As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework...As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.展开更多
Constructing the Chinese self-independent knowledge system of legal science is a great project to adapt Marxist legal science to the Chinese context and the needs of our times in the new era,a profound revolution in t...Constructing the Chinese self-independent knowledge system of legal science is a great project to adapt Marxist legal science to the Chinese context and the needs of our times in the new era,a profound revolution in the field of legal science,a precursor and foundation for constructing a system of legal science with Chinese characteristics,an urgent need to train high-quality legal talents with both virtues and talents,and an inevitable requirement for promoting the Chinese path to the modernization of the rule of law.To carry out such a systematic project,it is imperative to focus on the seven basic principles and scientific methods that include adhering to the ideological guidance of XI Jinping Thought on the Rule of Law.The theory of the system of socialist rule of law with Chinese characteristics,which is the cornerstone for the development of the Chinese self-independent knowledge system of legal science,has provided a necessary and much-needed theoretical paradigm for the development of the Chinese self independent knowledge system of legal science,has led to the innovative development of legal theory with Chinese characteristics in the new era,and will continue to do it.展开更多
By using household survey data, this paper examines the effect of legal knowledge, a proxy for farmers＇ ability to protect their land, on agricultural development in rural China. The Ordinary Least Square （OLS） est...By using household survey data, this paper examines the effect of legal knowledge, a proxy for farmers＇ ability to protect their land, on agricultural development in rural China. The Ordinary Least Square （OLS） estimation results indicate that legal knowledge in a household raises agricultural production. Further, once the production effect of legal knowledge is controlled for, the objective measure of land expropriation has no production effect. These results survive for alternative measures of legal knowledge and subsample analysis. A two-stage least squares strategy further confirms that the effect of legal knowledge on farm production is causal. A preliminary channel analysis suggests that the impact of legal knowledge on farm production works mainly through farmyard manure investments and labor incentives.展开更多
Legal documents are generally big and complex documents because of specific vocabulary,semantics and structure.One of the major challenges in legal processing systems is to generate summary of legal judgements.Till da...Legal documents are generally big and complex documents because of specific vocabulary,semantics and structure.One of the major challenges in legal processing systems is to generate summary of legal judgements.Till date,in most of the legal systems,the summary of judgements is produced manually by legal experts which are then used by Lawyers,Judges and other legal professionals.The manual process of summarization is very inefficient and time-consuming.Automatic text summarization(ATS)is the process of reducing the content of a textual document,while retaining the core description of text through the use of appropriate tool.The present work proposes a novel Fuzzy Analytical Hierarchical process(FAHP)based feature weighting scheme which helps in producing an efficient and effective summary of legal judgement.Model is applied on a number of legal judgements taken from Indian IT Act.Validation of the model is done using ROUGE(Recall-Oriented Understudy for Gisting Evaluation)tool with recall,precision,and f-measure as performance measures.The generated summaries are further assessed by legal experts and are found to be more promising than the summaries generated by traditional approaches.展开更多
基金funded by the Deanship of Scientific Research at Jouf University under Grant number DSR-2022-RG-0101。
文摘As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.
文摘Constructing the Chinese self-independent knowledge system of legal science is a great project to adapt Marxist legal science to the Chinese context and the needs of our times in the new era,a profound revolution in the field of legal science,a precursor and foundation for constructing a system of legal science with Chinese characteristics,an urgent need to train high-quality legal talents with both virtues and talents,and an inevitable requirement for promoting the Chinese path to the modernization of the rule of law.To carry out such a systematic project,it is imperative to focus on the seven basic principles and scientific methods that include adhering to the ideological guidance of XI Jinping Thought on the Rule of Law.The theory of the system of socialist rule of law with Chinese characteristics,which is the cornerstone for the development of the Chinese self-independent knowledge system of legal science,has provided a necessary and much-needed theoretical paradigm for the development of the Chinese self independent knowledge system of legal science,has led to the innovative development of legal theory with Chinese characteristics in the new era,and will continue to do it.
文摘By using household survey data, this paper examines the effect of legal knowledge, a proxy for farmers＇ ability to protect their land, on agricultural development in rural China. The Ordinary Least Square （OLS） estimation results indicate that legal knowledge in a household raises agricultural production. Further, once the production effect of legal knowledge is controlled for, the objective measure of land expropriation has no production effect. These results survive for alternative measures of legal knowledge and subsample analysis. A two-stage least squares strategy further confirms that the effect of legal knowledge on farm production is causal. A preliminary channel analysis suggests that the impact of legal knowledge on farm production works mainly through farmyard manure investments and labor incentives.
文摘Legal documents are generally big and complex documents because of specific vocabulary,semantics and structure.One of the major challenges in legal processing systems is to generate summary of legal judgements.Till date,in most of the legal systems,the summary of judgements is produced manually by legal experts which are then used by Lawyers,Judges and other legal professionals.The manual process of summarization is very inefficient and time-consuming.Automatic text summarization(ATS)is the process of reducing the content of a textual document,while retaining the core description of text through the use of appropriate tool.The present work proposes a novel Fuzzy Analytical Hierarchical process(FAHP)based feature weighting scheme which helps in producing an efficient and effective summary of legal judgement.Model is applied on a number of legal judgements taken from Indian IT Act.Validation of the model is done using ROUGE(Recall-Oriented Understudy for Gisting Evaluation)tool with recall,precision,and f-measure as performance measures.The generated summaries are further assessed by legal experts and are found to be more promising than the summaries generated by traditional approaches.
