A Comprehensive Review of Semantic Text Similarity for Developing Intelligent Judicial Recommendation Systems
Subject Areas : Multimedia Processing, Communications Systems, Intelligent SystemsNasim Gereilinia 1 , Neda Abdolvand 2 , Zahra Rezaei 3 , ّFarzad Movahedi Sobhani 4
1 - PhD Student, Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Associate Professor, Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.
3 - Assistant Professor, Department of Future Studies, Judiciary Research Institute, Tehran, Iran.
4 - Assistant Professor, Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Keywords: NLP, Deep Learning, Legal Text, Semantic Similarity,
Abstract :
Abstract
Intelligent judicial recommendation systems, aimed at enhancing the efficiency and consistency of judicial decision-making, rely on the ability to accurately measure semantic similarity between legal texts. This research investigates various methods for evaluating semantic similarity that can be employed in the development of judicial recommendation systems. The analysis covers a wide range of text similarity detection techniques, from basic vocabulary-based approaches to advanced natural language processing and machine learning models. By considering the unique characteristics of legal language and the challenges inherent in this domain, this study carefully analyzes the strengths and weaknesses of each method. In fact, by providing a comprehensive overview of the most advanced methods for semantic similarity of legal texts, this research paves the way for the design and development of powerful and effective intelligent decision support tools for the judicial system. The development of such tools will significantly assist judges and lawyers and play a crucial role in promoting procedural uniformity within the judicial system.
Introduction: As technology advances and governments become more digitized, a large volume of electronic structured and unstructured data is being generated in the judicial system. Analyzing this data can lead to improved operations and decision-making in the judicial system. The development of intelligent judicial recommendation systems, powered by semantic text similarity techniques, can revolutionize the way legal professionals work. Such systems can streamline legal research, improve the accuracy of judicial decisions, and ultimately enhance the administration of justice. In the common law system, case matching plays an important role and determines the outcome of the judgment of new cases. For this reason, legal experts often spend a lot of time finding similar cases. Therefore, finding similar cases in this legal system is of great importance. Finding similar cases in courts is also a major challenge in the field of legal document review. The length and complex structure of legal documents, as well as the existence of multiple legal issues in a single case, create this challenge. In the judicial system of the Islamic Republic of Iran, crimes are also graded, and crimes of similar degree should have similar punishments. In cases where there is a sharp difference of opinion between rulings, a unanimous ruling is issued. The purpose of this paper is to review methods of semantic similarity in legal texts. In studies conducted, various methods have been used for semantic similarity between texts. In recent years, most studies have focused on the use of deep learning algorithms on text data, the results of which have been better than shallow methods
Method: This paper reviews various methods of semantic similarity in legal texts with a focus on deep learning.
Result: Shallow methods for evaluating semantic similarity, despite their simplicity and efficiency, are unable to understand the complex patterns present in legal texts. In contrast, deep learning-based methods, with their high power of extracting complex concepts from texts, are recognized as the leaders in this field. Recent studies have shown that in most cases, deep learning-based methods outperform shallow approaches in semantic similarity analysis of legal texts.
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