Express 1(4), 119\u2013130 (2015)","journal-title":"Inf. Eng. Express"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Sennrich, R., Haddow, B., Birch, A.: Improving neural machine translation models with monolingual data. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 86\u201396 (2016)","DOI":"10.18653\/v1\/P16-1009"},{"key":"21_CR33","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.aasri.2014.05.013","volume":"6","author":"EA Smirnov","year":"2014","unstructured":"Smirnov, E.A., Timoshenko, D.M., Andrianov, S.N.: Comparison of regularization methods for imagenet classification with deep convolutional neural networks. Aasri Procedia 6, 89\u201394 (2014)","journal-title":"Aasri Procedia"},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Wang, W.Y., Yang, D.: That\u2019s so annoying!!!: a lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2557\u20132563 (2015)","DOI":"10.18653\/v1\/D15-1306"},{"key":"21_CR35","unstructured":"Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)"},{"key":"21_CR36","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"21_CR37","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649\u2013657 (2015)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-57321-8_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T06:54:23Z","timestamp":1724050463000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-57321-8_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030573201","9783030573218"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-57321-8_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CD-MAKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Cross-Domain Conference for Machine Learning and Knowledge Extraction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","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":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cd-make2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cd-make.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}