JEDoDF: Judicial Event Discrimination Based on Deep Forest

With the rapid development of natural language and the implementation of the Wisdom Court, intelligent judicial assistants has become a new application of natural language processing in the judicial field. The text classification method based on word vector and deep neural network implements statist...

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Vydáno v:2019 15th International Conference on Semantics, Knowledge and Grids (SKG) s. 36 - 43
Hlavní autoři: Wang, Yongchao, Xu, Mengting, Wang, Lei, Li, Huakang, Sun, Guozi
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2019
Témata:
Computer science
Deep Forest
Event Tree
Forestry
Hidden Markov models
Intelligent Judicial
Semantics
Syntactic Dependency Tree
Syntactics
Telecommunications
Vegetation
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Abstract With the rapid development of natural language and the implementation of the Wisdom Court, intelligent judicial assistants has become a new application of natural language processing in the judicial field. The text classification method based on word vector and deep neural network implements statistical classification of judicial documents, but it can not achieve the inherent logical interpretation of judicial cases. A method of extracting semantic logic tree from judicial case texts is proposed, and event tree can be interpreted by deep forest. Judicial documents are divided into several sub-tree fragments by sentence segmentation, and each sub-tree fragment is analyzed by dependency syntax to obtain core subject-predicate-object triples. TF-IDF algorithm is used to calculate the weights of triples, and get the core sub-event sequence, and use pruning algorithm to construct the max heap. The designed triple encoding algorithm realizes max heap vectorization of event tree, and embedded deep forest algorithm to realize classification discrimination of judicial text event tree. The experimental results show that the proposed event tree construction method combined with the deep forest algorithm can greatly improve the logical interpretation and accuracy of judicial text.
AbstractList With the rapid development of natural language and the implementation of the Wisdom Court, intelligent judicial assistants has become a new application of natural language processing in the judicial field. The text classification method based on word vector and deep neural network implements statistical classification of judicial documents, but it can not achieve the inherent logical interpretation of judicial cases. A method of extracting semantic logic tree from judicial case texts is proposed, and event tree can be interpreted by deep forest. Judicial documents are divided into several sub-tree fragments by sentence segmentation, and each sub-tree fragment is analyzed by dependency syntax to obtain core subject-predicate-object triples. TF-IDF algorithm is used to calculate the weights of triples, and get the core sub-event sequence, and use pruning algorithm to construct the max heap. The designed triple encoding algorithm realizes max heap vectorization of event tree, and embedded deep forest algorithm to realize classification discrimination of judicial text event tree. The experimental results show that the proposed event tree construction method combined with the deep forest algorithm can greatly improve the logical interpretation and accuracy of judicial text.
Author Wang, Lei
Xu, Mengting
Sun, Guozi
Wang, Yongchao
Li, Huakang
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  organization: Nanjing University of Posts and Telecommunications, China
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StartPage 36
SubjectTerms Computer science
Deep Forest
Event Tree
Forestry
Hidden Markov models
Intelligent Judicial
Semantics
Syntactic Dependency Tree
Syntactics
Telecommunications
Vegetation
Title JEDoDF: Judicial Event Discrimination Based on Deep Forest
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