Adjudication document annotation method and device based on machine learning algorithm

Abstract

The invention discloses an adjudication document annotation method and device based on a machine learning algorithm. The method comprises the following steps that: collecting the text set of adjudication documents to be annotated; carrying out structure segmentation on texts in the text set; establishing a semantic tag library; on the basis of the semantic tag library, carrying out manual annotation on the adjudication documents to be annotated; selecting parts of adjudication documents subjected to the manual annotation as a standard dataset to be handed for machine learning, and training andoptimizing a preliminary annotation model; selecting residual parts of adjudication document models subjected to the manual annotation to obtain a mature adjudication document annotation model; and after a target adjudication document to be annotated is segmented, inputting into the mature adjudication document annotation model to obtain an annotation result. Through the method, the problem in the relevant art that adjudication document legal elements are not completely extracted and case information extraction accuracy is low is solved.

Description

technical field [0001] The present invention relates to the technical field of text processing, in particular to a method and device for labeling referee documents based on machine learning algorithms. Background technique [0002] The content of judgment documents is usually relatively long, and the facts of some cases are more complicated. For judges, it is a great challenge to quickly capture the key information points of the case and clarify the judicial logic of the case from similar cases that are pushed, which is time-consuming and expensive. It is laborious and puts pressure on the judge's trial work. For adjudication documents, it is often necessary to analyze the type of adjudication documents, each paragraph, and extract the dimension information of each paragraph and other parameters, so as to summarize historical cases in a timely manner and discover laws to improve trial efficiency and achieve judicial justice. At the same time, analyzing the effective judgmen...

AI Technical Summary

Problems solved by technology

This method only considers the method of using keywords to extract legal elements, but its shortcomings are very prominent: the judgment document is not structurally divided, and the repetition of keywords leads to a decrease in the accuracy of case element extraction; the general use of case keywords to replace the concept of legal elements, Lack of professionalism in extracting legal elements of judgment documents, which does not conform to realistic judicial logic

[0008] Therefore, those skilled in the art are committed to developing a method and device for labeling referee documents based on machine learning algorithms. Through the type labeling method based on natural language understanding and semantic analysis, the referee documents are marked and expressed in an intuitive way. The complete legal elements and internal logical relationship of the case are solved, thus solving the problems of incomplete extraction of legal elements of judgment documents and low accuracy of case information extraction caused by ignoring legal expertise and judicial logic in existing methods

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