In recent years,machine learning algorithms and in particular deep learning has shown promising results when used in the field of legal domain.The legal field is strongly affected by the problem of information overloa...In recent years,machine learning algorithms and in particular deep learning has shown promising results when used in the field of legal domain.The legal field is strongly affected by the problem of information overload,due to the large amount of legal material stored in textual form.Legal text processing is essential in the legal domain to analyze the texts of the court events to automatically predict smart decisions.With an increasing number of digitally available documents,legal text processing is essential to analyze documents which helps to automate various legal domain tasks.Legal document classification is a valuable tool in legal services for enhancing the quality and efficiency of legal document review.In this paper,we propose Sammon Keyword Mapping-based Quadratic Discriminant Recurrent Multilayer Perceptive Deep Neural Classifier(SKM-QDRMPDNC),a system that applies deep neural methods to the problem of legal document classification.The SKM-QDRMPDNC technique consists of many layers to perform the keyword extraction and classification.First,the set of legal documents are collected from the dataset.Then the keyword extraction is performed using SammonMapping technique based on the distance measure.With the extracted features,Quadratic Discriminant analysis is applied to performthe document classification based on the likelihood ratio test.Finally,the classified legal documents are obtained at the output layer.This process is repeated until minimum error is attained.The experimental assessment is carried out using various performance metrics such as accuracy,precision,recall,F-measure,and computational time based on several legal documents collected from the dataset.The observed results validated that the proposed SKM-QDRMPDNC technique provides improved performance in terms of achieving higher accuracy,precision,recall,and F-measure with minimum computation time when compared to existing methods.展开更多
With the development of economic globalization,legal English has been developing very fast as one kind of ESP.Compared with general English,legal English is a special language style,which is characterized by stability...With the development of economic globalization,legal English has been developing very fast as one kind of ESP.Compared with general English,legal English is a special language style,which is characterized by stability,oddities and exactness.The language used in legal document belongs to a special kind of legal English,and has its own language characters like other social dialects.The paper analyses the features of the language of legal document from lexical and syntactic level.展开更多
文摘In recent years,machine learning algorithms and in particular deep learning has shown promising results when used in the field of legal domain.The legal field is strongly affected by the problem of information overload,due to the large amount of legal material stored in textual form.Legal text processing is essential in the legal domain to analyze the texts of the court events to automatically predict smart decisions.With an increasing number of digitally available documents,legal text processing is essential to analyze documents which helps to automate various legal domain tasks.Legal document classification is a valuable tool in legal services for enhancing the quality and efficiency of legal document review.In this paper,we propose Sammon Keyword Mapping-based Quadratic Discriminant Recurrent Multilayer Perceptive Deep Neural Classifier(SKM-QDRMPDNC),a system that applies deep neural methods to the problem of legal document classification.The SKM-QDRMPDNC technique consists of many layers to perform the keyword extraction and classification.First,the set of legal documents are collected from the dataset.Then the keyword extraction is performed using SammonMapping technique based on the distance measure.With the extracted features,Quadratic Discriminant analysis is applied to performthe document classification based on the likelihood ratio test.Finally,the classified legal documents are obtained at the output layer.This process is repeated until minimum error is attained.The experimental assessment is carried out using various performance metrics such as accuracy,precision,recall,F-measure,and computational time based on several legal documents collected from the dataset.The observed results validated that the proposed SKM-QDRMPDNC technique provides improved performance in terms of achieving higher accuracy,precision,recall,and F-measure with minimum computation time when compared to existing methods.
文摘With the development of economic globalization,legal English has been developing very fast as one kind of ESP.Compared with general English,legal English is a special language style,which is characterized by stability,oddities and exactness.The language used in legal document belongs to a special kind of legal English,and has its own language characters like other social dialects.The paper analyses the features of the language of legal document from lexical and syntactic level.
