Relevant bibliographies by topics / Legal Judgment Prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Legal Judgment Prediction'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Legal Judgment Prediction"

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Zhong, Haoxi, Yuzhong Wang, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun. "Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1250–57. http://dx.doi.org/10.1609/aaai.v34i01.5479.

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Legal Judgment Prediction (LJP) aims to predict judgment results according to the facts of cases. In recent years, LJP has drawn increasing attention rapidly from both academia and the legal industry, as it can provide references for legal practitioners and is expected to promote judicial justice. However, the research to date usually suffers from the lack of interpretability, which may lead to ethical issues like inconsistent judgments or gender bias. In this paper, we present QAjudge, a model based on reinforcement learning to visualize the prediction process and give interpretable judgments. QAjudge follows two essential principles in legal systems across the world: Presumption of Innocence and Elemental Trial. During inference, a Question Net will select questions from the given set and an Answer Net will answer the question according to the fact description. Finally, a Predict Net will produce judgment results based on the answers. Reward functions are designed to minimize the number of questions asked. We conduct extensive experiments on several real-world datasets. Experimental results show that QAjudge can provide interpretable judgments while maintaining comparable performance with other state-of-the-art LJP models. The codes can be found from https://github.com/thunlp/QAjudge.
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Chen, Junyi, Lan Du, Ming Liu, and 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, no. 4 (July 31, 2022): 1–15. http://dx.doi.org/10.1145/3503157.

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Legal judgment prediction (LJP) is used to predict judgment results based on the description of individual legal cases. In order to be more suitable for actual application scenarios in which the case has cited multiple articles and has multiple charges, we formulate legal judgment prediction as a multiple label learning problem and present a deep learning model that can effectively encode the content of each legal case via a multi-residual convolution neural network and the semantics of law articles via an article encoder. An article-wise attention mechanism is proposed to fuse the two types of encoded information. Experimental results derived on the CAIL2018 datasets show that our model provides a significant performance improvement over the existing neural models in predicting relevant law articles and charges.
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Shang, Xuerui. "A Computational Intelligence Model for Legal Prediction and Decision Support." Computational Intelligence and Neuroscience 2022 (June 24, 2022): 1–8. http://dx.doi.org/10.1155/2022/5795189.

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Legal judgment prediction (LJP) and decision support aim to enable machines to predict the verdict of legal cases after reading the description of facts, which is an application of artificial intelligence in the legal field. This paper proposes a legal judgment prediction model based on process supervision for the sequential dependence of each subtask in the legal judgment prediction task. Experimental results verify the effectiveness of the model framework and process monitoring mechanism adopted in this model. First, the convolutional neural network (CNN) algorithm was used to extract text features, and the principal component analysis (PCA) algorithm was used to reduce the dimension of data features. Next, the prediction model based on process supervision is proposed for the first time. When modeling the dependency relationship between sequential sub-data sets, process supervision is introduced to ensure the accuracy of the obtained dependency information, and genetic algorithm (GA) is introduced to optimize the parameters so as to improve the final prediction performance. Compared to our benchmark method, our algorithm achieved the best results on four different legal open data sets (CAIL2018_Small, CAIL2018_Large, CAIL2019_Small, and CAIL2019_Large). The realization of automatic prediction of legal judgment can not only assist judges, lawyers, and other professionals to make more efficient legal judgment but also provide legal aid for people who lack legal expertise.
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Zhu, Kongfan, Rundong Guo, Weifeng Hu, Zeqiang Li, and Yujun Li. "Legal Judgment Prediction Based on Multiclass Information Fusion." Complexity 2020 (October 26, 2020): 1–12. http://dx.doi.org/10.1155/2020/3089189.

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Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.
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Gan, Leilei, Kun Kuang, Yi Yang, and Fei Wu. "Judgment Prediction via Injecting Legal Knowledge into Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12866–74. http://dx.doi.org/10.1609/aaai.v35i14.17522.

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Legal Judgment Prediction (LJP) is a key problem in legal artificial intelligence, which is aimed to predict a law case's judgment based on a given text describing the facts of the law case. Most of the previous work treats LJP as a text classification task and generally adopts deep neural networks (DNNs) based methods to solve it. However, existing DNNs based work is data-hungry and hard to explain which legal knowledge is based on to make such a prediction. Thus, injecting legal knowledge into neural networks to interpret the model and improve performance remains a significant problem. In this paper, we propose to represent declarative legal knowledge as a set of first-order logic rules and integrate these logic rules into a co-attention network-based model explicitly. The use of logic rules enhances neural networks with explicit logical reason capabilities and makes the model more interpretable. We take the civil loan scenario as a case study and demonstrate the effectiveness of the proposed method through comprehensive experiments and analysis conducted on the collected dataset.
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Lyu, Yougang, Zihan Wang, Zhaochun Ren, Pengjie Ren, Zhumin Chen, Xiaozhong Liu, Yujun Li, Hongsong Li, and Hongye Song. "Improving legal judgment prediction through reinforced criminal element extraction." Information Processing & Management 59, no. 1 (January 2022): 102780. http://dx.doi.org/10.1016/j.ipm.2021.102780.

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Ma, Wenqing. "Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network." Mobile Information Systems 2022 (October 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/4636485.

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With the arrival of the third revolution of artificial intelligence, the applications of artificial intelligence in the fields of automatic driving, image recognition, smart home, machine translation, medical services, e-sports, and so on can be seen everywhere, and topics about artificial intelligence are constantly emerging. Since 2017, the discussion on artificial intelligence in the field of law has become more and more active. In this context, the application of artificial intelligence in the field of legal judgment and the hypothetical system based on this technology in court judgment has also become the object of discussion from time to time. In this paper, based on the artificial intelligence decision-making method of the deep neural network, aiming at the three subtasks of legal judgment prediction, namely, crime prediction, law recommendation, and sentence prediction, a multi-task judgment prediction model BERT12multi and a sentence interval prediction model BERT-Text CNN are proposed, which improve the prediction accuracy and adopt the knowledge distillation strategy to compress the model parameters and improve the reasoning speed of the judgment model. Experiments on the CAIL2018 data set show that the performance of the deep neural network model in crime prediction and law recommendation tasks can be significantly improved by adopting the pre training model adaptive training, grouping focus loss, and gradient confrontation training strategies. Using a step-by-step sentence prediction strategy can realize the weight sharing of pre training model and make use of the prediction results of charges and laws in sentence prediction. The recall training-prediction strategy can avoid error accumulation and improve the accuracy of sentence prediction. By integrating the artificial intelligence decision-making method, the case reasoning speed can be greatly improved, the highest compressible model volume can be about 11% of the original one, and the reasoning speed can be increased by about 8 times. At the same time, performance close to that of the deep neural model can be obtained, which is superior to other legal decision prediction models based on word embedding.
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Zhang, Hu, Bangze Pan, and Ru Li. "Legal Judgment Elements Extraction Approach with Law Article-aware Mechanism." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 3 (May 31, 2022): 1–15. http://dx.doi.org/10.1145/3485244.

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Legal judgment elements extraction (LJEE) aims to identify the different judgment features from the fact description in legal documents automatically, which helps to improve the accuracy and interpretability of the judgment results. In real court rulings, judges usually need to scan both the fact descriptions and the law articles repeatedly to find out the relevant information, and it is hard to acquire the key judgment features quickly, so legal judgment elements extraction is a crucial and challenging task for legal judgment prediction. However, most existing methods follow the text classification framework, which fails to model the attentive relations of the law articles and the legal judgment elements. To address this issue, we simulate the working process of human judges, and propose a legal judgment elements extraction method with a law article-aware mechanism, which captures the complex semantic correlations of the law article and the legal judgment elements. Experimental results show that our proposed method achieves significant improvements than other state-of-the-art baselines on the element recognition task dataset. Compared with the BERT-CNN model, the proposed “All labels Law Articles Embedding Model (ALEM)” improves the accuracy, recall, and F1 value by 0.5, 1.4 and 1.0, respectively.
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Li, Shang, Hongli Zhang, Lin Ye, Xiaoding Guo, and 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.

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He, Congqing, Tien-Ping Tan, Xiaobo Zhang, and 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.

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Dissertations / Theses on the topic "Legal Judgment Prediction"

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Liu, Yi-Hung, and 劉譯閎. "Judgment Retrieval and Statute Prediction for Legal Problems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/73683757790668962089.

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博士
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.
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Raj, Rohit. "Towards Robustness of Neural Legal Judgement System." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6145.

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Legal Judgment Prediction (LJP) implements Natural Language Processing (NLP) techniques to predict judgment results based on fact description. It can play a vital role as a legal assistant and benefit legal practitioners and regular citizens. Recently, the rapid advances in transformer- based pre-trained language models led to considerable improvement in this area. However, empirical results show that existing LJP systems are not robust to adversaries and noise. Also, they cannot handle large-length legal documents. In this work, we explore the robustness and efficiency of LJP systems even in a low data regime. In the first part, we empirically verify that existing state-of-the-art LJP systems are not robust. We further provide our novel architecture for LJP tasks which can handle extensive text lengths and adversarial examples. Our model performs better than state-of-the-art models, even in the presence of adversarial examples of the legal domain. In the second part, we investigate the approach for the LJP system in a low data regime. We further divide our second work into two scenarios depending on the number of unseen classes in the dataset which is being used for the LJP system. In the first scenario, we propose a few-shot approach with only two labels for the Judgement prediction task. In the second scenario, we propose an approach where we have an excessive number of labels for judgment prediction. For both approaches, we provide novel architectures using few-shot learning that are also robust to adversaries. We conducted extensive experiments on American, European, and Indian legal datasets in the few-shot scenario. Though trained using the few-shot approach, our models perform comparably to state-of-the-art models that are trained using large datasets in the legal domain.
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Book chapters on the topic "Legal Judgment Prediction"

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Long, Shangbang, Cunchao Tu, Zhiyuan Liu, and Maosong Sun. "Automatic Judgment Prediction via Legal Reading Comprehension." In Lecture Notes in Computer Science, 558–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32381-3_45.

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Zhao, Lili, Linan Yue, Yanqing An, Ye Liu, Kai Zhang, Weidong He, Yanmin Chen, Senchao Yuan, and Qi Liu. "Legal Judgment Prediction with Multiple Perspectives on Civil Cases." In Artificial Intelligence, 712–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93046-2_60.

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Wu, Tien-Hsuan, Ben Kao, Anne S. Y. Cheung, Michael M. K. Cheung, Chen Wang, Yongxi Chen, Guowen Yuan, and Reynold Cheng. "Integrating Domain Knowledge in AI-Assisted Criminal Sentencing of Drug Trafficking Cases." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200861.

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Judgment prediction is the task of predicting various outcomes of legal cases of which sentencing prediction is one of the most important yet difficult challenges. We study the applicability of machine learning (ML) techniques in predicting prison terms of drug trafficking cases. In particular, we study how legal domain knowledge can be integrated with ML models to construct highly accurate predictors. We illustrate how our criminal sentence predictors can be applied to address four important issues in legal knowledge management, which include (1) discovery of model drifts in legal rules, (2) identification of critical features in legal judgments, (3) fairness in machine predictions, and (4) explainability of machine predictions.
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Petrova, Alina, John Armour, and Thomas Lukasiewicz. "Extracting Outcomes from Appellate Decisions in US State Courts." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200857.

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Predicting the outcome of a legal process has recently gained considerable research attention. Numerous attempts have been made to predict the exact outcome, judgment, charge, and fines of a case given the textual description of its facts and metadata. However, most of the effort has been focused on Chinese and European law, for which there exist annotated datasets. In this paper, we introduce CASELAW4 — a new dataset of 350k common law judicial decisions from the U.S. Caselaw Access Project, of which 250k have been automatically annotated with binary outcome labels of AFFIRM or REVERSE by our hybrid learning system. To our knowledge, it is the first attempt to perform outcome extraction (a) on such a large volume of English-language judicial opinions, (b) on the Caselaw Access Project data, and (c) on US State Courts of Appeal cases, and it paves the way to large-scale outcome prediction and advanced legal analytics using U.S. Case Law. We set up baseline results for the outcome extraction task on the new dataset, achieving an F-measure of 82.32%.
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Hildebrandt, Mireille. "Boundary Work between Computational ‘Law’ and ‘Law-as-We-Know-it’." In Data at the Boundaries of European Law, 30—C2N118. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780198874195.003.0002.

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Abstract This chapter enquires into the use of big data analytics and prediction of judgment to inform both law and legal decision-making. The main argument is that the use of data-driven ‘legal technologies’ may transform the ‘mode of existence’ of law as-we-know-it, whose characteristics depend on its text-based nature. To explain why and how computational ‘law’ would be different, the author deciphers the mathematical assumptions of machine learning and natural language processing, opening the black box of algorithmic ‘insights’ at the level of its underlying research design. This allows her to compare the force of such computational ‘law’ with the force of law as-we-know-it. She then identifies some of the challenges as to legal protection, demonstrating the need for ‘by design’ approaches to anchor rule of law safeguards in the architecture of computational ‘law’, clarifying how and why ‘legal protection by design’ is not equivalent with ‘legal by design’ or ‘techno-regulation’.
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Piechowski, Lisa Drago. "Evaluating Workplace Disability." In The Oxford Handbook of Psychology and Law, 223—C13P82. Oxford University Press, 2023. http://dx.doi.org/10.1093/oxfordhb/9780197649138.013.13.

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Abstract Forensic disability evaluations occur in different contexts, each with its own legal standards. The common basis is establishing the presence of a valid psychological condition, assessing the functional impairments that derive from that condition, and comparing those impairments with the examinee’s work demands. This requires drawing a connection between the manifestations of the individual’s mental health condition and specific impairments in their functioning, then extrapolating these functional impairments to the requirements of their job. Research has begun to identify factors relevant to understanding these connections. However, the methodology for applying these factors to predict outcomes in individual cases has not yet been developed. Additional research is needed to identify specific methods and practices associated with more accurate outcomes in disability evaluations. The development of an actuarial model of prediction for return to work could improve the accuracy of disability assessments beyond reliance on clinical judgment.
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Contini, Alessandro, Sebastiano Piccolo, Lucia Lopez Zurita, and Urska Sadl. "Recognising Legal Characteristics of the Judgments of the European Court of Justice: Difficult but Not Impossible." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220461.

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Machine learning has improved significantly during the past decades. Computers perform remarkably in formerly difficult tasks. This article reports the preliminary results on the prediction of two characteristics of judgments of the European Court of Justice, which require the knowledge of concepts and doctrines of European Union law and judicial decision-making: The legal importance (doctrinal outcome) and leeway to the national courts and legislators (deference). The analysis relies on 1704 manually labelled judgments and trains a set of classifiers based on word embedding, LSTM, and convolutional neural networks. While all classifiers exceed simple baselines, the overall performance is weak. This suggests first, that the models learn meaningful representations of the judgments. Second, machine learning encounters significant challenges in the legal domain. These arise doe to the small training data, significant class imbalance, and the characteristics of the variables requiring external knowledge. The article also outlines directions for future research.
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"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)." In Legal Method and Reasoning, 231. Routledge-Cavendish, 2012. http://dx.doi.org/10.4324/9781843145103-176.

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Conference papers on the topic "Legal Judgment Prediction"

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Niklaus, Joel, Ilias Chalkidis, and Matthias Stürmer. "Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark." In 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.

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Chalkidis, Ilias, Ion Androutsopoulos, and Nikolaos Aletras. "Neural Legal Judgment Prediction in English." In 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.

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Chen, Long, Nuo Xu, and Yue Wang. "Legal Judgment Prediction with Label Dependencies." In 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.

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Dong, Qian, and Shuzi Niu. "Legal Judgment Prediction via Relational Learning." In 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.

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Zhong, Haoxi, Zhipeng Guo, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, and Maosong Sun. "Legal Judgment Prediction via Topological Learning." In 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.

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Almuslim, Intisar, and Diana Inkpen. "Legal Judgment Prediction for Canadian Appeal Cases." In 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA). IEEE, 2022. http://dx.doi.org/10.1109/cdma54072.2022.00032.

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Xu, Nuo, Pinghui Wang, Long Chen, Li Pan, Xiaoyan Wang, and Junzhou Zhao. "Distinguish Confusing Law Articles for Legal Judgment Prediction." In 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.

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Feng, Yi, Chuanyi Li, and Vincent Ng. "Legal Judgment Prediction via Event Extraction with Constraints." In 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.

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Liu, Yifei, Yiquan Wu, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu, and Kun Kuang. "ML-LJP: Multi-Law Aware Legal Judgment Prediction." In 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.

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Yang, Wenmian, Weijia Jia, Xiaojie Zhou, and Yutao Luo. "Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network." In 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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The Legal Judgment Prediction (LJP) is to determine judgment results based on the fact descriptions of the cases. LJP usually consists of multiple subtasks, such as applicable law articles prediction, charges prediction, and the term of the penalty prediction. These multiple subtasks have topological dependencies, the results of which affect and verify each other. However, existing methods use dependencies of results among multiple subtasks inefficiently. Moreover, for cases with similar descriptions but different penalties, current methods cannot predict accurately because the word collocation information is ignored. In this paper, we propose a Multi-Perspective Bi-Feedback Network with the Word Collocation Attention mechanism based on the topology structure among subtasks. Specifically, we design a multi-perspective forward prediction and backward verification framework to utilize result dependencies among multiple subtasks effectively. To distinguish cases with similar descriptions but different penalties, we integrate word collocations features of fact descriptions into the network via an attention mechanism. The experimental results show our model achieves significant improvements over baselines on all prediction tasks.
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