{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T17:34:21Z","timestamp":1772818461090,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176074"],"award-info":[{"award-number":["62176074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s13042-024-02180-w","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T11:01:36Z","timestamp":1716462096000},"page":"127-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Prompt-based data labeling method for aspect based sentiment analysis"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6504-3154","authenticated-orcid":false,"given":"Kun","family":"Bu","sequence":"first","affiliation":[]},{"given":"Yuanchao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"2180_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111148","volume":"283","author":"K Bu","year":"2024","unstructured":"Bu K, Liu Y, Ju X (2024) Efficient utilization of pre-trained models: a review of sentiment analysis via prompt learning. Knowl-Based Syst 283:111148. https:\/\/doi.org\/10.1016\/j.knosys.2023.111148","journal-title":"Knowl-Based Syst"},{"key":"2180_CR2","doi-asserted-by":"publisher","first-page":"246","DOI":"10.18653\/V1\/2021.EMNLP-MAIN.22","volume-title":"Learning implicit sentiment in aspect-based sentiment analysis with supervised contrastive pre-training","author":"Z Li","year":"2021","unstructured":"Li Z, Zou Y, Zhang C, Zhang Q, Wei Z (2021) Learning implicit sentiment in aspect-based sentiment analysis with supervised contrastive pre-training. In: Proceedings of the 2021 conference on empirical methods in natural language processing. Association for computational linguistics, Online and Punta Cana, Dominican Republic, pp 246\u2013256. https:\/\/doi.org\/10.18653\/V1\/2021.EMNLP-MAIN.22"},{"key":"2180_CR3","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Bartlett PL, Pereira FCN, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25: 26th Annual Conference on Neural Information Processing Systems. Proceedings of a Meeting Held 3\u20136 Dec 2012, Lake Tahoe, Nevada, United States, pp 1106\u20131114 . https:\/\/proceedings.neurips.cc\/paper\/2012\/hash\/c399862d3b9d6b76c8436e924a68c45b-Abstract.html"},{"key":"2180_CR4","unstructured":"Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. CoRR. arXiv:1712.04621"},{"key":"2180_CR5","unstructured":"Singh J, McCann B, Keskar NS, Xiong C, Socher R (2019) XLDA: Cross-lingual data augmentation for natural language inference and question answering. CoRR. arXiv:1905.11471"},{"key":"2180_CR6","doi-asserted-by":"publisher","unstructured":"Fadaee M, Bisazza A, Monz C (2017) Data augmentation for low-resource neural machine translation, pp 567\u2013573. https:\/\/doi.org\/10.18653\/V1\/P17-2090","DOI":"10.18653\/V1\/P17-2090"},{"key":"2180_CR7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10350","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"J Mueller","year":"2016","unstructured":"Mueller J, Thyagarajan A (2016) Siamese recurrent architectures for learning sentence similarity. Proceedings of the AAAI conference on artificial intelligence. 30(1). https:\/\/doi.org\/10.1609\/aaai.v30i1.10350"},{"key":"2180_CR8","doi-asserted-by":"publisher","unstructured":"Wei JW, Zou K (2019) EDA: easy data augmentation techniques for boosting performance on text classification tasks, pp 6381\u20136387. https:\/\/doi.org\/10.18653\/v1\/D19-1670","DOI":"10.18653\/v1\/D19-1670"},{"issue":"1","key":"2180_CR9","first-page":"27","volume":"27","author":"IJ Goodfellow","year":"2014","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial nets. Adv Neural Info Process Syst pp. 27","journal-title":"Advances in neural information processing systems"},{"key":"2180_CR10","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational bayes. The International Conference on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=33X9fd2-9FyZd"},{"key":"2180_CR11","doi-asserted-by":"publisher","unstructured":"Gupta R (2019) Data augmentation for low resource sentiment analysis using generative adversarial networks, pp 7380\u20137384. https:\/\/doi.org\/10.1109\/ICASSP.2019.8682544","DOI":"10.1109\/ICASSP.2019.8682544"},{"issue":"7","key":"2180_CR12","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1145\/3544558","volume":"55","author":"M Bayer","year":"2023","unstructured":"Bayer M, Kaufhold M, Reuter C (2023) A survey on data augmentation for text classification. ACM Comput Surv 55(7):146\u2013114639. https:\/\/doi.org\/10.1145\/3544558","journal-title":"ACM Comput Surv"},{"key":"2180_CR13","doi-asserted-by":"publisher","unstructured":"Kobayashi S (2018) Contextual augmentation: data augmentation by words with paradigmatic relations, pp 452\u2013457. https:\/\/doi.org\/10.18653\/v1\/n18-2072","DOI":"10.18653\/v1\/n18-2072"},{"key":"2180_CR14","first-page":"6691\u20136704","volume":"6691\u20136704","author":"B Wang","year":"2022","unstructured":"Wang B, Ding L, Zhong Q, Li X, Tao D (2022) A contrastive cross-channel data augmentation framework for aspect-based sentiment analysis. In: Proceedings of the 29th International\nConference on Computational Linguistics, Int Committee Comput Linguist, Gyeongju, Republic of Korea, 6691\u20136704","journal-title":"Computational Linguistics."},{"key":"2180_CR15","doi-asserted-by":"publisher","unstructured":"Ebrahimi J, Rao A, Lowd D, Dou D, Hotflip (2018) White-box adversarial examples for text classification, pp 31\u201336. https:\/\/doi.org\/10.18653\/V1\/P18-2006","DOI":"10.18653\/V1\/P18-2006"},{"key":"2180_CR16","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.aiopen.2022.03.001","volume":"3","author":"B Li","year":"2022","unstructured":"Li B, Hou Y, Che W (2022) Data augmentation approaches in natural language processing: a survey. AI Open 3:71\u201390. https:\/\/doi.org\/10.1016\/j.aiopen.2022.03.001","journal-title":"AI Open"},{"key":"2180_CR17","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/S40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson JM, Khoshgoftaar TM (2019) Survey on deep learning with class imbalance. J Big Data 6:27. https:\/\/doi.org\/10.1186\/S40537-019-0192-5","journal-title":"J Big Data"},{"key":"2180_CR18","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/J.INS.2013.07.007","volume":"250","author":"V L\u00f3pez","year":"2013","unstructured":"L\u00f3pez V, Fern\u00e1ndez A, Garc\u00eda S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113\u2013141. https:\/\/doi.org\/10.1016\/J.INS.2013.07.007","journal-title":"Inf Sci"},{"key":"2180_CR19","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/J.INS.2019.11.004","volume":"513","author":"FA Thabtah","year":"2020","unstructured":"Thabtah FA, Hammoud S, Kamalov F, Gonsalves AH (2020) Data imbalance in classification: experimental evaluation. Inf Sci 513:429\u2013441. https:\/\/doi.org\/10.1016\/J.INS.2019.11.004","journal-title":"Inf Sci"},{"key":"2180_CR20","unstructured":"Bu J, Daw A, Maruf M, Karpatne A (2021) Learning compact representations of neural networks using discriminative masking (DAM), pp 3491\u20133503"},{"key":"2180_CR21","doi-asserted-by":"publisher","unstructured":"Liu Z, Li J, Shen Z, Huang G, Yan S, Zhang C (2017) Learning efficient convolutional networks through network slimming, pp 2755\u20132763. https:\/\/doi.org\/10.1109\/ICCV.2017.298","DOI":"10.1109\/ICCV.2017.298"},{"key":"2180_CR22","doi-asserted-by":"publisher","unstructured":"Chen X, Zhang Y, Deng J, Jiang J, Wang W Gotta (2023) Generative few-shot question answering by prompt-based cloze data augmentation, pp 909\u2013917 . https:\/\/doi.org\/10.1137\/1.9781611977653.CH102","DOI":"10.1137\/1.9781611977653.CH102"},{"key":"2180_CR23","doi-asserted-by":"publisher","unstructured":"Liu J, Chen Y, Xu J (2022) Low-resource NER by data augmentation with prompting, pp 4252\u20134258. https:\/\/doi.org\/10.24963\/IJCAI.2022\/590","DOI":"10.24963\/IJCAI.2022\/590"},{"key":"2180_CR24","doi-asserted-by":"publisher","unstructured":"Wang Y, Xu C, Sun Q, Hu H, Tao C, Geng X, Jiang D (2022) Promda: prompt-based data augmentation for low-resource NLU tasks, pp 4242\u20134255. https:\/\/doi.org\/10.18653\/V1\/2022.ACL-LONG.292","DOI":"10.18653\/V1\/2022.ACL-LONG.292"},{"key":"2180_CR25","doi-asserted-by":"publisher","unstructured":"Abaskohi A, Rothe S, Yaghoobzadeh Y (2023) LM-CPPF: paraphrasing-guided data augmentation for contrastive prompt-based few-shot fine-tuning, pp 670\u2013681. https:\/\/doi.org\/10.18653\/V1\/2023.ACL-SHORT.59","DOI":"10.18653\/V1\/2023.ACL-SHORT.59"},{"issue":"6","key":"2180_CR26","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1109\/TBDATA.2023.3310267","volume":"9","author":"X Huang","year":"2023","unstructured":"Huang X, Li J, Wu J, Chang J, Liu D (2023) Transfer learning with document-level data augmentation for aspect-level sentiment classification. IEEE Trans Big Data 9(6):1643\u20131657. https:\/\/doi.org\/10.1109\/TBDATA.2023.3310267","journal-title":"IEEE Trans Big Data"},{"key":"2180_CR27","doi-asserted-by":"publisher","unstructured":"Zhao H, Huang L, Zhang R, Lu Q, Xue H (2020) Spanmlt: a span-based multi-task learning framework for pair-wise aspect and opinion terms extraction, pp 3239\u20133248. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.296","DOI":"10.18653\/v1\/2020.acl-main.296"},{"key":"2180_CR28","doi-asserted-by":"publisher","unstructured":"Chen S, Liu J, Wang Y, Zhang W, Chi Z (2020) Synchronous double-channel recurrent network for aspect-opinion pair extraction, pp 6515\u20136524. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.582","DOI":"10.18653\/v1\/2020.acl-main.582"},{"key":"2180_CR29","doi-asserted-by":"publisher","unstructured":"Chen Z, Qian T (2020) Relation-aware collaborative learning for unified aspect-based sentiment analysis, pp 3685\u20133694. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.340","DOI":"10.18653\/v1\/2020.acl-main.340"},{"key":"2180_CR30","doi-asserted-by":"publisher","unstructured":"Luo H, Ji L, Li T, Jiang D, Duan N (2020) GRACE: gradient harmonized and cascaded labeling for aspect-based sentiment analysis. EMNLP 2020, pp 54\u201364. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.6","DOI":"10.18653\/v1\/2020.findings-emnlp.6"},{"key":"2180_CR31","doi-asserted-by":"publisher","unstructured":"Cai H, Tu Y, Zhou X, Yu J, Xia R (2020) Aspect-category based sentiment analysis with hierarchical graph convolutional network, pp 833\u2013843. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.72","DOI":"10.18653\/v1\/2020.coling-main.72"},{"key":"2180_CR32","doi-asserted-by":"publisher","unstructured":"Li Y, Yang Z, Yin C, Pan X, Cui L, Huang Q, Wei T (2020) A joint model for aspect-category sentiment analysis with shared sentiment prediction layer 12522:388\u2013400. https:\/\/doi.org\/10.1007\/978-3-030-63031-7_28","DOI":"10.1007\/978-3-030-63031-7_28"},{"key":"2180_CR33","doi-asserted-by":"publisher","unstructured":"Liu J, Teng Z, Cui L, Liu H, Zhang Y (2021) Solving aspect category sentiment analysis as a text generation task, pp 4406\u20134416. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.361","DOI":"10.18653\/v1\/2021.emnlp-main.361"},{"key":"2180_CR34","doi-asserted-by":"publisher","unstructured":"Li R, Chen H, Feng F, Ma Z, Wang X, Hovy EH (2021) Dual graph convolutional networks for aspect-based sentiment analysis, pp 6319\u20136329. https:\/\/doi.org\/10.18653\/V1\/2021.ACL-LONG.494","DOI":"10.18653\/V1\/2021.ACL-LONG.494"},{"issue":"10","key":"2180_CR35","doi-asserted-by":"publisher","first-page":"10098","DOI":"10.1109\/TKDE.2023.3250499","volume":"35","author":"Q Zhong","year":"2023","unstructured":"Zhong Q, Ding L, Liu J, Du B, Jin H, Tao D (2023) Knowledge graph augmented network towards multiview representation learning for aspect-based sentiment analysis. IEEE Trans Knowl Data Eng 35(10):10098\u201310111. https:\/\/doi.org\/10.1109\/TKDE.2023.3250499","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2180_CR36","doi-asserted-by":"publisher","unstructured":"Longpre S, Wang Y, DuBois C (2020) How effective is task-agnostic data augmentation for pretrained transformers? EMNLP 2020, pp 4401\u20134411. https:\/\/doi.org\/10.18653\/V1\/2020.FINDINGS-EMNLP.394","DOI":"10.18653\/V1\/2020.FINDINGS-EMNLP.394"},{"issue":"11","key":"2180_CR37","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Haffner BYP (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"2180_CR38","unstructured":"Coulombe C (2018) Text data augmentation made simple by leveraging NLP cloud apis. arXiv:1812.04718"},{"key":"2180_CR39","unstructured":"Belinkov Y, Bisk Y (2018) Synthetic and natural noise both break neural machine translation. Int Conf Learn Represent. https:\/\/openreview.net\/forum?id=BJ8vJebC-"},{"key":"2180_CR40","doi-asserted-by":"crossref","unstructured":"Feng SY, Gangal V, Kang D, Mitamura T, Hovy EH (2020) Genaug: data augmentation for finetuning text generators. arXiv:2010.01794","DOI":"10.18653\/v1\/2020.deelio-1.4"},{"key":"2180_CR41","doi-asserted-by":"publisher","unstructured":"Wang WY, Yang D (2015) That\u2019s so annoying!!!: a lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using #petpeeve tweets, pp 2557\u20132563. https:\/\/doi.org\/10.18653\/V1\/D15-1306","DOI":"10.18653\/V1\/D15-1306"},{"key":"2180_CR42","doi-asserted-by":"publisher","unstructured":"Marivate V, Sefara T (2020) Improving short text classification through global augmentation methods 12279:385\u2013399. https:\/\/doi.org\/10.1007\/978-3-030-57321-8_21","DOI":"10.1007\/978-3-030-57321-8_21"},{"key":"2180_CR43","doi-asserted-by":"publisher","unstructured":"Rizos G, Hemker K, Schuller BW (2019) Augment to prevent: short-text data augmentation in deep learning for hate-speech classification, pp 991\u20131000. https:\/\/doi.org\/10.1145\/3357384.3358040","DOI":"10.1145\/3357384.3358040"},{"key":"2180_CR44","doi-asserted-by":"publisher","unstructured":"Huong TH, Hoang VTA (2020) Data augmentation technique based on text for vietnamese sentiment analysis, pp 13\u20131135. https:\/\/doi.org\/10.1145\/3406601.3406618","DOI":"10.1145\/3406601.3406618"},{"key":"2180_CR45","doi-asserted-by":"publisher","unstructured":"Wu X, Lv S, Zang L, Han J, Hu S (2019) Conditional BERT contextual augmentation 11539, pp 84\u201395. https:\/\/doi.org\/10.1007\/978-3-030-22747-0_7","DOI":"10.1007\/978-3-030-22747-0_7"},{"key":"2180_CR46","unstructured":"Hu Z, Tan B, Salakhutdinov R, Mitchell TM, Xing EP (2019) Learning data manipulation for augmentation and weighting, pp 15738\u201315749"},{"key":"2180_CR47","doi-asserted-by":"publisher","unstructured":"Qu Y, Shen D, Shen Y, Sajeev S, Chen W, Han J (2021) Coda: contrast-enhanced and diversity-promoting data augmentation for natural language understanding. The International Conference on Learning Representations (ICLR). https:\/\/doi.org\/10.48550\/arXiv.2010.08670. https:\/\/openreview.net\/forum?id=Ozk9MrX1hvA","DOI":"10.48550\/arXiv.2010.08670"},{"key":"2180_CR48","doi-asserted-by":"publisher","unstructured":"Anaby-Tavor A, Carmeli B, Goldbraich E, Kantor A, Kour G, Shlomov S, Tepper N, Zwerdling N (2020) Do not have enough data? deep learning to the rescue!, pp 7383\u20137390. https:\/\/doi.org\/10.1609\/AAAI.V34I05.6233","DOI":"10.1609\/AAAI.V34I05.6233"},{"key":"2180_CR49","unstructured":"Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler D.M, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D (2020) Language models are few-shot learners"},{"key":"2180_CR50","doi-asserted-by":"publisher","unstructured":"Schick T, Sch\u00fctze H (2021) Exploiting cloze-questions for few-shot text classification and natural language inference, pp 255\u2013269. https:\/\/doi.org\/10.18653\/v1\/2021.eacl-main.20","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"2180_CR51","doi-asserted-by":"publisher","unstructured":"Shin T, Razeghi Y, IV RLL, Wallace E, Singh S (2020) Autoprompt: eliciting knowledge from language models with automatically generated prompts, pp 4222\u20134235. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.346","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"2180_CR52","unstructured":"Liu X, Zheng Y, Du Z, Ding M, Qian Y, Yang Z, Tang J GPT understands, too. CoRR abs\/2103.10385 (2021) arXiv:2103.10385"},{"key":"2180_CR53","doi-asserted-by":"publisher","unstructured":"Gu Y, Han X, Liu Z, Huang M (2022) PPT: pre-trained prompt tuning for few-shot learning, pp 8410\u20138423. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.576","DOI":"10.18653\/v1\/2022.acl-long.576"},{"issue":"1","key":"2180_CR54","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/S13042-022-01535-5","volume":"14","author":"G Li","year":"2023","unstructured":"Li G, Wang H, Ding Y, Yan ZKX (2023) Data augmentation for aspect-based sentiment analysis. Int J Mach Learn Cybern 14(1):125\u2013133. https:\/\/doi.org\/10.1007\/S13042-022-01535-5","journal-title":"Int J Mach Learn Cybern"},{"key":"2180_CR55","unstructured":"Wang X, Wei J, Schuurmans D, Le QV, Chi EH, Narang S, Chowdhery A, Zhou D (2023) Self-consistency improves chain of thought reasoning in language models. The International Conference on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=1PL1NIMMrw"},{"key":"2180_CR56","doi-asserted-by":"publisher","unstructured":"Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) Semeval-2014 task 4: aspect based sentiment analysis, pp 27\u201335. https:\/\/doi.org\/10.3115\/v1\/s14-2004","DOI":"10.3115\/v1\/s14-2004"},{"key":"2180_CR57","doi-asserted-by":"publisher","unstructured":"Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, Al-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, Clercq OD, Hoste V, Apidianaki M, Tannier X, Loukachevitch N.V, Kotelnikov E.V, Bel N, Zafra S.M.J, Eryigit G (2016) Semeval-2016 task 5: Aspect based sentiment analysis, pp 19\u201330. https:\/\/doi.org\/10.18653\/v1\/s16-1002","DOI":"10.18653\/v1\/s16-1002"},{"key":"2180_CR58","doi-asserted-by":"publisher","unstructured":"Zhang C, Li Q, Song D (2019) Aspect-based sentiment classification with aspect-specific graph convolutional networks, pp 4567\u20134577. https:\/\/doi.org\/10.18653\/v1\/D19-1464","DOI":"10.18653\/v1\/D19-1464"},{"key":"2180_CR59","doi-asserted-by":"publisher","unstructured":"Liu Q, Zhang H, Zeng Y, Huang Z, Wu Z (2018) Content attention model for aspect based sentiment analysis, pp 1023\u20131032. https:\/\/doi.org\/10.1145\/3178876.3186001","DOI":"10.1145\/3178876.3186001"},{"issue":"8","key":"2180_CR60","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 (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"2180_CR61","doi-asserted-by":"publisher","unstructured":"Wang K, Shen W, Yang Y, Quan X, Wang R (2020) Relational graph attention network for aspect-based sentiment analysis, pp 3229\u20133238 .https:\/\/doi.org\/10.18653\/V1\/2020.ACL-MAIN.295","DOI":"10.18653\/V1\/2020.ACL-MAIN.295"},{"key":"2180_CR62","doi-asserted-by":"publisher","unstructured":"Morris JX, Lifland E, Yoo JY, Grigsby J, Jin D, Qi Y (2020) Textattack: a framework for adversarial attacks, data augmentation, and adversarial training in NLP, pp 119\u2013126. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-demos.16","DOI":"10.18653\/v1\/2020.emnlp-demos.16"},{"key":"2180_CR63","unstructured":"Mosbach M, Andriushchenko M, Klakow D (2021) On the stability of fine-tuning BERT: misconceptions, explanations, and strong baselines. The International Conference on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=nzpLWnVAyah"},{"key":"2180_CR64","doi-asserted-by":"publisher","first-page":"2591","DOI":"10.1007\/s13042-023-01784-y","volume":"14","author":"X Liu","year":"2023","unstructured":"Liu X, Zhong Y, Wang J, Li P (2023) Data augmentation using heuristic masked language modeling. Int J Mach Learn Cybernet 14:2591\u201326050","journal-title":"Int J Mach Learn Cybernet"},{"issue":"1","key":"2180_CR65","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s11280-021-00978-0","volume":"25","author":"Z Chen","year":"2022","unstructured":"Chen Z, Qian T (2022) Description and demonstration guided data augmentation for sequence tagging. World Wide Web 25(1):175\u2013194. https:\/\/doi.org\/10.1007\/s11280-021-00978-0","journal-title":"World Wide Web"},{"key":"2180_CR66","first-page":"271","volume":"1","author":"O Kolomiyets","year":"2011","unstructured":"Kolomiyets O, Bethard S, Moens M (2011) Model-portability experiments for textual temporal analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, Assoc Comput Linguist. Portland, Oregon, USA, 271\u2013276","journal-title":"Computational Linguistics."}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02180-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02180-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02180-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T07:40:53Z","timestamp":1737531653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02180-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,23]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["2180"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02180-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,23]]},"assertion":[{"value":"11 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}