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Heterogeneous legal data-oriented multi-task reading system and method

A reading system and multi-task technology, applied in the field of multi-task reading systems for heterogeneous legal data, can solve problems such as unanswerable questions, inferences to give answers, and inability to give answers to preset questions, etc. The effect of solving the heterogeneity problem

Inactive Publication Date: 2021-02-05
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

[0007] Diversity of questions: For a judgment document, researchers may ask some questions that can be directly answered in the document, such as the sentence, crime location, etc., or may ask questions that require inference to give answers, such as whether there is a gang crime Wait
At the same time, some instruments may not be able to give answers to preset questions, that is, unanswerable questions
The traditional machine reading comprehension model of fragment extraction cannot deal with many complex types of problems;

Method used

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  • Heterogeneous legal data-oriented multi-task reading system and method
  • Heterogeneous legal data-oriented multi-task reading system and method
  • Heterogeneous legal data-oriented multi-task reading system and method

Examples

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Embodiment 1

[0040] Such as figure 1 As shown, this embodiment provides a multi-task reading system for heterogeneous legal data, which includes sequentially connected:

[0041] Data input module for inputting statistical and textual legal data;

[0042] Data preprocessing module, used for data cleaning and data conversion of legal data;

[0043] The data analysis module is used to analyze the preprocessed data;

[0044] The reading result processing module is used to integrate the analyzed data to form structured reading result data;

[0045] The result push module is used to feed back the reading result data to legal researchers.

[0046] In this embodiment, the data preprocessing module includes a statistical data preprocessing module and a text data preprocessing module, and the statistical data preprocessing module is used to fill missing items in the statistical data, delete or replace abnormal items, The outlier data is counted, and the text data preprocessing module is used to ...

Embodiment 2

[0112] In this embodiment, two benchmarks are set: BIDAF and Bert, which are tested together with the model LegalSelfReader proposed in this embodiment.

[0113] lab environment

[0114] Experiment on a machine with 64-bit Windows system. The external storage space of the machine is 930GB, the memory space is 48GB, the CPU type is single-core Intel i7-8700K, the GPU type is NVIDA GeForceGTX 1080Ti, and the GPU size is 11GB. All experimental programs in this embodiment are written in python language, and the deep learning framework used is Pytorch, version number is 1.13.0.

[0115] The original data used in this example comes from the CAIL 2019 Legal Reading Comprehension Competition 1. This dataset is released by the Xunfei Joint Laboratory of Harbin Institute of Technology and University of Science and Technology. It is a multi-task machine reading comprehension dataset for the judicial field. The dataset name is CJRC. The chapters of the data set come from the China Judgm...

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Abstract

The invention relates to the technical field of document reading, in particular to a heterogeneous legal data-oriented multitask reading system and method, and the system comprises the following modules which are sequentially connected: a data input module which is used for inputting statistical and textual legal data; a data preprocessing module which is used for carrying out data cleaning and data conversion on the law data; a data analysis module which is used for analyzing the preprocessed data; a reading result processing module which is used for integrating the analyzed data to form structured reading result data; and a result pushing module which is used for feeding back the reading result data to the law researcher. Statistical analysis and machine reading understanding technologies are used at the same time, structured data such as statistical yearbook and unstructured data such as judgment documents, file materials and interview text records can be processed at the same time,and the problem of data heterogeneity is solved.

Description

technical field [0001] The invention relates to the technical field of document reading, in particular to a multi-task reading system and method for heterogeneous legal data. Background technique [0002] The application of artificial intelligence technology to the legal field can speed up and improve the legal research process, reduce the time cost and funds of legal research, which makes legal intelligence research a very promising field. Katz pointed out in a 2012 study that with the rapid development of artificial intelligence, traditional legal tasks, from generating legal documents to predicting case outcomes, will usher in changes. This change can also be glimpsed from three other aspects. First, speech recognition technology was used to record court hearings]. Second, use machine learning methods to assist lawyers in reviewing legal documents. Furthermore, some machine learning methods have also been applied to build an intelligent referee system. [0003] It can...

Claims

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Application Information

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IPC IPC(8): G06F40/216G06F40/295G06F40/30G06F40/126G06F16/35G06N3/04G06Q50/18
CPCG06F40/216G06F40/295G06F40/30G06F40/126G06F16/35G06N3/049G06Q50/18G06N3/047G06N3/045
Inventor 张引胡刚江池孟冠岐张可
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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