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Therefore, in this paper we propose a negative-supervised capsule graph neural network (NSCGNN) which explicitly takes use of the similarity and dissimilarity between samples to make the text representations of the same type closer with each other and the ones of different types farther away, leading to representative and discriminative class prototypes. We firstly construct a graph to obtain text representations in the form of node capsules, where both intra-cluster similarity and inter-cluster dissimilarity between all samples are explored with information aggregation and negative supervision. Then, in order to induce generalized class prototypes based on those node capsules obtained from graph neural network, the dynamic routing algorithm is utilized in our model. Experimental results demonstrate the effectiveness of our proposed NSCGNN model, which outperforms existing few-shot approaches on three benchmark datasets.<\/jats:p>","DOI":"10.3233\/jifs-210795","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T11:45:15Z","timestamp":1630669515000},"page":"6875-6887","source":"Crossref","is-referenced-by-count":1,"title":["Negative-supervised capsule graph neural network for few-shot text classification"],"prefix":"10.1177","volume":"41","author":[{"given":"Ling","family":"Ding","sequence":"first","affiliation":[{"name":"Computer Science and Technology Department, Tongji University, Cao\u2019an highway, Jiading District, Shanghai, China"}]},{"given":"Xiaojun","family":"Chen","sequence":"additional","affiliation":[{"name":"Computer Science and Technology Department, Tongji University, Cao\u2019an highway, Jiading District, Shanghai, China"}]},{"given":"Yang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Computer Science and Technology Department, Tongji University, Cao\u2019an highway, Jiading District, Shanghai, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-210795_ref1","unstructured":"Bao Y. , Wu M. , Chang S. and Barzilay R. , Few-shot text classification with distributional signatures, In International Conference on Learning Representations (2019)."},{"key":"10.3233\/JIFS-210795_ref2","doi-asserted-by":"crossref","unstructured":"Bogin B. , Berant J. and Gardner M. , Representing schema structure with graph neural networks for text-to-sql parsing, In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019), 4560\u20134565.","DOI":"10.18653\/v1\/P19-1448"},{"key":"10.3233\/JIFS-210795_ref4","unstructured":"Donahue J. , Jia Y. , Vinyals O. , Hoffman J. , Zhang N. , Tzeng E. and Darrell T. , Decaf: A deep convolutional activation feature for generic visual recognition, In International conference on machine learning (2014), PMLR, 647\u2013655."},{"key":"10.3233\/JIFS-210795_ref5","unstructured":"Finn C. , Abbeel P. and Levine S. , Model-agnostic metalearning for fast adaptation of deep networks,PMLR, In International Conference on Machine Learning (2017), 1126\u20131135."},{"key":"10.3233\/JIFS-210795_ref6","doi-asserted-by":"crossref","first-page":"6407","DOI":"10.1609\/aaai.v33i01.33016407","article-title":"Hybrid attentionbased prototypical networks for noisy few-shot relation classification, In","volume":"33","author":"Gao","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/JIFS-210795_ref7","unstructured":"Garcia V. and Bruna J. , Few-shot learning with graph neural networks, In, 6th International Conference on Learning Representations, ICLR 2018 (2018)."},{"key":"10.3233\/JIFS-210795_ref8","doi-asserted-by":"crossref","unstructured":"Geng R. , Li B. , Li Y. , Sun J. and Zhu X. , Dynamic memory induction networks for few-shot text classification, In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020), 1087\u20131094.","DOI":"10.18653\/v1\/2020.acl-main.102"},{"key":"10.3233\/JIFS-210795_ref9","doi-asserted-by":"crossref","unstructured":"Geng R. , Li B. , Li Y. , Zhu X. , Jian P. and Sun J. , Induction networks for few-shot text classification, In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (2019), 3895\u20133904.","DOI":"10.18653\/v1\/D19-1403"},{"key":"10.3233\/JIFS-210795_ref10","unstructured":"Gori M. , Monfardini G. and Scarselli F. , A new model for learning in graph domains, In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. 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