{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T08:12:10Z","timestamp":1759565530914},"reference-count":25,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2024,2,1]]},"DOI":"10.1587\/transinf.2023edp7119","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T22:14:54Z","timestamp":1706739294000},"page":"212-219","source":"Crossref","is-referenced-by-count":2,"title":["BRsyn-Caps: Chinese Text Classification Using Capsule Network Based on Bert and Dependency Syntax"],"prefix":"10.1587","volume":"E107.D","author":[{"given":"Jie","family":"LUO","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot and the School of Computer Science and Engineering, Wuhan Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengwan","family":"HE","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot and the School of Computer Science and Engineering, Wuhan Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"LUO","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot and the School of Computer Science and Engineering, Wuhan Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] M.E. Maron, \u201cAutomatic indexing: an experimental inquiry,\u201d Journal of the ACM (JACM), vol.8, no.3, pp.404-417, 1961. 10.1145\/321075.321084","DOI":"10.1145\/321075.321084"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] T. Cover and P. Hart, \u201cNearest neighbor pattern classification,\u201d IEEE transactions on information theory, vol.13, no.1, pp.21-27, 1967. 10.1109\/tit.1967.1053964","DOI":"10.1109\/TIT.1967.1053964"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] T. Joachims, \u201cText categorization with support vector machines: Learning with many relevant features,\u201d Machine Learning: ECML-98: 10th European Conference on Machine Learning Chemnitz, Germany, April 21-23, 1998 Proceedings, pp.137-142, Springer, 2005. 10.1007\/bfb0026683","DOI":"10.1007\/BFb0026683"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] Q. Li, H. Peng, J. Li, C. Xia, R. Yang, L. Sun, P.S. Yu, and L. He, \u201cA survey on text classification: From traditional to deep learning,\u201d ACM Transactions on Intelligent Systems and Technology (TIST), vol.13, no.2, pp.1-41, 2022. 10.1145\/3495162","DOI":"10.1145\/3495162"},{"key":"5","unstructured":"[5] Q. Li, H. Peng, J. Li, C. Xia, R. Yang, L. Sun, P.S. Yu, and L. He, \u201cA survey on text classification: From shallow to deep learning,\u201d arXiv preprint arXiv:2008.00364, 2020."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] Y. Kim, \u201cConvolutional neural networks for sentence classification,\u201d Proc. 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp.1746-1751, Association for Computational Linguistics, Oct. 2014. 10.3115\/v1\/d14-1181","DOI":"10.3115\/v1\/D14-1181"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] J.L. Elman, \u201cFinding structure in time,\u201d Cognitive science, vol.14, no.2, pp.179-211, 1990. 10.1207\/s15516709cog1402_1","DOI":"10.1207\/s15516709cog1402_1"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] A. Graves, \u201cLong short-term memory,\u201d Supervised sequence labelling with recurrent neural networks, pp.37-45, 2012. 10.1007\/978-3-642-24797-2_4","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"9","unstructured":"[9] S. Sabour, N. Frosst, and G.E. Hinton, \u201cDynamic routing between capsules,\u201d Advances in neural information processing systems, vol.30, 2017."},{"key":"10","unstructured":"[10] J. Devlin, M.W. Chang, K. Lee, and K. Toutanova, \u201cBert: Pre-training of deep bidirectional transformers for language understanding,\u201d arXiv preprint arXiv:1810.04805, 2018."},{"key":"11","unstructured":"[11] P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, \u201cGraph attention networks,\u201d arXiv preprint arXiv:1710.10903, 2017."},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] L. Yao, C. Mao, and Y. Luo, \u201cGraph convolutional networks for text classification,\u201d Proc. AAAI conference on artificial intelligence, vol.33, no.01, pp.7370-7377, 2019. 10.1609\/aaai.v33i01.33017370","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] Y. Zhang, R. Jin, and Z.-H. Zhou, \u201cUnderstanding bag-of-words model: a statistical framework,\u201d International journal of machine learning and cybernetics, vol.1, pp.43-52, 2010. 10.1007\/s13042-010-0001-0","DOI":"10.1007\/s13042-010-0001-0"},{"key":"14","unstructured":"[14] W.B. Cavnar, J.M. Trenkle, et al., \u201cN-gram-based text categorization,\u201d Proc. SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, NV, 1994."},{"key":"15","unstructured":"[15] R. Baeza-Yates, B. Ribeiro-Neto, et al., Modern Information Retrieval, ACM Press New York, 1999."},{"key":"16","unstructured":"[16] R. Socher, J. Pennington, E.H. Huang, A.Y. Ng, and C.D. Manning, \u201cSemi-supervised recursive autoencoders for predicting sentiment distributions,\u201d Proc. 2011 Conference on Empirical Methods in Natural Language Processing, pp.151-161, 2011."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] R. Johnson and T. Zhang, \u201cDeep pyramid convolutional neural networks for text categorization,\u201d Proc. 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.562-570, 2017. 10.18653\/v1\/p17-1052","DOI":"10.18653\/v1\/P17-1052"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] J. Wang, L.-C. Yu, K.R. Lai, and X. Zhang, \u201cDimensional sentiment analysis using a regional cnn-lstm model,\u201d Proc. 54th annual meeting of the association for computational linguistics (volume 2: Short papers), pp.225-230, 2016. 10.18653\/v1\/p16-2037","DOI":"10.18653\/v1\/P16-2037"},{"key":"19","unstructured":"[19] W. Zhao, J. Ye, M. Yang, Z. Lei, S. Zhang, and Z. Zhao, \u201cInvestigating capsule networks with dynamic routing for text classification,\u201d arXiv preprint arXiv:1804.00538, 2018."},{"key":"20","unstructured":"[20] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, \u0141. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d Advances in neural information processing systems, vol.30, 2017."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] C. Zhang, Q. Li, and D. Song, \u201cAspect-based sentiment classification with aspect-specific graph convolutional networks,\u201d arXiv preprint arXiv:1909.03477, 2019.","DOI":"10.18653\/v1\/D19-1464"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] K. Wang, W. Shen, Y. Yang, X. Quan, and R. Wang, \u201cRelational graph attention network for aspect-based sentiment analysis,\u201d arXiv preprint arXiv:2004.12362, 2020.","DOI":"10.18653\/v1\/2020.acl-main.295"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] X. Jia and L. Wang, \u201cAttention enhanced capsule network for text classification by encoding syntactic dependency trees with graph convolutional neural network,\u201d PeerJ Computer Science, vol.8, p.e831, 2022. 10.7717\/peerj-cs.831","DOI":"10.7717\/peerj-cs.831"},{"key":"24","unstructured":"[24] P. Liu, X. Qiu, and X. Huang, \u201cRecurrent neural network for text classification with multi-task learning,\u201d arXiv preprint arXiv:1605.05101, 2016."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] P. Zhou, W. Shi, J. Tian, Z. Qi, B. Li, H. Hao, and B. Xu, \u201cAttention-based bidirectional long short-term memory networks for relation classification,\u201d Proc. 54th annual meeting of the association for computational linguistics (volume 2: Short papers), pp.207-212, 2016. 10.18653\/v1\/p16-2034","DOI":"10.18653\/v1\/P16-2034"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/2\/E107.D_2023EDP7119\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T04:17:38Z","timestamp":1706933858000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/2\/E107.D_2023EDP7119\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,1]]},"references-count":25,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023edp7119","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,1]]},"article-number":"2023EDP7119"}}