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TenLa: an approach based on controllable tensor decomposition and optimized lasso regression for judgement prediction of legal cases.

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Title: TenLa: an approach based on controllable tensor decomposition and optimized lasso regression for judgement prediction of legal cases.
Authors: Guo, Xiaoding1 (AUTHOR) 15b903068@hit.edu.cn, Zhang, Hongli1 (AUTHOR), Ye, Lin1 (AUTHOR), Li, Shang1 (AUTHOR)
Source: Applied Intelligence. Apr2021, Vol. 51 Issue 4, p2233-2252. 20p.
Subjects: Classification algorithms, Forecasting, Artificial intelligence, Mathematical optimization, Big data
Abstract: With the development of big data and artificial intelligence technology, the computer-assisted judgment of legal cases has become an inevitable trend in the intersection of computer science and law. Judgment prediction methods of legal cases mainly consist of two parts: (1) modeling of legal cases and (2) construction of judgment prediction algorithms. Previous methods for the judgment prediction of legal cases are mainly based on feature models and classification algorithms. Traditional feature models require extensive expert knowledge and manual annotation. They are highly dependent on vocabulary and grammatical information in databases, which are not conducive to the improvement of accuracy and universality of subsequent prediction algorithms. In addition, prediction results obtained by classification algorithms are coarse in granularity and low in accuracy. In general, judgments in similar legal cases are similar. This article proposes a new method for the judgment prediction of legal cases, namely, TenLa, which is based on a controllable algorithm of tensor decomposition and an optimized Lasso regression model. TenLa takes similarities between legal cases as an important indicator of judgment prediction and is mainly divided into three parts: (1) ModTen; we propose a modeling method for legal cases, namely, ModTen, which represents legal cases as three-dimensional tensors. (2) ConTen; we propose a new tensor decomposition algorithm, namely, ConTen, which decomposes tensors obtained by ModTen into core tensors through the intermediary tensor. Core tensors greatly reduce the dimensions of original tensors. (3) OLass; we propose an optimized Lasso regression algorithm, namely, OLass. Core tensors obtained by ConTen are used to train OLass. Specifically, we propose an optimization algorithm for OLass with respect to the intermediary tensor in ConTen; thus, the core tensors obtained by ConTen carry tensor elements and tensor structure information that is most conducive to the improvement of the accuracy of OLass. Experiments show that TenLa has higher accuracy than traditional judgment prediction algorithms. [ABSTRACT FROM AUTHOR]
Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Xiaoding%22">Guo, Xiaoding</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 15b903068@hit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hongli%22">Zhang, Hongli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ye%2C+Lin%22">Ye, Lin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Shang%22">Li, Shang</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: With the development of big data and artificial intelligence technology, the computer-assisted judgment of legal cases has become an inevitable trend in the intersection of computer science and law. Judgment prediction methods of legal cases mainly consist of two parts: (1) modeling of legal cases and (2) construction of judgment prediction algorithms. Previous methods for the judgment prediction of legal cases are mainly based on feature models and classification algorithms. Traditional feature models require extensive expert knowledge and manual annotation. They are highly dependent on vocabulary and grammatical information in databases, which are not conducive to the improvement of accuracy and universality of subsequent prediction algorithms. In addition, prediction results obtained by classification algorithms are coarse in granularity and low in accuracy. In general, judgments in similar legal cases are similar. This article proposes a new method for the judgment prediction of legal cases, namely, TenLa, which is based on a controllable algorithm of tensor decomposition and an optimized Lasso regression model. TenLa takes similarities between legal cases as an important indicator of judgment prediction and is mainly divided into three parts: (1) ModTen; we propose a modeling method for legal cases, namely, ModTen, which represents legal cases as three-dimensional tensors. (2) ConTen; we propose a new tensor decomposition algorithm, namely, ConTen, which decomposes tensors obtained by ModTen into core tensors through the intermediary tensor. Core tensors greatly reduce the dimensions of original tensors. (3) OLass; we propose an optimized Lasso regression algorithm, namely, OLass. Core tensors obtained by ConTen are used to train OLass. Specifically, we propose an optimization algorithm for OLass with respect to the intermediary tensor in ConTen; thus, the core tensors obtained by ConTen carry tensor elements and tensor structure information that is most conducive to the improvement of the accuracy of OLass. Experiments show that TenLa has higher accuracy than traditional judgment prediction algorithms. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              M: 04
              Text: Apr2021
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