A Markov Logic Networks Based Method to Predict Judicial Decisions of Divorce Cases

Li, Jiajing; Zhang, Guoying; Yan, Hongfei; Yu, Longxue; Meng, Tao

doi:10.1109/smartcloud.2018.00029

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…Li et al [74] propose a Markov Logic Networks (MLN) probability model to predict the judicial decision of divorce cases. The authors used 695 418 documents from China Judgments Online (China Judgments Online: wenshu.court.gov.cn/, accessed on 16 September 2022).…”

Section: Private Lawmentioning

confidence: 99%

Survey of Text Mining Techniques Applied to Judicial Decisions Prediction

2022

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This paper reviews the most recent literature on experiments with different Machine Learning, Deep Learning and Natural Language Processing techniques applied to predict judicial and administrative decisions. Among the most outstanding findings, we have that the most used data mining techniques are Support Vector Machine (SVM), K Nearest Neighbours (K-NN) and Random Forest (RF), and in terms of the most used deep learning techniques, we found Long-Term Memory (LSTM) and transformers such as BERT. An important finding in the papers reviewed was that the use of machine learning techniques has prevailed over those of deep learning. Regarding the place of origin of the research carried out, we found that 64% of the works belong to studies carried out in English-speaking countries, 8% in Portuguese and 28% in other languages (such as German, Chinese, Turkish, Spanish, etc.). Very few works of this type have been carried out in Spanish-speaking countries. The classification criteria of the works have been based, on the one hand, on the identification of the classifiers used to predict situations (or events with legal interference) or judicial decisions and, on the other hand, on the application of classifiers to the phenomena regulated by the different branches of law: criminal, constitutional, human rights, administrative, intellectual property, family law, tax law and others. The corpus size analyzed in the reviewed works reached 100,000 documents in 2020. Finally, another important finding lies in the accuracy of these predictive techniques, reaching predictions of over 60% in different branches of law.

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Section: Private Lawmentioning

confidence: 99%

Survey of Text Mining Techniques Applied to Judicial Decisions Prediction

2022

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“…Research on LJP has been studied for decades. Early researches relied primarily on hand-craft features and applied statistical [1], [2] or machine learning methods [3]- [5], [32] for it. Due to limitations of data, they focused on a tiny subset of case categories.…”

Section: Related Workmentioning

confidence: 99%

“…Earlier works solved these sub-tasks as independent text classification problems, while ignoring the close dependencies among different sub-tasks [1]- [5]. Recently, many works have demonstrated benefits of modelling such dependencies.…”

Section: Introductionmentioning

confidence: 99%

Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer

Yunyun¹,

Shen²,

Li³

et al. 2021

Preprint

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Given the fact of a case, Legal Judgment Prediction (LJP) involves a series of sub-tasks such as predicting violated law articles, charges and term of penalty. We propose leveraging a unified text-to-text Transformer for LJP, where the dependencies among sub-tasks can be naturally established within the auto-regressive decoder. Compared with previous works, it has three advantages: (1) it fits in the pretraining pattern of masked language models, and thereby can benefit from the semantic prompts of each sub-task rather than treating them as atomic labels, (2) it utilizes a single unified architecture, enabling full parameter sharing across all sub-tasks, and (3) it can incorporate both classification and generative sub-tasks. We show that this unified transformer, albeit pretrained on general-domain text, outperforms pretrained models tailored specifically for the legal domain. Through an extensive set of experiments, we find that the best order to capture dependencies is different from human intuitions, and the most reasonable logical order for humans can be sub-optimal for the model. We further include two more auxiliary tasks: court view generation and article content prediction, showing they can not only improve the prediction accuracy, but also provide interpretable explanations for model outputs even when an error is made. With the best configuration, our model outperforms both previous SOTA and a single-tasked version of the unified transformer by a large margin. Code and dataset are available at https://github.com/oli-yun/Dependency-LJP.

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“…A similar study obtained N-gram features from the case text for the Supreme Court of the United States and the United States Circuit Court and built ML and deep learning (DL) models to predict those appellate affirm or reverse decisions, and a convolutional neural network (CNN) model for district-to-circuit reversal prediction outperformed other models [6]. In addition, Li et al used NLP techniques to build a knowledge extraction engine and obtain a database, proposing a Markov logic network to predict the judicial decision of divorce cases [7]. Furthermore, Jiang et al used a deep reinforcement learning method to extract rationales from input text and implemented a charge prediction task [8].…”

Section: Introductionmentioning

confidence: 99%

Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases

2021

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The discretionary damage of mental suffering in fatal car accident cases in Taiwan is subjective, uncertain, and unpredictable; thus, plaintiffs, defendants, and their lawyers find it difficult to judge whether spending much of their money and time on the lawsuit is worthwhile and which legal factors judges will consider important and dominant when they are assessing the mental suffering damages. To address these problems, we propose k-nearest neighbor, classification and regression trees, and random forests as learning algorithms for regression to build optimal predictive models. In addition, we reveal the importance ranking of legal factors by permutation feature importance. The experimental results show that the random forest model outperformed the other models and achieved good performance, and “the mental suffering damages that plaintiff claims” and “the age of the victim” play important roles in assessments of mental suffering damages in fatal car accident cases in Taiwan. Therefore, litigants and their lawyers can predict the discretionary damages of mental suffering in advance and wisely decide whether they should litigate or not, and then they can focus on the crucial legal factors and develop the best litigation strategy.

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A Markov Logic Networks Based Method to Predict Judicial Decisions of Divorce Cases

Cited by 12 publications

References 4 publications

Survey of Text Mining Techniques Applied to Judicial Decisions Prediction

Survey of Text Mining Techniques Applied to Judicial Decisions Prediction

Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer

Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases

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