{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T15:00:59Z","timestamp":1774969259817,"version":"3.50.1"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031704413","type":"print"},{"value":"9783031704420","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70442-0_2","type":"book-chapter","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T08:09:40Z","timestamp":1725955780000},"page":"20-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Transformer-Based Architecture for\u00a0Judgment Prediction and\u00a0Explanation in\u00a0Legal Proceedings"],"prefix":"10.1007","author":[{"given":"Arooba","family":"Maqsood","sequence":"first","affiliation":[]},{"given":"Adnan","family":"Ul-Hasan","sequence":"additional","affiliation":[]},{"given":"Faisal","family":"Shafait","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, H.: A predictive performance comparison of machine learning models for judicial cases. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/SSCI.2017.8285436"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Virtucio, M.B.L., et al.: Predicting decisions of the Philippine supreme court using natural language processing and machine learning. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 130\u2013135. IEEE (2018)","DOI":"10.1109\/COMPSAC.2018.10348"},{"key":"2_CR3","unstructured":"Shaikha, R.A., Sahua, T.P., Anandb, V.: Predicting outcomes of legal cases based on legal factors using classifiers. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 130\u2013135. IEEE (2018)"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s10506-019-09255-y","volume":"28","author":"M Medvedeva","year":"2020","unstructured":"Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the European court of human rights. Artif. Intell. Law 28, 237\u2013266 (2020)","journal-title":"Artif. Intell. Law"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Liu, L., An, D., Wang, Y., Ma, X., Jiang, C.: Research on legal judgment prediction based on BERT and LSTM-CNN fusion model. In: 2021 3rd World Symposium on Artificial Intelligence (WSAI), pp. 41\u201345. IEEE (2021)","DOI":"10.1109\/WSAI51899.2021.9486374"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Strickson, B., Iglesia, B.D.L.: Legal judgement prediction for UK courts. In: Proceedings of the 2020 the 3rd International Conference on Information Science and System, pp. 204\u2013209 (2020)","DOI":"10.1145\/3388176.3388183"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Pillai, V.G., Chandran, L.R.: Verdict prediction for Indian courts using bag of words and convolutional neural network. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 676\u2013683. IEEE (2020)","DOI":"10.1109\/ICSSIT48917.2020.9214278"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Kowsrihawat, K., Vateekul, P., Boonkwan, P.: Predicting judicial decisions of criminal cases from Thai supreme court using bi-directional GRU with attention mechanism. In: 2018 5th Asian Conference on Defense Technology (ACDT), pp. 50\u201355. IEEE (2018)","DOI":"10.1109\/ACDT.2018.8592948"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Wang, Y., Gao, J., Chen, J.: Deep learning algorithm for judicial judgment prediction based on BERT. In: 2020 5th International Conference on Computing, Communication and Security (ICCCS), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/ICCCS49678.2020.9277068"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Chalkidis, I., Androutsopoulos, I., Aletras, N.: Neural legal judgment prediction. arXiv preprint arXiv:1906.02059 (2019). (in English)","DOI":"10.18653\/v1\/P19-1424"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Malik, V., et al.: ILDC for CJPE: Indian legal documents corpus for court judgment prediction and explanation. arXiv preprint arXiv:2105.13562 (2021)","DOI":"10.18653\/v1\/2021.acl-long.313"},{"issue":"5","key":"2_CR12","doi-asserted-by":"publisher","first-page":"2780","DOI":"10.11591\/eei.v10i5.3157","volume":"10","author":"DE Cahyani","year":"2021","unstructured":"Cahyani, D.E., Patasik, I.: Performance comparison of TF-IDF and word2vec models for emotion text classification. Bull. Electr. Eng. Inform. 10(5), 2780\u20132788 (2021)","journal-title":"Bull. Electr. Eng. Inform."},{"key":"2_CR13","unstructured":"Yahui, C.: Convolutional neural network for sentence classification. Master\u2019s thesis, University of Waterloo (2015)"},{"key":"2_CR14","unstructured":"Zhou, P., Qi, Z., Zheng, S., Xu, J., Bao, H., Xu, B.: Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling. arXiv preprint arXiv:1611.06639 (2016)"},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"180558","DOI":"10.1109\/ACCESS.2019.2957510","volume":"7","author":"J Xie","year":"2019","unstructured":"Xie, J., Chen, B., Gu, X., Liang, F., Xu, X.: Self-attention-based BiLSTM model for short text fine-grained sentiment classification. IEEE Access 7, 180558\u2013180570 (2019)","journal-title":"IEEE Access"},{"key":"2_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","volume-title":"Multiple Classifier Systems","author":"TG Dietterich","year":"2000","unstructured":"Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1\u201315. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45014-9_1"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)","DOI":"10.18653\/v1\/E17-2068"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 562\u2013570 (2017)","DOI":"10.18653\/v1\/P17-1052"},{"key":"2_CR20","unstructured":"Li, J., Monroe, W., Jurafsky, D.: Understanding neural networks through representation erasure. arXiv preprint arXiv:1612.08220 (2016)"},{"key":"2_CR21","unstructured":"Xiao, C., et al.: CAIL2018: a large-scale legal dataset for judgment prediction. arXiv preprint arXiv:1807.02478 (2018)"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480\u20131489 (2016)","DOI":"10.18653\/v1\/N16-1174"},{"key":"2_CR23","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"2_CR24","unstructured":"He, P., Liu, X., Gao, J., Chen, W: DeBERTa: decoding-enhanced BERT with disentangled attention. arXiv preprint arXiv:2006.03654 (2020)"},{"key":"2_CR25","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"2_CR26","unstructured":"Geng, S., Lebret, R., Aberer, K.: Legal transformer models may not always help. arXiv preprint arXiv:2109.06862 (2021)"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: LEGAL-BERT: the muppets straight out of law school. arXiv preprint arXiv:2010.02559 (2019)","DOI":"10.18653\/v1\/2020.findings-emnlp.261"},{"key":"2_CR28","unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: LongFormer: the long-document transformer. arXiv preprint arXiv:2004.05150 (2020)"},{"key":"2_CR29","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"2","key":"2_CR30","doi-asserted-by":"publisher","first-page":"83","DOI":"10.3390\/info13020083","volume":"13","author":"A Gasparetto","year":"2022","unstructured":"Gasparetto, A., Marcuzzo, M., Zangari, A., Albarelli, A.: A survey on text classification algorithms: from text to predictions. Information 13(2), 83 (2022)","journal-title":"Information"},{"key":"2_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1007\/978-3-030-32381-3_45","volume-title":"Chinese Computational Linguistics","author":"S Long","year":"2019","unstructured":"Long, S., Tu, C., Liu, Z., Sun, M.: Automatic judgment prediction via legal reading comprehension. In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds.) CCL 2019. LNCS (LNAI), vol. 11856, pp. 558\u2013572. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32381-3_45"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Paul, S., Mandal, A., Goyal, P., Ghosh, S.: Pre-trained language models for the legal domain: a case study on Indian law. In: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law, pp. 187\u2013196 (2023)","DOI":"10.1145\/3594536.3595165"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Zheng, L., Guha, N., Anderson, B.R., Henderson, P., Ho, D.E.: When does pretraining help? Assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pp. 159\u2013168 (2021)","DOI":"10.1145\/3462757.3466088"},{"key":"2_CR34","unstructured":"Zheng, L., Guha, N., Anderson, B.R., Henderson, P., Ho, D.E.: Paragraph-level rationale extraction through regularization: a case study on European court of human rights cases. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pp. 159\u2013168 (2021)"}],"container-title":["Lecture Notes in Computer Science","Document Analysis Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70442-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T08:10:18Z","timestamp":1725955818000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70442-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031704413","9783031704420"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70442-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"11 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Document Analysis Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"das2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/das2024.seecs.edu.pk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}