Using machine learning to create a repository of judgments concerning a new practice area: a case study in animal protection law

Watson, Joe; Aglionby, Guy; March, Samuel A.

doi:10.1007/s10506-022-09313-y

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…We employed advanced techniques for sentiment analysis of Canadian marine case law papers, including deep learning and traditional machine learning models. This analytical approach extends beyond statistics, offering valuable insights for informed decision making in judge selection and jury verdicts [29]. Integrating technology into legal scholarship reflects a proactive response to contemporary challenges, enhancing the adaptability of legal practices.…”

Section: Accuracy = Correct Predictions Total Predictionsmentioning

confidence: 99%

Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model

Abimbola,

de La Cal Marin,

Tan

2024

MAKE

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This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytic policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN)- and long short-term memory (LSTM)-based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, conventional machine learning techniques such as support vector machine (SVM) yield an accuracy rate of 52.57%, naïve Bayes at 57.44%, and logistic regression at 61.86%. The superior accuracy of the CNN and STM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession.

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Section: Accuracy = Correct Predictions Total Predictionsmentioning

confidence: 99%

Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model

Abimbola,

de La Cal Marin,

Tan

2024

MAKE

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“…The specificity of the analysis of legal texts is because this text refers to positive legal norms (Ashley, K. D., 2021;Watson, 2022;Nay, 2021). This standard is general and abstract.…”

Section: Introductionmentioning

confidence: 99%

Model for Data Training Judge’s Decisions in Corruption Cases in Indonesia

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Data is one of the supporting elements for decision-making in all scientific fields, including in legal dogmatics. As a practical science, legal dogmatics is responsible for responding to legal problems in societies, particularly through court decisions. In the Indonesian legal tradition, which is heavily influenced by the civil law system, judges in charge of deciding cases do not always use references in the form of precedents drawn from similar case decisions. Situations like this often attract criticism as they are likely to cause inconsistencies in court decision-making and will ultimately reduce public confidence in the courts. This paper offers a way to overcome this tendency and aims to model judgment decisions from cases that are related to cases through data mining with the decision tree method. The methods used are Decision Tree ID3, with data derived from decisions on corruption cases decided by the Corruption Court in Indonesia over the past ten years. This data-training model has shown variables that can be used as data-training that really needs consistency in decision making in corruption cases in Indonesia. Considering that the judges' decisions are the result of factual as well as normative reasoning, there are many elements that must be anticipated. The judge's inconsistency in interpreting the facts results in the judge's interpretation of the elements of the crime. This disparity is the source of the weakness of the model that is currently presented in this study. One way to overcome this is to increase the data variance. If the researcher only uses duplicate data from the previous training data, the end result will not be very different.

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A neural network to identify requests, decisions, and arguments in court rulings on custody

Muñoz-Soro,

del Hoyo Alonso,

Montañes

et al. 2024

Artif Intell Law

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Court rulings are among the most important documents in all legal systems. This article describes a study in which natural language processing is used for the automatic characterization of Spanish judgments that deal with the physical custody (joint or individual) of minors. The model was trained to identify a set of elements: the type of custody requested by the plaintiff, the type of custody decided on by the court, and eight of the most commonly used arguments in this type of judgment. Two jurists independently annotated more than 3000 judgments, which were used to train a model based on transformers. The main difficulties encountered in this task were the complexity of the judicial language and the need to work with appellate court rulings that have a more complicated structure than decisions at first instance. For the complete court rulings, the F1 score of the inter-annotator agreement ranged from 0.60 to 0.86 and the Kappa index from 0.33 to 0.73. The F1 score of the agreement between the model and the annotators ranged from 0.66 to 0.93 and the Kappa index from 0.57 to 0.80. These results in which the model performance exceeds even the inter-annotator agreement show the high ability of transformers to identify abstract entities in legal texts.

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Using machine learning to create a repository of judgments concerning a new practice area: a case study in animal protection law

Cited by 8 publications

References 32 publications

Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model

Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model

Model for Data Training Judge’s Decisions in Corruption Cases in Indonesia

A neural network to identify requests, decisions, and arguments in court rulings on custody

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