Literatura académica sobre el tema "Legal Judgment Prediction"
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Artículos de revistas sobre el tema "Legal Judgment Prediction"
Zhong, Haoxi, Yuzhong Wang, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu y Maosong Sun. "Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 01 (3 de abril de 2020): 1250–57. http://dx.doi.org/10.1609/aaai.v34i01.5479.
Texto completoChen, Junyi, Lan Du, Ming Liu y Xiabing Zhou. "Mulan: A Multiple Residual Article-Wise Attention Network for Legal Judgment Prediction". ACM Transactions on Asian and Low-Resource Language Information Processing 21, n.º 4 (31 de julio de 2022): 1–15. http://dx.doi.org/10.1145/3503157.
Texto completoShang, Xuerui. "A Computational Intelligence Model for Legal Prediction and Decision Support". Computational Intelligence and Neuroscience 2022 (24 de junio de 2022): 1–8. http://dx.doi.org/10.1155/2022/5795189.
Texto completoZhu, Kongfan, Rundong Guo, Weifeng Hu, Zeqiang Li y Yujun Li. "Legal Judgment Prediction Based on Multiclass Information Fusion". Complexity 2020 (26 de octubre de 2020): 1–12. http://dx.doi.org/10.1155/2020/3089189.
Texto completoGan, Leilei, Kun Kuang, Yi Yang y Fei Wu. "Judgment Prediction via Injecting Legal Knowledge into Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 14 (18 de mayo de 2021): 12866–74. http://dx.doi.org/10.1609/aaai.v35i14.17522.
Texto completoLyu, Yougang, Zihan Wang, Zhaochun Ren, Pengjie Ren, Zhumin Chen, Xiaozhong Liu, Yujun Li, Hongsong Li y Hongye Song. "Improving legal judgment prediction through reinforced criminal element extraction". Information Processing & Management 59, n.º 1 (enero de 2022): 102780. http://dx.doi.org/10.1016/j.ipm.2021.102780.
Texto completoMa, Wenqing. "Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network". Mobile Information Systems 2022 (11 de octubre de 2022): 1–9. http://dx.doi.org/10.1155/2022/4636485.
Texto completoZhang, Hu, Bangze Pan y Ru Li. "Legal Judgment Elements Extraction Approach with Law Article-aware Mechanism". ACM Transactions on Asian and Low-Resource Language Information Processing 21, n.º 3 (31 de mayo de 2022): 1–15. http://dx.doi.org/10.1145/3485244.
Texto completoLi, Shang, Hongli Zhang, Lin Ye, Xiaoding Guo y Binxing Fang. "MANN: A Multichannel Attentive Neural Network for Legal Judgment Prediction". IEEE Access 7 (2019): 151144–55. http://dx.doi.org/10.1109/access.2019.2945771.
Texto completoHe, Congqing, Tien-Ping Tan, Xiaobo Zhang y Sheng Xue. "Knowledge-Enriched Multi-Cross Attention Network for Legal Judgment Prediction". IEEE Access 11 (2023): 87571–82. http://dx.doi.org/10.1109/access.2023.3305259.
Texto completoTesis sobre el tema "Legal Judgment Prediction"
Liu, Yi-Hung y 劉譯閎. "Judgment Retrieval and Statute Prediction for Legal Problems". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/73683757790668962089.
Texto completo國立中央大學
資訊管理學系
103
Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although a few previous studies focused on assisting professionals in the retrieval of related legal documents, to our knowledge, they did not take into account the general public and their difficulty in describing legal problems in professional legal terms and could not provide relevant statutes to the general public using problem statements. In this dissertation, we formulate two research topics: judgment retrieval and statute prediction using the unique characteristics of legal documents. In the first research topic, we design a text mining based method that allows the general public to use everyday vocabulary to search for and retrieve criminal judgments. Then we present an innovative approach, the three-phase prediction (TPP) algorithm, which enables laypeople to use daily vocabulary to describe their problems and find pertinent statutes for their cases. There are two experiments to validate our proposed research methods. The first experimental study compares the performances of traditional TF-IDF method and our judgment retrieval approach through a survey. The second one is based on the statute prediction problem, and four state of the art retrieval functions including Cosine similarity, Pearson correlation coefficient, Spearman's correlation coefficient and TF-IDF methods are compared with TPP. Both proposed methods have been verified for accuracy and effectiveness by using Chinese Criminal Code judgments. The results show that the proposed methods are accurate and they are more advantageous than traditional methods.
Raj, Rohit. "Towards Robustness of Neural Legal Judgement System". Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6145.
Texto completoCapítulos de libros sobre el tema "Legal Judgment Prediction"
Long, Shangbang, Cunchao Tu, Zhiyuan Liu y Maosong Sun. "Automatic Judgment Prediction via Legal Reading Comprehension". En Lecture Notes in Computer Science, 558–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32381-3_45.
Texto completoZhao, Lili, Linan Yue, Yanqing An, Ye Liu, Kai Zhang, Weidong He, Yanmin Chen, Senchao Yuan y Qi Liu. "Legal Judgment Prediction with Multiple Perspectives on Civil Cases". En Artificial Intelligence, 712–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93046-2_60.
Texto completoWu, Tien-Hsuan, Ben Kao, Anne S. Y. Cheung, Michael M. K. Cheung, Chen Wang, Yongxi Chen, Guowen Yuan y Reynold Cheng. "Integrating Domain Knowledge in AI-Assisted Criminal Sentencing of Drug Trafficking Cases". En Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200861.
Texto completoPetrova, Alina, John Armour y Thomas Lukasiewicz. "Extracting Outcomes from Appellate Decisions in US State Courts". En Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200857.
Texto completoHildebrandt, Mireille. "Boundary Work between Computational ‘Law’ and ‘Law-as-We-Know-it’". En Data at the Boundaries of European Law, 30—C2N118. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780198874195.003.0002.
Texto completoPiechowski, Lisa Drago. "Evaluating Workplace Disability". En The Oxford Handbook of Psychology and Law, 223—C13P82. Oxford University Press, 2023. http://dx.doi.org/10.1093/oxfordhb/9780197649138.013.13.
Texto completoContini, Alessandro, Sebastiano Piccolo, Lucia Lopez Zurita y Urska Sadl. "Recognising Legal Characteristics of the Judgments of the European Court of Justice: Difficult but Not Impossible". En Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220461.
Texto completo"general rule from particular cases and is inconclusive which suggests the end processes of legal judgments are inconclusive. However, when it is, the courts ensure that inconclusive reasoning can be enforced! Like deductive reasoning, the logic of inductive reasoning has no interest in the actual truth of the propositions that are the premises or the conclusion. Just because a logical form is correctly constructed, it does not mean that the conclusion expressed is true. The truth of a conclusion depends upon whether the major and minor premises express statements that are true. The statements may be false. Much time is spent by lawyers in court attempting to prove the truth of statements used as building blocks in the construction of arguments. In an inductive argument, the premises only tend to support the conclusions, but they do not compel the conclusion. By tradition, the study of inductive logic was kept to arguments by way of analogy, or methods of generalisation, on the basis of a finite number of observations. Argument by analogy is the most common form of argument in law. Such an argument begins by stating that two objects are observed to be similar by a number of attributes. It is concluded that the two objects are similar with respect to a third. The strength of such an argument depends upon the degree of relationship. Lawyers are advisers and they offer predictive advice based on how previous similar cases have been dealt with. All advice is based on the lawyers’ perception of what would happen in court; this is usually enough to ensure that, in the vast majority of civil cases, matters between disputants are settled. The lawyers’ perception is based upon their experience of how judges reason. Although deductive reasoning lends support to the Blackstonian theory that the law is always there to be found, there is room for the judge to exercise discretion. A judge will have to find the major premise. The judge may do this by looking at statutes or precedent. In the absence of statute, precedent or custom, he or she may need to create one by analogy or a process of induction. Once the judge has stated the major premise the judge will need to examine the facts of the case to ascertain if they are governed by the major premise. If this has been established, the conclusion will follow syllogistically. In the vast majority of cases, the conclusion will simply be an application of existing law to the facts. Occasionally, the decision creates a new law which may or may not be stated as a proposition of law. To ascertain whether a new law has been stated may require a comparison between the material facts implied within the major premise and the facts which make up the minor premise. To summarise, judges are involved in a type of inductive reasoning called reasoning by analogy. This is a process of reasoning by comparing examples. The purpose is to reach a conclusion in a novel situation. This process has been described as a three stage process: (1) the similarity between the cases is observed; (2) the rule of law (ratio decidendi) inherent in the first case is stated. Reasoning is from the particular to the general (deduction); (3) that rule is applied to the case for decision. At this point, reasoning is from the general to the particular (induction)." En Legal Method and Reasoning, 231. Routledge-Cavendish, 2012. http://dx.doi.org/10.4324/9781843145103-176.
Texto completoActas de conferencias sobre el tema "Legal Judgment Prediction"
Niklaus, Joel, Ilias Chalkidis y Matthias Stürmer. "Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark". En Proceedings of the Natural Legal Language Processing Workshop 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.nllp-1.3.
Texto completoChalkidis, Ilias, Ion Androutsopoulos y Nikolaos Aletras. "Neural Legal Judgment Prediction in English". En Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1424.
Texto completoChen, Long, Nuo Xu y Yue Wang. "Legal Judgment Prediction with Label Dependencies". En 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). IEEE, 2020. http://dx.doi.org/10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00070.
Texto completoDong, Qian y Shuzi Niu. "Legal Judgment Prediction via Relational Learning". En SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3404835.3462931.
Texto completoZhong, Haoxi, Zhipeng Guo, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu y Maosong Sun. "Legal Judgment Prediction via Topological Learning". En Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/d18-1390.
Texto completoAlmuslim, Intisar y Diana Inkpen. "Legal Judgment Prediction for Canadian Appeal Cases". En 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA). IEEE, 2022. http://dx.doi.org/10.1109/cdma54072.2022.00032.
Texto completoXu, Nuo, Pinghui Wang, Long Chen, Li Pan, Xiaoyan Wang y Junzhou Zhao. "Distinguish Confusing Law Articles for Legal Judgment Prediction". En Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.280.
Texto completoFeng, Yi, Chuanyi Li y Vincent Ng. "Legal Judgment Prediction via Event Extraction with Constraints". En Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.48.
Texto completoLiu, Yifei, Yiquan Wu, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu y Kun Kuang. "ML-LJP: Multi-Law Aware Legal Judgment Prediction". En SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3539618.3591731.
Texto completoYang, Wenmian, Weijia Jia, Xiaojie Zhou y Yutao Luo. "Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/567.
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