{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T20:26:15Z","timestamp":1769977575545,"version":"3.49.0"},"reference-count":42,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2022,3,14]]},"abstract":"<jats:p>The similar case matching task aims to detect which two cases are more similar for a given triplet. It plays a significant role in the legal industry and thus has gained much attention. Due to the rapid development of natural language processing technology, various deep learning techniques have been applied to similar case matching task and obtained attractive performance. Most existing researches usually focus on encoding legal documents into a continuous vector. However, a unified vector is difficult to model multiple elements of the case. In the real world, cases contain numerous elements, which are the basis for legal practitioners to judge the similarity among cases. Legal experts usually focus on whether the two cases have similar legal elements. It makes this task especially challenging. In this paper, we propose a novel model, namely Interactive Attention Capsule Network (dubbed as IACN). It attempts to simulate the process of judgment by legal experts, which captures fine-grained elements similarity to make an interpretable judgment. In other words, the IACN judges the similarity of the case pairs based on the legal elements. The more similar legal elements of a case pair, the higher the degree of similarity of the case pair. In addition, we devise an interactive dynamic routing mechanism, which can better learn the interactive representation of legal elements among cases than the vanilla dynamic routing. We conduct extensive experiments based on a real-world dataset. The experimental results consistently demonstrate the superiorities and competitiveness of our proposed model.<\/jats:p>","DOI":"10.3233\/ida-205632","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T22:19:43Z","timestamp":1647987583000},"page":"525-541","source":"Crossref","is-referenced-by-count":6,"title":["IACN: Interactive attention capsule network for similar case matching"],"prefix":"10.1177","volume":"26","author":[{"given":"Hui","family":"Li","sequence":"first","affiliation":[{"name":"Law School, Hunan University, Changsha, Hunan, China"},{"name":"Law School, Hunan University, Changsha, Hunan, China"}]},{"given":"Jin","family":"Lu","sequence":"additional","affiliation":[{"name":"Changsha Lvzhidao Information Technology Co., Ltd., Changsha, Hunan, China"},{"name":"Law School, Hunan University, Changsha, Hunan, China"}]},{"given":"Yuquan","family":"Le","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China"},{"name":"Changsha Lvzhidao Information Technology Co., Ltd., Changsha, Hunan, China"}]},{"given":"Jiawei","family":"He","sequence":"additional","affiliation":[{"name":"Changsha Lvzhidao Information Technology Co., Ltd., Changsha, Hunan, China"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-205632_ref1","doi-asserted-by":"crossref","unstructured":"E. Agirre, C. Banea, C. Cardie, D. Cer, M. Diab, A. Gonzalez-Agirre, W. Guo, I. Lopez-Gazpio, M. Maritxalar, R. Mihalcea et al., Semeval-2015 task 2: Semantic textual similarity, english, spanish and pilot on interpretability, in: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015, pp. 252\u2013263.","DOI":"10.18653\/v1\/S15-2045"},{"key":"10.3233\/IDA-205632_ref2","doi-asserted-by":"crossref","unstructured":"E. Agirre, C. Banea, C. Cardie, D. Cer, M. Diab, A. Gonzalez-Agirre, W. Guo, R. Mihalcea, G. Rigau and J. Wiebe, Semeval-2014 task 10: Multilingual semantic textual similarity, in: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, pp. 81\u201391.","DOI":"10.3115\/v1\/S14-2010"},{"key":"10.3233\/IDA-205632_ref3","doi-asserted-by":"crossref","unstructured":"E. Agirre, C. Banea, D. Cer, M. Diab, A. Gonzalez Agirre, R. Mihalcea, G. Rigau Claramunt and J. Wiebe, Semeval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation, in: SemEval-2016. 10th International Workshop on Semantic Evaluation; 2016 Jun 16\u201317; San Diego, CA. Stroudsburg (PA): ACL; 2016. pp. 497\u2013511, ACL (Association for Computational Linguistics), 2016.","DOI":"10.18653\/v1\/S16-1081"},{"key":"10.3233\/IDA-205632_ref4","unstructured":"E. Agirre, D. Cer, M. Diab and A. Gonzalez-Agirre, Semeval-2012 task 6: A pilot on semantic textual similarity, in: * SEM 2012: The First Joint Conference on Lexical and Computational Semantics\u2013Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), 2012, pp. 385\u2013393."},{"key":"10.3233\/IDA-205632_ref5","unstructured":"E. Agirre, D. Cer, M. Diab, A. Gonzalez-Agirre and W. Guo, * sem 2013 shared task: Semantic textual similarity, in: Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 2013, pp. 32\u201343."},{"issue":"1","key":"10.3233\/IDA-205632_ref6","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0306-4573(02)00021-3","article-title":"An information-theoretic perspective of tf-idf measures","volume":"39","author":"Aizawa","year":"2003","journal-title":"Information Processing & Management"},{"key":"10.3233\/IDA-205632_ref8","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"The Journal of Machine Learning Research"},{"key":"10.3233\/IDA-205632_ref10","doi-asserted-by":"crossref","unstructured":"J. Bromley, I. Guyon, Y. LeCun, E. S\u00e4ckinger and R. Shah, Signature verification using a \u201csiamese\u201d time delay neural network, in: Advances in Neural Information Processing Systems, 1994, pp. 737\u2013744.","DOI":"10.1142\/9789812797926_0003"},{"key":"10.3233\/IDA-205632_ref14","unstructured":"J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, in: Roceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019, pp. 4171\u20134186."},{"key":"10.3233\/IDA-205632_ref15","doi-asserted-by":"crossref","unstructured":"X. Dong and J. Shen, Triplet loss in siamese network for object tracking, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 459\u2013474.","DOI":"10.1007\/978-3-030-01261-8_28"},{"key":"10.3233\/IDA-205632_ref17","doi-asserted-by":"crossref","unstructured":"B. Hachey and C. Grover, Sentence extraction for legal text summarisation, in: IJCAI, 2005, pp. 1686\u20131687.","DOI":"10.1145\/1165485.1165498"},{"key":"10.3233\/IDA-205632_ref18","doi-asserted-by":"crossref","unstructured":"C. He, L. Peng, Y. Le, J. He and X. Zhu, SECaps: A sequence enhanced capsule model for charge prediction, in: I.V. Tetko, V. K\u016frkov\u00e1, P. Karpov and F. Theis, eds, Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Text and Time Series, Cham, 2019, pp. 227\u2013239. Springer International Publishing.","DOI":"10.1007\/978-3-030-30490-4_19"},{"issue":"4","key":"10.3233\/IDA-205632_ref19","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1017\/S1351324901002807","article-title":"Natural language question answering: the view from here","volume":"7","author":"Hirschman","year":"2001","journal-title":"Natural Language Engineering"},{"key":"10.3233\/IDA-205632_ref20","unstructured":"B. Hu, Z. Lu, H. Li and Q. Chen, Convolutional neural network architectures for matching natural language sentences, in: Advances in Neural Information Processing Systems, 2014, pp. 2042\u20132050."},{"key":"10.3233\/IDA-205632_ref22","unstructured":"Z. Hu, X. Li, C. Tu, Z. Liu and M. Sun, Few-shot charge prediction with discriminative legal attributes, in: Proceedings of the 27th International Conference on Computational Linguistics, 2018, pp. 487\u2013498."},{"key":"10.3233\/IDA-205632_ref23","doi-asserted-by":"crossref","unstructured":"P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero and L. Heck, Learning deep structured semantic models for web search using clickthrough data, in: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, 2013, pp. 2333\u20132338.","DOI":"10.1145\/2505515.2505665"},{"key":"10.3233\/IDA-205632_ref24","doi-asserted-by":"crossref","unstructured":"J.-Y. Jiang, M. Zhang, C. Li, M. Bendersky, N. Golbandi and M. Najork, Semantic text matching for long-form documents, in: The World Wide Web Conference, 2019, pp. 795\u2013806.","DOI":"10.1145\/3308558.3313707"},{"key":"10.3233\/IDA-205632_ref25","first-page":"829","article-title":"Mathematical models for legal prediction","volume":"2","author":"Keown","year":"1980","journal-title":"Computer\/LJ"},{"key":"10.3233\/IDA-205632_ref26","doi-asserted-by":"crossref","unstructured":"Y. Kim, Convolutional neural networks for sentence classification, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, Oct. 2014, pp. 1746\u20131751. Association for Computational Linguistics.","DOI":"10.3115\/v1\/D14-1181"},{"issue":"1","key":"10.3233\/IDA-205632_ref27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2307\/1951767","article-title":"Predicting supreme court decisions mathematically: A quantitative analysis of the i\u2218 right to counsel i\u00b1 cases","volume":"51","author":"Kort","year":"1957","journal-title":"American Political Science Review"},{"key":"10.3233\/IDA-205632_ref29","doi-asserted-by":"crossref","unstructured":"Y. Le, Z.-J. Wang, Z. Quan, J. He and B. Yao, Acv-tree: A new method for sentence similarity modeling, in: IJCAI, 2018, pp. 4137\u20134143.","DOI":"10.24963\/ijcai.2018\/575"},{"issue":"2","key":"10.3233\/IDA-205632_ref30","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/52.582976","article-title":"Document ranking and the vector-space model","volume":"14","author":"Lee","year":"1997","journal-title":"IEEE Software"},{"key":"10.3233\/IDA-205632_ref31","doi-asserted-by":"crossref","unstructured":"B. Liu, D. Niu, H. Wei, J. Lin, Y. He, K. Lai and Y. Xu, Matching article pairs with graphical decomposition and convolutions, in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp.\u00a06284\u20136294.","DOI":"10.18653\/v1\/P19-1632"},{"key":"10.3233\/IDA-205632_ref32","doi-asserted-by":"crossref","unstructured":"B. Luo, Y. Feng, J. Xu, X. Zhang and D. Zhao, Learning to predict charges for criminal cases with legal basis, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, Sept. 2017, pp. 2727\u20132736. Association for Computational Linguistics.","DOI":"10.18653\/v1\/D17-1289"},{"key":"10.3233\/IDA-205632_ref34","unstructured":"T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado and J. Dean, Distributed representations of words and phrases and their compositionality, in: NIPS, 2013, pp. 3111\u20133119."},{"key":"10.3233\/IDA-205632_ref35","doi-asserted-by":"crossref","unstructured":"B. Mitra, F. Diaz and N. Craswell, Learning to match using local and distributed representations of text for web search, in: Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 1291\u20131299.","DOI":"10.1145\/3038912.3052579"},{"key":"10.3233\/IDA-205632_ref36","first-page":"1006","article-title":"Applying correlation analysis to case prediction","volume":"42","author":"Nagel","year":"1963","journal-title":"Tex. L. Rev."},{"key":"10.3233\/IDA-205632_ref37","doi-asserted-by":"crossref","unstructured":"L. Pang, Y. Lan, J. Guo, J. Xu, S. Wan and X. Cheng, Text matching as image recognition, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30, 2016.","DOI":"10.1609\/aaai.v30i1.10341"},{"key":"10.3233\/IDA-205632_ref38","doi-asserted-by":"crossref","unstructured":"J. Pennington, R. Socher and C.D. Manning, Glove: Global vectors for word representation, in: EMNLP, 2014, pp.\u00a01532\u20131543.","DOI":"10.3115\/v1\/D14-1162"},{"key":"10.3233\/IDA-205632_ref40","unstructured":"S. Sabour, N. Frosst and G. Hinton, Matrix capsules with em routing, in: 6th International Conference on Learning Representations, ICLR, 2018."},{"key":"10.3233\/IDA-205632_ref41","unstructured":"S. Sabour, N. Frosst and G.E. Hinton, Dynamic routing between capsules, in: I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett, eds, Advances in Neural Information Processing Systems 30, Curran Associates, Inc., 2017, pp. 3856\u20133866."},{"issue":"5","key":"10.3233\/IDA-205632_ref42","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","article-title":"Term-weighting approaches in automatic text retrieval","volume":"24","author":"Salton","year":"1988","journal-title":"Information Processing & Management"},{"issue":"11","key":"10.3233\/IDA-205632_ref43","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1145\/361219.361220","article-title":"A vector space model for automatic indexing","volume":"18","author":"Salton","year":"1975","journal-title":"Communications of the ACM"},{"key":"10.3233\/IDA-205632_ref45","doi-asserted-by":"crossref","unstructured":"Y. Shen, X. He, J. Gao, L. Deng and G. Mesnil, Learning semantic representations using convolutional neural networks for web search, in: Proceedings of the 23rd International Conference on World Wide Web, 2014, pp. 373\u2013374.","DOI":"10.1145\/2567948.2577348"},{"issue":"1","key":"10.3233\/IDA-205632_ref46","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s10506-017-9195-8","article-title":"On the concept of relevance in legal information retrieval","volume":"25","author":"Van Opijnen","year":"2017","journal-title":"Artificial Intelligence and Law"},{"key":"10.3233\/IDA-205632_ref47","doi-asserted-by":"crossref","first-page":"e262","DOI":"10.7717\/peerj-cs.262","article-title":"Legal document similarity: A multi-criteria decision-making perspective","volume":"6","author":"Wagh","year":"2020","journal-title":"PeerJ Computer Science"},{"key":"10.3233\/IDA-205632_ref48","doi-asserted-by":"crossref","unstructured":"S. Wan, Y. Lan, J. Guo, J. Xu, L. Pang and X. Cheng, A deep architecture for semantic matching with multiple positional sentence representations, in: Thirtieth AAAI Conference on Artificial Intelligence, 2016.","DOI":"10.1609\/aaai.v30i1.10342"},{"key":"10.3233\/IDA-205632_ref52","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1162\/tacl_a_00097","article-title":"Abcnn: Attention-based convolutional neural network for modeling sentence pairs","volume":"4","author":"Yin","year":"2016","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"10.3233\/IDA-205632_ref53","doi-asserted-by":"crossref","first-page":"7484","DOI":"10.1609\/aaai.v33i01.33017484","article-title":"Multi-labeled relation extraction with attentive capsule network","volume":"33","author":"Zhang","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/IDA-205632_ref54","doi-asserted-by":"crossref","unstructured":"H. Zhong, Z. Guo, C. Tu, C. Xiao, Z. Liu and M. 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