{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:08:25Z","timestamp":1775326105231,"version":"3.50.1"},"reference-count":305,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2021YFB1714800"],"award-info":[{"award-number":["2021YFB1714800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["U20B2053 and 61872022"],"award-info":[{"award-number":["U20B2053 and 61872022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100011347","name":"State Key Laboratory of Software Development Environment","doi-asserted-by":"crossref","award":["SKLSDE-2020ZX-12"],"award-info":[{"award-number":["SKLSDE-2020ZX-12"]}],"id":[{"id":"10.13039\/501100011347","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NSF","award":["III-1763325, III-1909323, III-2106758, and SaTC-1930941"],"award-info":[{"award-number":["III-1763325, III-1909323, III-2106758, and SaTC-1930941"]}]},{"name":"NSF ONR","award":["N00014-18-1-2009"],"award-info":[{"award-number":["N00014-18-1-2009"]}]},{"name":"Lehigh\u2019s accelerator","award":["S00010293"],"award-info":[{"award-number":["S00010293"]}]},{"name":"CAAI-Huawei MindSpore Open Fund"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.<\/jats:p>","DOI":"10.1145\/3495162","type":"journal-article","created":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T10:13:32Z","timestamp":1649412812000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":313,"title":["A Survey on Text Classification: From Traditional to Deep Learning"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1612-4644","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"first","affiliation":[{"name":"Beihang University, Haidian district, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7422-630X","authenticated-orcid":false,"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"Beihang University, Haidian district, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-0055","authenticated-orcid":false,"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University, Haidian district, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7581-0882","authenticated-orcid":false,"given":"Congying","family":"Xia","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6334-4925","authenticated-orcid":false,"given":"Renyu","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Leeds, Leeds, England, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1539-7939","authenticated-orcid":false,"given":"Lichao","family":"Sun","sequence":"additional","affiliation":[{"name":"Lehigh University, Bethlehem, PA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7810-9071","authenticated-orcid":false,"given":"Lifang","family":"He","sequence":"additional","affiliation":[{"name":"Lehigh University, Bethlehem, PA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/p15-1150"},{"key":"e_1_3_2_3_2","first-page":"1604","volume-title":"Proc. ICML, 2015","author":"Zhu Xiao-Dan","year":"2015","unstructured":"Xiao-Dan Zhu, Parinaz Sobhani, and Hongyu Guo. 2015. Long short-term memory over recursive structures. In Proc. ICML, 2015. 1604\u20131612. http:\/\/proceedings.mlr.press\/v37\/zhub15.html."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01606-1"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.07.048"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/p14-1062"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d15-1280"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n16-1062"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/321075.321084"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0026683"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-2045"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/info10040150"},{"key":"e_1_3_2_14_2","article-title":"Deep learning based text classification: A comprehensive review","volume":"2004","author":"Minaee Shervin","year":"2020","unstructured":"Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. 2020. Deep learning based text classification: A comprehensive review. CoRR abs\/2004.03705 (2020). arxiv:2004.03705https:\/\/arxiv.org\/abs\/2004.03705.","journal-title":"CoRR"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_17_2","first-page":"3146","volume-title":"Proc. NeurIPS, 2017","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A highly efficient gradient boosting decision tree. In Proc. NeurIPS, 2017. 3146\u20133154. http:\/\/papers.nips.cc\/paper\/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/d14-1181"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"e_1_3_2_20_2","first-page":"4171","volume-title":"Proc. NAACL, 2019","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proc. NAACL, 2019. 4171\u20134186. https:\/\/www.aclweb.org\/anthology\/N19-1423\/."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-010-0001-0"},{"key":"e_1_3_2_22_2","volume-title":"Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval","volume":"161175","author":"Cavnar William B.","year":"1994","unstructured":"William B. Cavnar, John M. Trenkle, et\u00a0al. 1994. N-gram-based text categorization. In Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, Vol. 161175. Citeseer."},{"key":"e_1_3_2_23_2","unstructured":"Ricardo Baeza-Yates and Berthier Ribeiro-Neto. 1999. Modern information retrieval. ACM press Vol. 463."},{"key":"e_1_3_2_24_2","volume-title":"Proc. ICLR, 2013","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Proc. ICLR, 2013. http:\/\/arxiv.org\/abs\/1301.3781."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/d14-1162"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835930"},{"key":"e_1_3_2_27_2","volume-title":"Proc. ACL, 2004","author":"Schneider Karl-Michael","year":"2004","unstructured":"Karl-Michael Schneider. 2004. A new feature selection score for multinomial Na\u00efve Bayes text classification based on KL-divergence. In Proc. ACL, 2004. https:\/\/www.aclweb.org\/anthology\/P04-3024\/."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.5555\/1146355"},{"key":"e_1_3_2_29_2","first-page":"540","volume-title":"Proc. AAAI, 2007","author":"Dai Wenyuan","year":"2007","unstructured":"Wenyuan Dai, Gui-Rong Xue, Qiang Yang, and Yong Yu. 2007. Transferring Na\u00efve Bayes classifiers for text classification. In Proc. AAAI, 2007. 540\u2013545. http:\/\/www.aaai.org\/Library\/AAAI\/2007\/aaai07-085.php."},{"key":"e_1_3_2_30_2","article-title":"Maximum likelihood from incomplete data via the EM algorithm","year":"1977","unstructured":"A.P.Dempster, N.M.Laird, and D. B.Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society (1977).","journal-title":"Journal of the Royal Statistical Society"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/UKRCON.2017.8100379"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.4994463"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551516677946"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICACTM.2019.8776800"},{"key":"e_1_3_2_35_2","unstructured":"2007. 20NG Corpus. http:\/\/ana.cachopo.org\/datasets-for-single-label-text-categorization. (2007)."},{"key":"e_1_3_2_36_2","first-page":"509","volume-title":"Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA","author":"Craven Mark","year":"1998","unstructured":"Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, and Se\u00e1n Slattery. 1998. Learning to extract symbolic knowledge from the World Wide Web. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA. 509\u2013516. http:\/\/www.aaai.org\/Library\/AAAI\/1998\/aaai98-072.php."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCCEE.2017.7866706"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/1039621.1039623"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/108\/5\/052074"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.08.040"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2001.989592"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2004.12.023"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1142\/9789812799623_0053"},{"key":"e_1_3_2_45_2","first-page":"480","volume-title":"AAAI\/IAAI","author":"Taira Hirotoshi","year":"1999","unstructured":"Hirotoshi Taira and Masahiko Haruno. 1999. Feature selection in SVM text categorization. In AAAI\/IAAI. 480\u2013486."},{"key":"e_1_3_2_46_2","first-page":"1479","volume-title":"IJCAI","author":"Li Xin","year":"2013","unstructured":"Xin Li and Yuhong Guo. 2013. Active learning with multi-label SVM classification. In IJCAI. Citeseer, 1479\u20131485."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.5555\/3227211.3227330"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/383952.383974"},{"key":"e_1_3_2_49_2","volume-title":"International Conference on Machine Learning","author":"Joachims T.","year":"1999","unstructured":"T. Joachims. 1999. Transductive inference for text classification using support vector machines. In International Conference on Machine Learning."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.5555\/541177"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009887311454"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116251"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.5555\/152181"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/RIDE.1997.583715"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1147\/sj.413.0428"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.94"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-59119-2_166"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007614523901"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10160-6_26"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357891"},{"key":"e_1_3_2_61_2","first-page":"151","volume-title":"Proc. EMNLP, 2011","author":"Socher Richard","year":"2011","unstructured":"Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proc. EMNLP, 2011. 151\u2013161. https:\/\/www.aclweb.org\/anthology\/D11-1014\/."},{"key":"e_1_3_2_62_2","unstructured":"2011. A MATLAB Implementation of RAE. https:\/\/github.com\/vin00\/Semi-Supervised-Recursive-Autoencoders-for-Predicting-Sentiment-Distributions. (2011)."},{"key":"e_1_3_2_63_2","first-page":"1201","volume-title":"Proc. EMNLP, 2012","author":"Socher Richard","year":"2012","unstructured":"Richard Socher, Brody Huval, Christopher D. Manning, and Andrew Y. Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In Proc. EMNLP, 2012. 1201\u20131211. https:\/\/www.aclweb.org\/anthology\/D12-1110\/."},{"key":"e_1_3_2_64_2","unstructured":"https:\/\/github.com\/github-pengge\/MV_RNN 2012 A Tensorflow Implementation of MV_RNN"},{"key":"e_1_3_2_65_2","first-page":"1631","volume-title":"Proc. EMNLP, 2013","author":"Socher Richard","year":"2013","unstructured":"Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proc. EMNLP, 2013. 1631\u20131642. https:\/\/www.aclweb.org\/anthology\/D13-1170\/."},{"key":"e_1_3_2_66_2","unstructured":"https:\/\/github.com\/pondruska\/DeepSentiment 2013 A MATLAB Implementation of RNTN"},{"key":"e_1_3_2_67_2","first-page":"2096","volume-title":"Proc. NIPS, 2014","author":"Irsoy Ozan","year":"2014","unstructured":"Ozan Irsoy and Claire Cardie. 2014. Deep recursive neural networks for compositionality in language. In Proc. NIPS, 2014. 2096\u20132104. http:\/\/papers.nips.cc\/paper\/5551-deep-recursive-neural-networks-for-compositionality-in-language."},{"key":"e_1_3_2_68_2","first-page":"1188","volume-title":"Proc. ICML, 2014","author":"Le Quoc V.","year":"2014","unstructured":"Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proc. ICML, 2014. 1188\u20131196. http:\/\/proceedings.mlr.press\/v32\/le14.html."},{"key":"e_1_3_2_69_2","unstructured":"2014. A PyTorch Implementation of Paragraph Vectors (doc2vec). https:\/\/github.com\/inejc\/paragraph-vectors. (2014)."},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/p15-1162"},{"key":"e_1_3_2_71_2","unstructured":"2015. An Implementation of DAN. https:\/\/github.com\/miyyer\/dan. (2015)."},{"key":"e_1_3_2_72_2","unstructured":"2015. A PyTorch Implementation of Tree-LSTM. https:\/\/github.com\/stanfordnlp\/treelstm. (2015)."},{"key":"e_1_3_2_73_2","doi-asserted-by":"crossref","unstructured":"Siwei Lai Liheng Xu Kang Liu and Jun Zhao. 2015. Recurrent convolutional neural networks for text classification(AAAI\u201915). AAAI Press 2267\u20132273.","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"e_1_3_2_74_2","unstructured":"2015. A Tensorflow Implementation of TextRCNN. https:\/\/github.com\/roomylee\/rcnn-text-classification. (2015)."},{"key":"e_1_3_2_75_2","unstructured":"2015. An Implementation of MT-LSTM. https:\/\/github.com\/AlexAntn\/MTLSTM. (2015)."},{"key":"e_1_3_2_76_2","first-page":"526","volume-title":"Proc. ICML, 2016","author":"Johnson Rie","year":"2016","unstructured":"Rie Johnson and Tong Zhang. 2016. Supervised and semi-supervised text categorization using LSTM for region embeddings. In Proc. ICML, 2016. 526\u2013534. http:\/\/proceedings.mlr.press\/v48\/johnson16.html."},{"key":"e_1_3_2_77_2","unstructured":"2015. An Implementation of oh-2LSTMp. http:\/\/riejohnson.com\/cnn_20download.html. (2015)."},{"key":"e_1_3_2_78_2","first-page":"3485","volume-title":"Proc. COLING, 2016","author":"Zhou Peng","year":"2016","unstructured":"Peng Zhou, Zhenyu Qi, Suncong Zheng, Jiaming Xu, Hongyun Bao, and Bo Xu. 2016. Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. In Proc. COLING, 2016. 3485\u20133495. https:\/\/www.aclweb.org\/anthology\/C16-1329\/."},{"key":"e_1_3_2_79_2","unstructured":"2016. An Implementation of BLSTM-2DCNN. https:\/\/github.com\/ManuelVs\/NNForTextClassification. (2016)."},{"key":"e_1_3_2_80_2","first-page":"2873","volume-title":"Proc. IJCAI, 2016","author":"Liu Pengfei","year":"2016","unstructured":"Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Recurrent neural network for text classification with multi-task learning. In Proc. IJCAI, 2016. 2873\u20132879. http:\/\/www.ijcai.org\/Abstract\/16\/408."},{"key":"e_1_3_2_81_2","unstructured":"2016. A PyTorch Implementation of Multi-Task. https:\/\/github.com\/baixl\/text_classification. (2016)."},{"key":"e_1_3_2_82_2","first-page":"1615","volume-title":"Proc. EMNLP, 2017","author":"Felbo Bjarke","year":"2017","unstructured":"Bjarke Felbo, Alan Mislove, Anders S\u00f8gaard, Iyad Rahwan, and Sune Lehmann. 2017. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In Proc. EMNLP, 2017. 1615\u20131625. https:\/\/www.aclweb.org\/anthology\/D17-1169\/."},{"key":"e_1_3_2_83_2","unstructured":"https:\/\/github.com\/bfelbo\/DeepMoji 2018 A Keras Implementation of DeepMoji"},{"key":"e_1_3_2_84_2","volume-title":"Proc. ICLR, 2017","author":"Dieng Adji B.","year":"2017","unstructured":"Adji B. Dieng, Chong Wang, Jianfeng Gao, and John W. Paisley. 2017. TopicRNN: A recurrent neural network with long-range semantic dependency. In Proc. ICLR, 2017. https:\/\/openreview.net\/forum?id=rJbbOLcex."},{"key":"e_1_3_2_85_2","unstructured":"2017. A PyTorch Implementation of TopicRNN. https:\/\/github.com\/dangitstam\/topic-rnn. (2017)."},{"key":"e_1_3_2_86_2","volume-title":"Proc. ICLR, 2017","author":"Miyato Takeru","year":"2017","unstructured":"Takeru Miyato, Andrew M. Dai, and Ian J. Goodfellow. 2017. Adversarial training methods for semi-supervised text classification. In Proc. ICLR, 2017. https:\/\/openreview.net\/forum?id=r1X3g2_xl."},{"key":"e_1_3_2_87_2","unstructured":"2017. A Tensorflow Implementation of Virtual Adversarial Training. https:\/\/github.com\/tensorflow\/models\/tree\/master\/adversarial_text. (2017)."},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186015"},{"key":"e_1_3_2_89_2","unstructured":"2018. A PyTorch Implementation of RNN-Capsule. https:\/\/github.com\/wangjiosw\/Sentiment-Analysis-by-Capsules. (2018)."},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/757"},{"key":"e_1_3_2_91_2","unstructured":"2019. An Implementation of HM-DenseRNNs. https:\/\/github.com\/zhaoyizhaoyi\/hm-densernns. (2019)."},{"key":"e_1_3_2_92_2","unstructured":"2014. A Keras Implementation of TextCNN. https:\/\/github.com\/alexander-rakhlin\/CNN-for-Sentence-Classification-in-Keras. (2014)."},{"key":"e_1_3_2_93_2","unstructured":"2014. A Tensorflow Implementation of DCNN. https:\/\/github.com\/kinimod23\/ATS_Project. (2014)."},{"key":"e_1_3_2_94_2","first-page":"649","volume-title":"Proc. NeurIPS, 2015","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Proc. NeurIPS, 2015. 649\u2013657. http:\/\/papers.nips.cc\/paper\/5782-character-level-convolutional-networks-for-text-classification."},{"key":"e_1_3_2_95_2","unstructured":"2015. A Tensorflow Implementation of CharCNN. https:\/\/github.com\/mhjabreel\/CharCNN. (2015)."},{"key":"e_1_3_2_96_2","unstructured":"2016. A Keras Implementation of SeqTextRCNN. https:\/\/github.com\/ilimugur\/short-text-classification. (2016)."},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080834"},{"key":"e_1_3_2_98_2","unstructured":"2017. A Pytorch Implementation of XML-CNN. https:\/\/github.com\/siddsax\/XML-CNN. (2017)."},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1052"},{"key":"e_1_3_2_100_2","unstructured":"2017. A PyTorch Implementation of DPCNN. https:\/\/github.com\/Cheneng\/DPCNN. (2017)."},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/406"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1350"},{"key":"e_1_3_2_103_2","unstructured":"2018. A Tensorflow Implementation of TextCapsule. https:\/\/github.com\/andyweizhao\/capsule_text_classification. (2018)."},{"key":"e_1_3_2_104_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1093"},{"key":"e_1_3_2_105_2","unstructured":"2018. An Implementation of HFT-CNN. https:\/\/github.com\/ShimShim46\/HFT-CNN. (2018)."},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.330110067"},{"key":"e_1_3_2_107_2","volume-title":"Proc. ICLR, 2020","author":"Bao Yujia","year":"2020","unstructured":"Yujia Bao, Menghua Wu, Shiyu Chang, and Regina Barzilay. 2020. Few-shot text classification with distributional signatures. In Proc. ICLR, 2020. https:\/\/openreview.net\/forum?id=H1emfT4twB."},{"key":"e_1_3_2_108_2","unstructured":"2020. A PyTorch Implementation of Few-shot Text Classification with Distributional Signatures. https:\/\/github.com\/YujiaBao\/Distributional-Signatures. (2020)."},{"key":"e_1_3_2_109_2","first-page":"1480","volume-title":"Proc. NAACL, 2016","author":"Yang Zichao","year":"2016","unstructured":"Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alexander J. Smola, and Eduard H. Hovy. 2016. Hierarchical attention networks for document classification. In Proc. NAACL, 2016. 1480\u20131489. https:\/\/www.aclweb.org\/anthology\/N16-1174\/."},{"key":"e_1_3_2_110_2","unstructured":"2014. A Keras Implementation of TextCNN. https:\/\/github.com\/richliao\/textClassifier. (2014)."},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d16-1024"},{"key":"e_1_3_2_112_2","unstructured":"2013. NLP&CC Corpus. http:\/\/tcci.ccf.org.cn\/conference\/2013\/index.html. (2013)."},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d16-1053"},{"key":"e_1_3_2_114_2","unstructured":"2016. A Tensorflow Implementation of LSTMN. https:\/\/github.com\/JRC1995\/Abstractive-Summarization. (2016)."},{"key":"e_1_3_2_115_2","volume-title":"Proc. ICLR, 2017","author":"Lin Zhouhan","year":"2017","unstructured":"Zhouhan Lin, Minwei Feng, C\u00edcero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. In Proc. ICLR, 2017. https:\/\/openreview.net\/forum?id=BJC_jUqxe."},{"key":"e_1_3_2_116_2","unstructured":"2017. A PyTorch Implementation of Structured-Self-Attention. https:\/\/github.com\/kaushalshetty\/Structured-Self-Attention. (2017)."},{"key":"e_1_3_2_117_2","first-page":"3915","volume-title":"Proc. COLING, 2018","author":"Yang Pengcheng","year":"2018","unstructured":"Pengcheng Yang, Xu Sun, Wei Li, Shuming Ma, Wei Wu, and Houfeng Wang. 2018. SGM: Sequence generation model for multi-label classification. In Proc. COLING, 2018. 3915\u20133926. https:\/\/www.aclweb.org\/anthology\/C18-1330\/."},{"key":"e_1_3_2_118_2","unstructured":"2018. A PyTorch Implementation of SGM. https:\/\/github.com\/lancopku\/SGM. (2018)."},{"key":"e_1_3_2_119_2","first-page":"2227","volume-title":"Proc. NAACL, 2018","author":"Peters Matthew E.","year":"2018","unstructured":"Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proc. NAACL, 2018. 2227\u20132237. https:\/\/www.aclweb.org\/anthology\/N18-1202\/."},{"key":"e_1_3_2_120_2","unstructured":"2018. A PyTorch Implementation of ELMo. https:\/\/github.com\/flairNLP\/flair. (2018)."},{"key":"e_1_3_2_121_2","volume-title":"Proc. ICLR, 2018","author":"Shen Tao","year":"2018","unstructured":"Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang. 2018. Bi-directional block self-attention for fast and memory-efficient sequence modeling. In Proc. ICLR, 2018. https:\/\/openreview.net\/forum?id=H1cWzoxA-."},{"key":"e_1_3_2_122_2","unstructured":"2018. A PyTorch Implementation of BiBloSA. https:\/\/github.com\/galsang\/BiBloSA-pytorch. (2018)."},{"key":"e_1_3_2_123_2","first-page":"5812","volume-title":"Proc. NeurIPS, 2019","author":"You Ronghui","year":"2019","unstructured":"Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, and Shanfeng Zhu. 2019. AttentionXML: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification. In Proc. NeurIPS, 2019. 5812\u20135822. http:\/\/papers.nips.cc\/paper\/8817-attentionxml-label-tree-based-attention-aware-deep-model-for-high-performance-extreme-multi-label-text-classification."},{"key":"e_1_3_2_124_2","unstructured":"2019. A PyTorch Implementation of AttentionXML. https:\/\/github.com\/yourh\/AttentionXML. (2019)."},{"key":"e_1_3_2_125_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1045"},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016407"},{"key":"e_1_3_2_127_2","unstructured":"2019. A PyTorch Implementation of HATT-Proto. https:\/\/github.com\/thunlp\/HATT-Proto. (2019)."},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016252"},{"key":"e_1_3_2_129_2","unstructured":"2019. A PyTorch Implementation of STCKA. https:\/\/github.com\/AIRobotZhang\/STCKA. (2019)."},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.399"},{"key":"e_1_3_2_131_2","unstructured":"2020. A Pytorch Implementation of HyperGAT. https:\/\/github.com\/kaize0409\/HyperGAT. (2020)."},{"key":"e_1_3_2_132_2","first-page":"7847","volume-title":"The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020","author":"Guo Qipeng","year":"2020","unstructured":"Qipeng Guo, Xipeng Qiu, Pengfei Liu, Xiangyang Xue, and Zheng Zhang. 2020. Multi-scale self-attention for text classification. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. 7847\u20137854. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6290."},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.543"},{"key":"e_1_3_2_134_2","unstructured":"2019. A Tensorflow Implementation of BERT. https:\/\/github.com\/google-research\/bert. (2019)."},{"key":"e_1_3_2_135_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1636"},{"key":"e_1_3_2_136_2","unstructured":"2019. A Tensorflow Implementation of BERT-BASE. https:\/\/github.com\/iliaschalkidis\/lmtc-eurlex57k. (2019)."},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32381-3_16"},{"key":"e_1_3_2_138_2","unstructured":"2019. A Tensorflow Implementation of BERT4doc-Classification. https:\/\/github.com\/xuyige\/BERT4doc-Classification. (2019)."},{"key":"e_1_3_2_139_2","first-page":"5754","volume-title":"Proc. NeurIPS, 2019","author":"Yang Zhilin","year":"2019","unstructured":"Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019. XLNet: Generalized autoregressive pretraining for language understanding. In Proc. NeurIPS, 2019. 5754\u20135764. http:\/\/papers.nips.cc\/paper\/8812-xlnet-generalized-autoregressive-pretraining-for-language-understanding."},{"key":"e_1_3_2_140_2","unstructured":"2019. A Tensorflow Implementation of XLNet. https:\/\/github.com\/zihangdai\/xlnet. (2019)."},{"key":"e_1_3_2_141_2","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","volume":"1907","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019). arxiv:1907.11692http:\/\/arxiv.org\/abs\/1907.11692.","journal-title":"CoRR"},{"key":"e_1_3_2_142_2","unstructured":"2019. A PyTorch Implementation of RoBERTa. https:\/\/github.com\/pytorch\/fairseq. (2019)."},{"key":"e_1_3_2_143_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.191"},{"key":"e_1_3_2_144_2","unstructured":"2020. A Pytorch Implementation of GAN-BERT. https:\/\/github.com\/crux82\/ganbert. (2020)."},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.498"},{"key":"e_1_3_2_146_2","unstructured":"2020. An Implementation of BAE. https:\/\/github.com\/QData\/TextAttack\/blob\/master\/textattack\/attack_recipes\/bae_garg_2019.py. (2020)."},{"key":"e_1_3_2_147_2","volume-title":"Proc. ICLR, 2020","author":"Lan Zhenzhong","year":"2020","unstructured":"Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. ALBERT: A lite BERT for self-supervised learning of language representations. In Proc. ICLR, 2020. https:\/\/openreview.net\/forum?id=H1eA7AEtvS."},{"key":"e_1_3_2_148_2","unstructured":"2020. A Tensorflow Implementation of ALBERT. https:\/\/github.com\/google-research\/ALBERT. (2020)."},{"key":"e_1_3_2_149_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.668"},{"key":"e_1_3_2_150_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403368"},{"key":"e_1_3_2_151_2","unstructured":"2020. An Implementation of X-Transformer. https:\/\/github.com\/OctoberChang\/X-Transformer. (2020)."},{"key":"e_1_3_2_152_2","article-title":"LightXML: Transformer with dynamic negative sampling for high-performance extreme multi-label text classification","volume":"2101","author":"Jiang Ting","year":"2021","unstructured":"Ting Jiang, Deqing Wang, Leilei Sun, Huayi Yang, Zhengyang Zhao, and Fuzhen Zhuang. 2021. LightXML: Transformer with dynamic negative sampling for high-performance extreme multi-label text classification. CoRR abs\/2101.03305 (2021). arxiv:2101.03305https:\/\/arxiv.org\/abs\/2101.03305.","journal-title":"CoRR"},{"key":"e_1_3_2_153_2","unstructured":"2021. An Implementation of LightXML. https:\/\/github.com\/kongds\/LightXML. (2021)."},{"key":"e_1_3_2_154_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186005"},{"key":"e_1_3_2_155_2","unstructured":"2018. A Tensorflow Implementation of DeepGraphCNNforTexts. https:\/\/github.com\/HKUST-KnowComp\/DeepGraphCNNforTexts. (2018)."},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"e_1_3_2_157_2","unstructured":"2019. A Tensorflow Implementation of TextGCN. https:\/\/github.com\/yao8839836\/text_gcn. (2019)."},{"key":"e_1_3_2_158_2","first-page":"6861","volume-title":"Proc. ICML, 2019","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019. Simplifying graph convolutional networks. In Proc. ICML, 2019. 6861\u20136871. http:\/\/proceedings.mlr.press\/v97\/wu19e.html."},{"key":"e_1_3_2_159_2","unstructured":"2019. An Implementation of SGC. https:\/\/github.com\/Tiiiger\/SGC. (2019)."},{"key":"e_1_3_2_160_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1345"},{"key":"e_1_3_2_161_2","unstructured":"2019. An Implementation of TextLevelGNN. https:\/\/github.com\/LindgeW\/TextLevelGNN. (2019)."},{"key":"e_1_3_2_162_2","article-title":"Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification","author":"Peng Hao","year":"2019","unstructured":"Hao Peng, Jianxin Li, Senzhang Wang, Lihong Wang, Qiran Gong, Renyu Yang, Bo Li, Philip Yu, and Lifang He. 2019. Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification. IEEE Transactions on Knowledge and Data Engineering (2019).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_163_2","first-page":"334","volume-title":"Proc. ACL, 2020","author":"Zhang Yufeng","year":"2020","unstructured":"Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, and Liang Wang. 2020. Every document owns its structure: Inductive text classification via graph neural networks. In Proc. ACL, 2020. 334\u2013339. https:\/\/www.aclweb.org\/anthology\/2020.acl-main.31\/."},{"key":"e_1_3_2_164_2","unstructured":"2019. A Tensorflow Implementation of TextING. https:\/\/github.com\/CRIPAC-DIG\/TextING. (2019)."},{"key":"e_1_3_2_165_2","first-page":"8409","volume-title":"The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020","author":"Liu Xien","year":"2020","unstructured":"Xien Liu, Xinxin You, Xiao Zhang, Ji Wu, and Ping Lv. 2020. Tensor graph convolutional networks for text classification. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. 8409\u20138416. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6359."},{"key":"e_1_3_2_166_2","unstructured":"2019. A Tensorflow Implementation of TensorGCN. https:\/\/github.com\/THUMLP\/TensorGCN. (2019)."},{"key":"e_1_3_2_167_2","doi-asserted-by":"publisher","DOI":"10.5220\/0008940304940505"},{"key":"e_1_3_2_168_2","unstructured":"2020. A Repository of MAGNET. https:\/\/github.com\/monk1337\/MAGnet. (2020)."},{"key":"e_1_3_2_169_2","unstructured":"2017. A Tensorflow Implementation of Miyato et\u00a0al.https:\/\/github.com\/TobiasLee\/Text-Classification. (2017)."},{"key":"e_1_3_2_170_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1351"},{"key":"e_1_3_2_171_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n19-1108"},{"key":"e_1_3_2_172_2","unstructured":"2019. A Tensorflow Implementation of KG4ZeroShotText. https:\/\/github.com\/JingqingZ\/KG4ZeroShotText. (2019)."},{"key":"e_1_3_2_173_2","doi-asserted-by":"publisher","DOI":"10.1109\/IADCC.2009.4809024"},{"key":"e_1_3_2_174_2","doi-asserted-by":"publisher","DOI":"10.1145\/3234150"},{"key":"e_1_3_2_175_2","first-page":"8665","volume-title":"The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020","author":"Qin Libo","year":"2020","unstructured":"Libo Qin, Wanxiang Che, Yangming Li, Minheng Ni, and Ting Liu. 2020. DCR-Net: A deep co-interactive relation network for joint dialog act recognition and sentiment classification. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. 8665\u20138672. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6391."},{"key":"e_1_3_2_176_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.260"},{"key":"e_1_3_2_177_2","first-page":"8018","volume-title":"The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020","author":"Jin Di","year":"2020","unstructured":"Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020. Is BERT really robust? A strong baseline for natural language attack on text classification and entailment. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. 8018\u20138025. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6311."},{"key":"e_1_3_2_178_2","volume-title":"IJCAI 2021","author":"Li Chen","year":"2021","unstructured":"Chen Li, Xutan Peng, Hao Peng, Jianxin Li, and Lihong Wang. 2021. TextGTL: Graph-based transductive learning for semi-supervised TextClassification via structure-sensitive interpolation. In IJCAI 2021. ijcai.org."},{"key":"e_1_3_2_179_2","article-title":"Virtual adversarial training: A regularization method for supervised and semi-supervised learning","volume":"1704","author":"Miyato Takeru","year":"2017","unstructured":"Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2017. Virtual adversarial training: A regularization method for supervised and semi-supervised learning. CoRR abs\/1704.03976 (2017). arxiv:1704.03976http:\/\/arxiv.org\/abs\/1704.03976.","journal-title":"CoRR"},{"key":"e_1_3_2_180_2","first-page":"44","volume-title":"Proc. ICANN, 2011","author":"Hinton Geoffrey E.","year":"2011","unstructured":"Geoffrey E. Hinton, Alex Krizhevsky, and Sida D. Wang. 2011. Transforming auto-encoders. In Proc. ICANN, 2011, Timo Honkela, W\u0142odzis\u0142aw Duch, Mark Girolami, and Samuel Kaski (Eds.). Springer Berlin, Berlin, 44\u201351."},{"key":"e_1_3_2_181_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_182_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d15-1075"},{"key":"e_1_3_2_183_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/579"},{"key":"e_1_3_2_184_2","first-page":"919","volume-title":"Proc. NeurIPS, 2015","author":"Johnson Rie","year":"2015","unstructured":"Rie Johnson and Tong Zhang. 2015. Semi-supervised convolutional neural networks for text categorization via region embedding. In Proc. NeurIPS, 2015. 919\u2013927. http:\/\/papers.nips.cc\/paper\/5849-semi-supervised-convolutional-neural-networks-for-text-categorization-via-region-embedding."},{"key":"e_1_3_2_185_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_2_186_2","doi-asserted-by":"publisher","DOI":"10.5555\/936851"},{"key":"e_1_3_2_187_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-8438-6_2"},{"key":"e_1_3_2_188_2","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12317"},{"key":"e_1_3_2_189_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1052"},{"key":"e_1_3_2_190_2","first-page":"151","volume-title":"Proc. IJCNLP, 2017","author":"Xue Wei","year":"2017","unstructured":"Wei Xue, Wubai Zhou, Tao Li, and Qing Wang. 2017. MTNA: A neural multi-task model for aspect category classification and aspect term extraction on restaurant reviews. In Proc. IJCNLP, 2017. 151\u2013156. https:\/\/www.aclweb.org\/anthology\/I17-2026\/."},{"key":"e_1_3_2_191_2","volume-title":"Proc. ICLR, 2015","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proc. ICLR, 2015. http:\/\/arxiv.org\/abs\/1409.0473."},{"key":"e_1_3_2_192_2","first-page":"487","volume-title":"Proc. COLING, 2018","author":"Hu Zikun","year":"2018","unstructured":"Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, and Maosong Sun. 2018. Few-shot charge prediction with discriminative legal attributes. In Proc. COLING, 2018. 487\u2013498. https:\/\/www.aclweb.org\/anthology\/C18-1041\/."},{"key":"e_1_3_2_193_2","first-page":"5998","volume-title":"Proc. NeurIPS, 2017","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proc. NeurIPS, 2017. 5998\u20136008. http:\/\/papers.nips.cc\/paper\/7181-attention-is-all-you-need."},{"key":"e_1_3_2_194_2","first-page":"5876","volume-title":"Proc. AAAI, 2018","author":"Ma Yukun","year":"2018","unstructured":"Yukun Ma, Haiyun Peng, and Erik Cambria. 2018. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In Proc. AAAI, 2018. 5876\u20135883. https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16541."},{"key":"e_1_3_2_195_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d16-1058"},{"key":"e_1_3_2_196_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1380"},{"key":"e_1_3_2_197_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1044"},{"key":"e_1_3_2_198_2","article-title":"Attentive pooling networks","volume":"1602","author":"Santos C\u00edcero Nogueira dos","year":"2016","unstructured":"C\u00edcero Nogueira dos Santos, Ming Tan, Bing Xiang, and Bowen Zhou. 2016. Attentive pooling networks. CoRR abs\/1602.03609 (2016). arxiv:1602.03609http:\/\/arxiv.org\/abs\/1602.03609.","journal-title":"CoRR"},{"key":"e_1_3_2_199_2","article-title":"Pre-trained models for natural language processing: A survey","volume":"2003","author":"Qiu Xipeng","year":"2020","unstructured":"Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020. Pre-trained models for natural language processing: A survey. CoRR abs\/2003.08271 (2020). arXiv:2003.08271https:\/\/arxiv.org\/abs\/2003.08271.","journal-title":"CoRR"},{"key":"e_1_3_2_200_2","unstructured":"Alec Radford. 2018. Improving language understanding by generative pre-training."},{"key":"e_1_3_2_201_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1285"},{"key":"e_1_3_2_202_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1356"},{"key":"e_1_3_2_203_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"e_1_3_2_204_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00300"},{"key":"e_1_3_2_205_2","article-title":"ERNIE: Enhanced representation through knowledge integration","volume":"1904","author":"Sun Yu","year":"2019","unstructured":"Yu Sun, Shuohuan Wang, Yu-Kun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019. ERNIE: Enhanced representation through knowledge integration. CoRR abs\/1904.09223 (2019). arxiv:1904.09223http:\/\/arxiv.org\/abs\/1904.09223.","journal-title":"CoRR"},{"key":"e_1_3_2_206_2","first-page":"3837","volume-title":"Proc. NeurIPS, 2016","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Proc. NeurIPS, 2016. 3837\u20133845. http:\/\/papers.nips.cc\/paper\/6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering."},{"key":"e_1_3_2_207_2","article-title":"Reinforced neighborhood selection guided multi-relational graph neural networks","author":"Peng Hao","year":"2021","unstructured":"Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, and Philip S. Yu. 2021. Reinforced neighborhood selection guided multi-relational graph neural networks. arXiv preprint arXiv:2104.07886 (2021).","journal-title":"arXiv preprint arXiv:2104.07886"},{"key":"e_1_3_2_208_2","article-title":"Lime: Low-cost incremental learning for dynamic heterogeneous information networks","author":"Peng Hao","year":"2021","unstructured":"Hao Peng, Renyu Yang, Zheng Wang, Jianxin Li, Lifang He, Philip Yu, Albert Zomaya, and Raj Ranjan. 2021. Lime: Low-cost incremental learning for dynamic heterogeneous information networks. IEEE Trans. Comput. (2021).","journal-title":"IEEE Trans. Comput."},{"key":"e_1_3_2_209_2","article-title":"Higher-order attribute-enhancing heterogeneous graph neural networks","author":"Li Jianxin","year":"2021","unstructured":"Jianxin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip Yu, and Lifang He. 2021. Higher-order attribute-enhancing heterogeneous graph neural networks. IEEE Transactions on Knowledge and Data Engineering (2021).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_210_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d17-1159"},{"key":"e_1_3_2_211_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy157"},{"key":"e_1_3_2_212_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d17-1209"},{"key":"e_1_3_2_213_2","volume-title":"Proc. ICLR, 2018","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In Proc. ICLR, 2018. https:\/\/openreview.net\/forum?id=rJXMpikCZ."},{"key":"e_1_3_2_214_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1488"},{"key":"e_1_3_2_215_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/584"},{"key":"e_1_3_2_216_2","first-page":"737","volume-title":"Proc. NeurIPS, 1993]","author":"Bromley Jane","year":"1993","unstructured":"Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard S\u00e4ckinger, and Roopak Shah. 1993. Signature verification using a siamese time delay neural network. In Proc. NeurIPS, 1993]. 737\u2013744. http:\/\/papers.nips.cc\/paper\/769-signature-verification-using-a-siamese-time-delay-neural-network."},{"key":"e_1_3_2_217_2","first-page":"2786","volume-title":"Proc. AAAI, 2016","author":"Mueller Jonas","year":"2016","unstructured":"Jonas Mueller and Aditya Thyagarajan. 2016. Siamese recurrent architectures for learning sentence similarity. In Proc. AAAI, 2016. 2786\u20132792. http:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI16\/paper\/view\/12195."},{"key":"e_1_3_2_218_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.07.089"},{"key":"e_1_3_2_219_2","unstructured":"Takeru Miyato Shin ichi Maeda Masanori Koyama Ken Nakae and Shin Ishii. 2015. Distributional Smoothing with Virtual Adversarial Training. (2015). arxiv:stat.ML\/1507.00677."},{"key":"e_1_3_2_220_2","first-page":"6053","volume-title":"Proc. AAAI, 2018","author":"Zhang Tianyang","year":"2018","unstructured":"Tianyang Zhang, Minlie Huang, and Li Zhao. 2018. Learning structured representation for text classification via reinforcement learning. In Proc. AAAI, 2018. 6053\u20136060. https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16537."},{"key":"e_1_3_2_221_2","unstructured":"Jason Weston Sumit Chopra and Antoine Bordes. 2015. Memory Networks. (2015). arxiv:cs.AI\/1410.3916."},{"key":"e_1_3_2_222_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d17-1310"},{"key":"e_1_3_2_223_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1401"},{"key":"e_1_3_2_224_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1495"},{"key":"e_1_3_2_225_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01441-4"},{"key":"e_1_3_2_226_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMC2-NIPS53020.2019.00016"},{"key":"e_1_3_2_227_2","unstructured":"2002. MR Corpus. http:\/\/www.cs.cornell.edu\/people\/pabo\/movie-review-data\/. (2002)."},{"key":"e_1_3_2_228_2","unstructured":"2013. SST Corpus. http:\/\/nlp.stanford.edu\/sentiment. (2013)."},{"key":"e_1_3_2_229_2","first-page":"1631","volume-title":"Proc. EMNLP, 2013","author":"Socher Richard","year":"2013","unstructured":"Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proc. EMNLP, 2013. Association for Computational Linguistics, Seattle, Washington, USA, 1631\u20131642. https:\/\/www.aclweb.org\/anthology\/D13-1170."},{"key":"e_1_3_2_230_2","unstructured":"2005. MPQA Corpus. http:\/\/www.cs.pitt.edu\/mpqa\/. (2005)."},{"key":"e_1_3_2_231_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623758"},{"key":"e_1_3_2_232_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d15-1167"},{"key":"e_1_3_2_233_2","unstructured":"2015. Amazon Review Corpus. https:\/\/www.kaggle.com\/datafiniti\/consumer-reviews-of-amazon-products. (2015)."},{"key":"e_1_3_2_234_2","unstructured":"2013. Twitter Corpus. https:\/\/www.cs.york.ac.uk\/semeval-2013\/task2\/. (2013)."},{"key":"e_1_3_2_235_2","unstructured":"2004. AG Corpus. http:\/\/www.di.unipi.it\/gulli\/AG_corpus_of_news_articles.html. (2004)."},{"key":"e_1_3_2_236_2","unstructured":"2007. Reuters Corpus. https:\/\/www.cs.umb.edu\/smimarog\/textmining\/datasets\/. (2007)."},{"key":"e_1_3_2_237_2","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367560"},{"key":"e_1_3_2_238_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271671"},{"key":"e_1_3_2_239_2","doi-asserted-by":"publisher","DOI":"10.3233\/SW-140134"},{"key":"e_1_3_2_240_2","unstructured":"2015. Ohsumed Corpus. http:\/\/davis.wpi.edu\/xmdv\/datasets\/ohsumed.html. (2015)."},{"key":"e_1_3_2_241_2","unstructured":"2019. EUR-Lex Corpus. http:\/\/www.ke.tu-darmstadt.de\/resources\/eurlex\/eurlex.html. (2019)."},{"key":"e_1_3_2_242_2","unstructured":"2016. Amazon670K Corpus. http:\/\/manikvarma.org\/downloads\/XC\/XMLRepository.html. (2016)."},{"key":"e_1_3_2_243_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623715"},{"key":"e_1_3_2_244_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3009247"},{"key":"e_1_3_2_245_2","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2662067"},{"key":"e_1_3_2_246_2","unstructured":"www.datatang.com\/data\/44139 and 43543 2015 Fudan Corpus"},{"key":"e_1_3_2_247_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d16-1264"},{"key":"e_1_3_2_248_2","first-page":"858","volume-title":"Proc. NAACL, 2013","author":"Yao Xuchen","year":"2013","unstructured":"Xuchen Yao, Benjamin Van Durme, Chris Callison-Burch, and Peter Clark. 2013. Answer extraction as sequence tagging with tree edit distance. In Proc. NAACL, 2013. 858\u2013867. https:\/\/www.aclweb.org\/anthology\/N13-1106\/."},{"key":"e_1_3_2_249_2","unstructured":"2002. TREC Corpus. https:\/\/cogcomp.seas.upenn.edu\/Data\/QA\/QC\/. (2002)."},{"key":"e_1_3_2_250_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d15-1237"},{"key":"e_1_3_2_251_2","doi-asserted-by":"publisher","DOI":"10.3115\/1218955.1218990"},{"key":"e_1_3_2_252_2","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014073"},{"key":"e_1_3_2_253_2","unstructured":"2017. Reuters Corpus. https:\/\/martin-thoma.com\/nlp-reuters. (2017)."},{"key":"e_1_3_2_254_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.10.033"},{"key":"e_1_3_2_255_2","unstructured":"2020. Reuters10 Corpus. http:\/\/www.nltk.org\/book\/ch02.html. (2020)."},{"key":"e_1_3_2_256_2","doi-asserted-by":"publisher","DOI":"10.5555\/1005332.1005345"},{"key":"e_1_3_2_257_2","unstructured":"2004. RCV1-V2 Corpus. http:\/\/www.ai.mit.edu\/projects\/jmlr\/papers\/volume5\/lewis04a\/lyrl2004_rcv1v2_README.htm. (2004)."},{"key":"e_1_3_2_258_2","first-page":"115","volume-title":"Proc. ACL, 2005","author":"Pang Bo","year":"2005","unstructured":"Bo Pang and Lillian Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proc. ACL, 2005. 115\u2013124. https:\/\/www.aclweb.org\/anthology\/P05-1015\/."},{"key":"e_1_3_2_259_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-005-7880-9"},{"key":"e_1_3_2_260_2","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21662"},{"key":"e_1_3_2_261_2","volume-title":"Proc. SemEval, 2016","author":"Nakov Preslav","year":"2016","unstructured":"Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov. 2016. SemEval-2016 task 4: Sentiment analysis in Twitter. In Proc. SemEval, 2016."},{"key":"e_1_3_2_262_2","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baq036"},{"key":"e_1_3_2_263_2","volume-title":"Proc. NeurIPS, 2016","author":"Nguyen Tri","year":"2016","unstructured":"Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In Proc. NeurIPS, 2016. http:\/\/ceur-ws.org\/Vol-1773\/CoCoNIPS_2016_paper9.pdf."},{"key":"e_1_3_2_264_2","unstructured":"https:\/\/data.quora.com\/First-Quora-Dataset-Release-QuestionPairs. ([n. d.])."},{"key":"e_1_3_2_265_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-2124"},{"key":"e_1_3_2_266_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n18-1101"},{"key":"e_1_3_2_267_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/s14-2001"},{"key":"e_1_3_2_268_2","volume-title":"Proc. COLING, 2004","author":"Dolan Bill","year":"2004","unstructured":"Bill Dolan, Chris Quirk, and Chris Brockett. 2004. Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources. In Proc. COLING, 2004. https:\/\/www.aclweb.org\/anthology\/C04-1051\/."},{"key":"e_1_3_2_269_2","article-title":"SemEval-2017 task 1: Semantic textual similarity - multilingual and cross-lingual focused evaluation","volume":"1708","author":"Cer Daniel M.","year":"2017","unstructured":"Daniel M. Cer, Mona T. Diab, Eneko Agirre, I\u00f1igo Lopez-Gazpio, and Lucia Specia. 2017. SemEval-2017 task 1: Semantic textual similarity - multilingual and cross-lingual focused evaluation. CoRR abs\/1708.00055 (2017). arxiv:1708.00055http:\/\/arxiv.org\/abs\/1708.00055.","journal-title":"CoRR"},{"key":"e_1_3_2_270_2","doi-asserted-by":"publisher","DOI":"10.1007\/11736790_9"},{"key":"e_1_3_2_271_2","first-page":"5189","volume-title":"Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th Innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018","author":"Khot Tushar","year":"2018","unstructured":"Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018. SciTaiL: A textual entailment dataset from science question answering. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th Innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018. 5189\u20135197. https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/17368."},{"key":"e_1_3_2_272_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2017.0-134"},{"key":"e_1_3_2_273_2","unstructured":"2018. AmazonCat-13K Corpus. https:\/\/drive.google.com\/open?id=1VwHAbri6y6oh8lkpZ6sSY_b1FRNnCLFL. (2018)."},{"key":"e_1_3_2_274_2","unstructured":"2017. BlurbGenreCollection-EN Corpus. https:\/\/www.inf.uni-hamburg.de\/en\/inst\/ab\/lt\/resources\/data\/blurb-genre-collection.html. (2017)."},{"key":"e_1_3_2_275_2","first-page":"94","volume-title":"Proc. NAACL, 2009","author":"Hendrickx Iris","year":"2009","unstructured":"Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid \u00d3 S\u00e9aghdha, Sebastian Pad\u00f3, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. 2009. SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In Proc. NAACL, 2009. 94\u201399. https:\/\/www.aclweb.org\/anthology\/W09-2415\/."},{"key":"e_1_3_2_276_2","volume-title":"Proc. LREC, 2008","author":"Strassel Stephanie M.","year":"2008","unstructured":"Stephanie M. Strassel, Mark A. Przybocki, Kay Peterson, Zhiyi Song, and Kazuaki Maeda. 2008. Linguistic resources and evaluation techniques for evaluation of cross-document automatic content extraction. In Proc. LREC, 2008. http:\/\/www.lrec-conf.org\/proceedings\/lrec2008\/summaries\/677.html."},{"key":"e_1_3_2_277_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d17-1004"},{"key":"e_1_3_2_278_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15939-8_10"},{"key":"e_1_3_2_279_2","unstructured":"2019. FewRel Corpus. https:\/\/github.com\/thunlp\/FewRel. (2019)."},{"key":"e_1_3_2_280_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-2585-3_36"},{"key":"e_1_3_2_281_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2005.1415300"},{"key":"e_1_3_2_282_2","unstructured":"Dan Jurafsky and Elizabeth Shriberg. 1997. Switchboard SWBD-DAMSL shallow-discourse-function annotation coders manual. (01 1997)."},{"key":"e_1_3_2_283_2","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767738"},{"key":"e_1_3_2_284_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809071"},{"key":"e_1_3_2_285_2","doi-asserted-by":"publisher","DOI":"10.5555\/1857999.1858119"},{"key":"e_1_3_2_286_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1031"},{"key":"e_1_3_2_287_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1216"},{"key":"e_1_3_2_288_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1441"},{"key":"e_1_3_2_289_2","article-title":"Train once, test anywhere: Zero-shot learning for text classification","volume":"1712","author":"Pushp Pushpankar Kumar","year":"2017","unstructured":"Pushpankar Kumar Pushp and Muktabh Mayank Srivastava. 2017. Train once, test anywhere: Zero-shot learning for text classification. CoRR abs\/1712.05972 (2017). arxiv:1712.05972http:\/\/arxiv.org\/abs\/1712.05972.","journal-title":"CoRR"},{"key":"e_1_3_2_290_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/556"},{"key":"e_1_3_2_291_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1403"},{"key":"e_1_3_2_292_2","first-page":"13773","volume-title":"Proc. AAAI, 2020","author":"Deng Shumin","year":"2020","unstructured":"Shumin Deng, Ningyu Zhang, Zhanlin Sun, Jiaoyan Chen, and Huajun Chen. 2020. When low resource NLP meets unsupervised language model: Meta-pretraining then meta-learning for few-shot text classification (student abstract). In Proc. AAAI, 2020. 13773\u201313774. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/7158."},{"key":"e_1_3_2_293_2","first-page":"1087","volume-title":"Proc. ACL, 2020","author":"Geng Ruiying","year":"2020","unstructured":"Ruiying Geng, Binhua Li, Yongbin Li, Jian Sun, and Xiaodan Zhu. 2020. Dynamic memory induction networks for few-shot text classification. In Proc. ACL, 2020. 1087\u20131094. https:\/\/www.aclweb.org\/anthology\/2020.acl-main.102\/."},{"key":"e_1_3_2_294_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.205"},{"key":"e_1_3_2_295_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01221-4"},{"key":"e_1_3_2_296_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1021"},{"key":"e_1_3_2_297_2","first-page":"53","volume-title":"Proc. EKAW, 2018","author":"T\u00fcrker Rima","year":"2018","unstructured":"Rima T\u00fcrker, Lei Zhang, Maria Koutraki, and Harald Sack. 2018. TECNE: Knowledge based text classification using network embeddings. In Proc. EKAW, 2018. 53\u201356. http:\/\/ceur-ws.org\/Vol-2262\/ekaw-demo-18.pdf."},{"key":"e_1_3_2_298_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/619"},{"key":"e_1_3_2_299_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJMEI.2021.111861"},{"key":"e_1_3_2_300_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01680-w"},{"key":"e_1_3_2_301_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106876"},{"key":"e_1_3_2_302_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.544"},{"key":"e_1_3_2_303_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2969705"},{"key":"e_1_3_2_304_2","article-title":"A simple yet brisk and efficient active learning platform for text classification","volume":"2102","author":"Kanchinadam Teja","year":"2021","unstructured":"Teja Kanchinadam, Qian You, Keith Westpfahl, James Kim, Siva Gunda, Sebastian Seith, and Glenn Fung. 2021. A simple yet brisk and efficient active learning platform for text classification. CoRR abs\/2102.00426 (2021). arxiv:2102.00426https:\/\/arxiv.org\/abs\/2102.00426.","journal-title":"CoRR"},{"key":"e_1_3_2_305_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1496"},{"key":"e_1_3_2_306_2","article-title":"T-Miner: A generative approach to defend against trojan attacks on DNN-based text classification","volume":"2103","author":"Azizi Ahmadreza","year":"2021","unstructured":"Ahmadreza Azizi, Ibrahim Asadullah Tahmid, Asim Waheed, Neal Mangaokar, Jiameng Pu, Mobin Javed, Chandan K. Reddy, and Bimal Viswanath. 2021. T-Miner: A generative approach to defend against trojan attacks on DNN-based text classification. CoRR abs\/2103.04264 (2021). arxiv:2103.04264https:\/\/arxiv.org\/abs\/2103.04264.","journal-title":"CoRR"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3495162","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3495162","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3495162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:02Z","timestamp":1750191122000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3495162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,8]]},"references-count":305,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4,30]]}},"alternative-id":["10.1145\/3495162"],"URL":"https:\/\/doi.org\/10.1145\/3495162","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,8]]},"assertion":[{"value":"2021-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}