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27.12.2022 | Original Research

Using attention methods to predict judicial outcomes

verfasst von: Vithor Gomes Ferreira Bertalan, Evandro Eduardo Seron Ruiz

Erschienen in: Artificial Intelligence and Law | Ausgabe 1/2024

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Abstract

The prediction of legal judgments is one of the most recognized fields in Natural Language Processing, Artificial Intelligence, and Law combined. By legal prediction, we mean intelligent systems capable of predicting specific judicial characteristics such as the judicial outcome, the judicial class, and the prediction of a particular case. In this study, we used an artificial intelligence classifier to predict the decisions of Brazilian courts. To this end, we developed a text crawler to extract data from official Brazilian electronic legal systems, consisting of two datasets of cases of second-degree murder and active corruption. We applied various classifiers, such as Support Vector Machines, Neural Networks, and others, to predict judicial outcomes by analyzing text features from the dataset. Our research demonstrated that Regression Trees, Gated Recurring Units, and Hierarchical Attention Networks tended to have higher metrics across our datasets. As the final goal, we searched the weights of one of the algorithms, Hierarchical Attention Networks, to find samples of the words that might be used to acquit or convict defendants based on their relevance to the algorithm.

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Literatur
Zurück zum Zitat Alarie B, Niblett A, Yoon AH (2018) How artificial intelligence will affect the practice of law. Univ Tor Law J 68(supplement 1):106–124CrossRef Alarie B, Niblett A, Yoon AH (2018) How artificial intelligence will affect the practice of law. Univ Tor Law J 68(supplement 1):106–124CrossRef
Zurück zum Zitat Aletras N, Tsarapatsanis D, Preoţiuc-Pietro D, Lampos V (2016) Predicting judicial decisions of the European Court of Human Rights: a natural language processing perspective. Peer J Comput Sci 2:e93CrossRef Aletras N, Tsarapatsanis D, Preoţiuc-Pietro D, Lampos V (2016) Predicting judicial decisions of the European Court of Human Rights: a natural language processing perspective. Peer J Comput Sci 2:e93CrossRef
Zurück zum Zitat Alschner W, Skougarevskiy D (2017) Towards an automated production of legal texts using recurrent neural networks. In: Proceedings of the 16th Edition of the International Conference on Articial Intelligence and Law, Association for Computing Machinery, New York, NY, USA, ICAIL ’17, p 229-232, https://doi.org/10.1145/3086512.3086536 Alschner W, Skougarevskiy D (2017) Towards an automated production of legal texts using recurrent neural networks. In: Proceedings of the 16th Edition of the International Conference on Articial Intelligence and Law, Association for Computing Machinery, New York, NY, USA, ICAIL ’17, p 229-232, https://​doi.​org/​10.​1145/​3086512.​3086536
Zurück zum Zitat Antonucci L, Crocetta C, d’Ovidio FD (2014) Evaluation of Italian judicial system. Proc Econ Financ 17:121–130CrossRef Antonucci L, Crocetta C, d’Ovidio FD (2014) Evaluation of Italian judicial system. Proc Econ Financ 17:121–130CrossRef
Zurück zum Zitat Antos A, Nadhamuni N (2021) Practical guide to artificial intelligence and contract review. In: Research Handbook on Big Data Law, Edward Elgar Publishing Antos A, Nadhamuni N (2021) Practical guide to artificial intelligence and contract review. In: Research Handbook on Big Data Law, Edward Elgar Publishing
Zurück zum Zitat Ashley KD, Brüninghaus S (2009) Automatically classifying case texts and predicting outcomes. Artif Intell Law 17(2):125–165CrossRef Ashley KD, Brüninghaus S (2009) Automatically classifying case texts and predicting outcomes. Artif Intell Law 17(2):125–165CrossRef
Zurück zum Zitat Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis-a brief tutorial. Inst Signal Inf Process 18(1998):1–8 Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis-a brief tutorial. Inst Signal Inf Process 18(1998):1–8
Zurück zum Zitat Bishop CM (2006) Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg Bishop CM (2006) Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg
Zurück zum Zitat Branting LK, Yeh A, Weiss B, Merkhofer E, Brown B (2015) Inducing predictive models for decision support in administrative adjudication. In: AI Approaches to the Complexity of Legal Systems, Springer, pp 465–477 Branting LK, Yeh A, Weiss B, Merkhofer E, Brown B (2015) Inducing predictive models for decision support in administrative adjudication. In: AI Approaches to the Complexity of Legal Systems, Springer, pp 465–477
Zurück zum Zitat Chalkidis I, Fergadiotis M, Malakasiotis P, Aletras N, Androutsopoulos I (2019) Extreme multi-label legal text classification: A case study in eu legislation. arXiv preprint arXiv:1905.10892 Chalkidis I, Fergadiotis M, Malakasiotis P, Aletras N, Androutsopoulos I (2019) Extreme multi-label legal text classification: A case study in eu legislation. arXiv preprint arXiv:​1905.​10892
Zurück zum Zitat Chantar HK, Corne DW (2011) Feature subset selection for Arabic document categorization using bpso-knn. In: 2011 Third World Congress on Nature and Biologically Inspired Computing, IEEE, pp 546–551 Chantar HK, Corne DW (2011) Feature subset selection for Arabic document categorization using bpso-knn. In: 2011 Third World Congress on Nature and Biologically Inspired Computing, IEEE, pp 546–551
Zurück zum Zitat Chi Y, Zhang P, Wang F, Lu T, Gu N (2022) Legal judgement prediction of sentence commutation with multi-document information. In: CCF Conference on Computer Supported Cooperative Work and Social Computing, Springer, pp 473–487 Chi Y, Zhang P, Wang F, Lu T, Gu N (2022) Legal judgement prediction of sentence commutation with multi-document information. In: CCF Conference on Computer Supported Cooperative Work and Social Computing, Springer, pp 473–487
Zurück zum Zitat Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:​1412.​3555
Zurück zum Zitat Dar JA, Srivastava KK, Lone SA (2022) Spectral features and optimal hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals. Biomed Signal Process Control 78:103905CrossRef Dar JA, Srivastava KK, Lone SA (2022) Spectral features and optimal hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals. Biomed Signal Process Control 78:103905CrossRef
Zurück zum Zitat Desmet B, Hoste V (2014) Recognising suicidal messages in dutch social media. In: 9th international conference on language resources and evaluation (LREC), pp 830–835 Desmet B, Hoste V (2014) Recognising suicidal messages in dutch social media. In: 9th international conference on language resources and evaluation (LREC), pp 830–835
Zurück zum Zitat de Sa CA, Santos RLdS, Moura RS (2017) An approach for defining the author reputation of comments on products. In: International Conference on Applications of Natural Language to Information Systems, Springer, pp 326–331 de Sa CA, Santos RLdS, Moura RS (2017) An approach for defining the author reputation of comments on products. In: International Conference on Applications of Natural Language to Information Systems, Springer, pp 326–331
Zurück zum Zitat Do PK, Nguyen HT, Tran CX, Nguyen MT, Nguyen ML (2017) Legal question answering using ranking svm and deep convolutional neural network. arXiv preprint arXiv:1703.05320 Do PK, Nguyen HT, Tran CX, Nguyen MT, Nguyen ML (2017) Legal question answering using ranking svm and deep convolutional neural network. arXiv preprint arXiv:​1703.​05320
Zurück zum Zitat Gao S, Young MT, Qiu JX, Yoon HJ, Christian JB, Fearn PA, Tourassi GD, Ramanthan A (2018) Hierarchical attention networks for information extraction from cancer pathology reports. J Am Med Inf Assoc 25(3):321–330CrossRef Gao S, Young MT, Qiu JX, Yoon HJ, Christian JB, Fearn PA, Tourassi GD, Ramanthan A (2018) Hierarchical attention networks for information extraction from cancer pathology reports. J Am Med Inf Assoc 25(3):321–330CrossRef
Zurück zum Zitat Gokhale R, Fasli M (2017) Deploying a co-training algorithm to classify human-rights abuses. In: 2017 International Conference on the Frontiers and Advances in Data Science (FADS), IEEE, pp 108–113 Gokhale R, Fasli M (2017) Deploying a co-training algorithm to classify human-rights abuses. In: 2017 International Conference on the Frontiers and Advances in Data Science (FADS), IEEE, pp 108–113
Zurück zum Zitat Hartmann N, Fonseca E, Shulby C, Treviso M, Rodrigues J, Aluisio S (2017) Portuguese word embeddings: evaluating on word analogies and natural language tasks. arXiv preprint arXiv:1708.06025 Hartmann N, Fonseca E, Shulby C, Treviso M, Rodrigues J, Aluisio S (2017) Portuguese word embeddings: evaluating on word analogies and natural language tasks. arXiv preprint arXiv:​1708.​06025
Zurück zum Zitat He X, Shi S, Geng X, Xu L (2022) Hierarchical attention-based context-aware network for red tide forecasting. Appl Soft Comput 127:109337CrossRef He X, Shi S, Geng X, Xu L (2022) Hierarchical attention-based context-aware network for red tide forecasting. Appl Soft Comput 127:109337CrossRef
Zurück zum Zitat Kanakaraj M, Guddeti RMR (2015) Performance analysis of ensemble methods on twitter sentiment analysis using NLP techniques. In: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), IEEE, pp 169–170 Kanakaraj M, Guddeti RMR (2015) Performance analysis of ensemble methods on twitter sentiment analysis using NLP techniques. In: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), IEEE, pp 169–170
Zurück zum Zitat Kastellec JP (2010) The statistical analysis of judicial decisions and legal rules with classification trees. J Empir Leg Stud 7(2):202–230CrossRef Kastellec JP (2010) The statistical analysis of judicial decisions and legal rules with classification trees. J Empir Leg Stud 7(2):202–230CrossRef
Zurück zum Zitat Krestel R, Fankhauser P, Nejdl W (2009) Latent dirichlet allocation for tag recommendation. In: Proceedings of the third ACM conference on Recommender systems, pp 61–68 Krestel R, Fankhauser P, Nejdl W (2009) Latent dirichlet allocation for tag recommendation. In: Proceedings of the third ACM conference on Recommender systems, pp 61–68
Zurück zum Zitat Kufandirimbwa O, Kuranga C (2012) Towards judicial data mining: arguing for adoption in the judicial system. Online J Phys Environ Sci Res 1(2):15–21 Kufandirimbwa O, Kuranga C (2012) Towards judicial data mining: arguing for adoption in the judicial system. Online J Phys Environ Sci Res 1(2):15–21
Zurück zum Zitat Le TTN, Shirai K, Le Nguyen M, Shimazu A (2015) Extracting indices from Japanese legal documents. Artif Intell Law 23(4):315–344CrossRef Le TTN, Shirai K, Le Nguyen M, Shimazu A (2015) Extracting indices from Japanese legal documents. Artif Intell Law 23(4):315–344CrossRef
Zurück zum Zitat Li X, Chen W, Wang T, Huang W (2017) Target-specific convolutional bi-directional lstm neural network for political ideology analysis. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, Springer, pp 64–72 Li X, Chen W, Wang T, Huang W (2017) Target-specific convolutional bi-directional lstm neural network for political ideology analysis. In: Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, Springer, pp 64–72
Zurück zum Zitat Liu Z, Chen H (2017) A predictive performance comparison of machine learning models for judicial cases. In: 2017 IEEE Symposium series on computational intelligence (SSCI), IEEE, pp 1–6 Liu Z, Chen H (2017) A predictive performance comparison of machine learning models for judicial cases. In: 2017 IEEE Symposium series on computational intelligence (SSCI), IEEE, pp 1–6
Zurück zum Zitat Liu Z, Tu C, Sun M (2019) Legal cause prediction with inner descriptions and outer hierarchies. In: China National Conference on Chinese Computational Linguistics, Springer, pp 573–586 Liu Z, Tu C, Sun M (2019) Legal cause prediction with inner descriptions and outer hierarchies. In: China National Conference on Chinese Computational Linguistics, Springer, pp 573–586
Zurück zum Zitat Loh WY (2011) Classification and regression trees. Wiley interdiscip Rev: Data Min Knowl Discov 1(1):14–23 Loh WY (2011) Classification and regression trees. Wiley interdiscip Rev: Data Min Knowl Discov 1(1):14–23
Zurück zum Zitat Luo B, Feng Y, Xu J, Zhang X, Zhao D (2017) Learning to predict charges for criminal cases with legal basis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Copenhagen, Denmark, pp 2727–2736 Luo B, Feng Y, Xu J, Zhang X, Zhao D (2017) Learning to predict charges for criminal cases with legal basis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Copenhagen, Denmark, pp 2727–2736
Zurück zum Zitat Ma J, Gao W, Joty S, Wong KF (2019) Sentence-level evidence embedding for claim verification with hierarchical attention networks. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, pp 2561–2571 Ma J, Gao W, Joty S, Wong KF (2019) Sentence-level evidence embedding for claim verification with hierarchical attention networks. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, pp 2561–2571
Zurück zum Zitat Mac Kim S, Xu Q, Qu L, Wan S, Paris C (2017) Demographic inference on twitter using recursive neural networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp 471–477 Mac Kim S, Xu Q, Qu L, Wan S, Paris C (2017) Demographic inference on twitter using recursive neural networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp 471–477
Zurück zum Zitat Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New York, NY, USACrossRef Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New York, NY, USACrossRef
Zurück zum Zitat McShane BB, Watson OP, Baker T, Griffith SJ (2012) Predicting securities fraud settlements and amounts: a hierarchical bayesian model of federal securities class action lawsuits. J Empir Leg Stud 9(3):482–510CrossRef McShane BB, Watson OP, Baker T, Griffith SJ (2012) Predicting securities fraud settlements and amounts: a hierarchical bayesian model of federal securities class action lawsuits. J Empir Leg Stud 9(3):482–510CrossRef
Zurück zum Zitat Moens MF (2001) Innovative techniques for legal text retrieval. Artif Intell Law 9(1):29–57CrossRef Moens MF (2001) Innovative techniques for legal text retrieval. Artif Intell Law 9(1):29–57CrossRef
Zurück zum Zitat Obasi CK, Ugwu C (2015) Feature selection and vectorization in legal case documents using chi-square statistical analysis and naïve bayes approaches. IOSR J Comput Eng 17(2):42–50 Obasi CK, Ugwu C (2015) Feature selection and vectorization in legal case documents using chi-square statistical analysis and naïve bayes approaches. IOSR J Comput Eng 17(2):42–50
Zurück zum Zitat Oliveira FLd, Cunha LG (2020) The indicators on the brazilian judiciary: limitations, challenges and the use of technology. Revista Direito GV 16(1) Oliveira FLd, Cunha LG (2020) The indicators on the brazilian judiciary: limitations, challenges and the use of technology. Revista Direito GV 16(1)
Zurück zum Zitat Pavlinek M, Podgorelec V (2017) Text classification method based on self-training and lda topic models. Expert Syst Appl 80:83–93CrossRef Pavlinek M, Podgorelec V (2017) Text classification method based on self-training and lda topic models. Expert Syst Appl 80:83–93CrossRef
Zurück zum Zitat Pelle R, Alcântara C, Moreira VP (2018) A classifier ensemble for offensive text detection. In: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, Association for Computing Machinery, New York, NY, USA, WebMedia ’18, p 237-243 Pelle R, Alcântara C, Moreira VP (2018) A classifier ensemble for offensive text detection. In: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, Association for Computing Machinery, New York, NY, USA, WebMedia ’18, p 237-243
Zurück zum Zitat Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543 Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543
Zurück zum Zitat Rao A, Spasojevic N (2016) Actionable and political text classification using word embeddings and lstm. arXiv preprint arXiv:1607.02501 Rao A, Spasojevic N (2016) Actionable and political text classification using word embeddings and lstm. arXiv preprint arXiv:​1607.​02501
Zurück zum Zitat Remmits Y (2017) Finding the topics of case law: Latent dirichlet allocation on supreme court decisions. PhD thesis, Radboud Universiteit Remmits Y (2017) Finding the topics of case law: Latent dirichlet allocation on supreme court decisions. PhD thesis, Radboud Universiteit
Zurück zum Zitat Rios-Figueroa J (2006) Judicial independence and corruption: An analysis of latin america. Available at SSRN 912924 Rios-Figueroa J (2006) Judicial independence and corruption: An analysis of latin america. Available at SSRN 912924
Zurück zum Zitat Roy D, Dutta M (2022) Optimal hierarchical attention network-based sentiment analysis for movie recommendation. Soc Netw Anal Min 12(1):1–16MathSciNetCrossRef Roy D, Dutta M (2022) Optimal hierarchical attention network-based sentiment analysis for movie recommendation. Soc Netw Anal Min 12(1):1–16MathSciNetCrossRef
Zurück zum Zitat Sannier N, Adedjouma M, Sabetzadeh M, Briand L (2017) An automated framework for detection and resolution of cross references in legal texts. Requir Eng 22(2):215–237CrossRef Sannier N, Adedjouma M, Sabetzadeh M, Briand L (2017) An automated framework for detection and resolution of cross references in legal texts. Requir Eng 22(2):215–237CrossRef
Zurück zum Zitat Sulea OM, Zampieri M, Malmasi S, Vela M, Dinu LP, Van Genabith J (2017a) Exploring the use of text classification in the legal domain. arXiv preprint arXiv:1710.09306 Sulea OM, Zampieri M, Malmasi S, Vela M, Dinu LP, Van Genabith J (2017a) Exploring the use of text classification in the legal domain. arXiv preprint arXiv:​1710.​09306
Zurück zum Zitat Sulea OM, Zampieri M, Vela M, Van Genabith J (2017b) Predicting the law area and decisions of french supreme court cases. arXiv preprint arXiv:1708.01681 Sulea OM, Zampieri M, Vela M, Van Genabith J (2017b) Predicting the law area and decisions of french supreme court cases. arXiv preprint arXiv:​1708.​01681
Zurück zum Zitat Sun C, Zhang Y, Liu X, Wu F (2020) Legal Intelligence: Algorithmic, Data, and Social Challenges. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 2464–2467 Sun C, Zhang Y, Liu X, Wu F (2020) Legal Intelligence: Algorithmic, Data, and Social Challenges. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 2464–2467
Zurück zum Zitat Surden H (2014) Machine learning and law. Wash Law Rev 89:87–115 Surden H (2014) Machine learning and law. Wash Law Rev 89:87–115
Zurück zum Zitat Tamilarasan Ramasamy DJJ (2022) Early risk detection of depression from social media posts using hierarchical attention networks. J Algebr Stat 13(1):483–489 Tamilarasan Ramasamy DJJ (2022) Early risk detection of depression from social media posts using hierarchical attention networks. J Algebr Stat 13(1):483–489
Zurück zum Zitat Tarnpradab S, Liu F, Hua KA (2017) Toward extractive summarization of online forum discussions via hierarchical attention networks. In: The Thirtieth International Flairs Conference Tarnpradab S, Liu F, Hua KA (2017) Toward extractive summarization of online forum discussions via hierarchical attention networks. In: The Thirtieth International Flairs Conference
Zurück zum Zitat Tran OT, Ngo BX, Le Nguyen M, Shimazu A (2014) Automated reference resolution in legal texts. Artif Intell Law 22(1):29–60CrossRef Tran OT, Ngo BX, Le Nguyen M, Shimazu A (2014) Automated reference resolution in legal texts. Artif Intell Law 22(1):29–60CrossRef
Zurück zum Zitat Turian J, Ratinov L, Bengio Y (2010) Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics, Association for Computational Linguistics, pp 384–394 Turian J, Ratinov L, Bengio Y (2010) Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics, Association for Computational Linguistics, pp 384–394
Zurück zum Zitat Wenguan W, Yunwen C, Hua C, Yanneng Z, Huiyu Y (2019) Judicial document intellectual processing using hybrid deep neural networks. J Tsinghua Univ (Sci Technol) 59(7):505–511 Wenguan W, Yunwen C, Hua C, Yanneng Z, Huiyu Y (2019) Judicial document intellectual processing using hybrid deep neural networks. J Tsinghua Univ (Sci Technol) 59(7):505–511
Zurück zum Zitat Xie J, Liu X, Dajun Zeng D (2018) Mining e-cigarette adverse events in social media using bi-lstm recurrent neural network with word embedding representation. J Am Med Inf Assoc 25(1):72–80CrossRef Xie J, Liu X, Dajun Zeng D (2018) Mining e-cigarette adverse events in social media using bi-lstm recurrent neural network with word embedding representation. J Am Med Inf Assoc 25(1):72–80CrossRef
Zurück zum Zitat Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) 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, Association for Computational Linguistics, San Diego, California, pp 1480–1489 Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) 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, Association for Computational Linguistics, San Diego, California, pp 1480–1489
Zurück zum Zitat Zeng Y, Wang R, Zeleznikow J, Kemp E (2007) A knowledge representation model for the intelligent retrieval of legal cases. Int J Law Inf Technol 15(3):299–319CrossRef Zeng Y, Wang R, Zeleznikow J, Kemp E (2007) A knowledge representation model for the intelligent retrieval of legal cases. Int J Law Inf Technol 15(3):299–319CrossRef
Zurück zum Zitat Zhang Z, Robinson D, Tepper J (2018) Detecting hate speech on twitter using a convolution-gru based deep neural network. In: European semantic web conference, Springer, pp 745–760 Zhang Z, Robinson D, Tepper J (2018) Detecting hate speech on twitter using a convolution-gru based deep neural network. In: European semantic web conference, Springer, pp 745–760
Metadaten
Titel
Using attention methods to predict judicial outcomes
verfasst von
Vithor Gomes Ferreira Bertalan
Evandro Eduardo Seron Ruiz
Publikationsdatum
27.12.2022
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence and Law / Ausgabe 1/2024
Print ISSN: 0924-8463
Elektronische ISSN: 1572-8382
DOI
https://doi.org/10.1007/s10506-022-09342-7