Triad similar task-based trial risk early warning method

Abstract

The invention discloses a triad similar task-based trial risk early warning method, and the method comprises the following steps: acquiring data from a legal document database, performing keyword matching, and extracting case description and a judgment result; carrying out the text preprocessing, including word segmentation, data enhancement and other operations, and generating multiple triples; generating a case vector by using an LSTM network, and carrying out triple similar task judgment to obtain a representation model of the case; preprocessing the current case and the historical case respectively and inputting the preprocessed cases into a representation model to obtain respective case representations; calculating a topM case with the highest similarity, obtaining a judgment result representation of the topM case, and finally calculating the similarity between the result and the judgment result of the current case to obtain a deviation risk value. According to the method, the current legal document can be analyzed in a triple similarity calculation mode, and the case with relatively high similarity is found out from the historical electronic case. A recommended judgment rangeis given according to the judgment result of the past case, thereby achieving the purpose of trial deviation early warning.

Description

technical field [0001] The invention relates to the fields of deep learning and natural language processing, in particular to a trial risk early warning method based on triple similarity tasks. Background technique [0002] In the era of big data, artificial intelligence has a wide range of applications in various industries. In the construction of smart courts, by giving machines the ability to understand legal texts and applying artificial intelligence technology in the judicial field, it can provide judicial staff with intelligent assistance systems such as case understanding, sentencing assistance, and risk warning, which can effectively improve the efficiency of court trials. , and improve the quality of judgments, and promote the intelligentization of trials, enforcement, and services through information technology. By making full use of the cutting-edge technology of artificial intelligence, it analyzes, processes, and classifies the information marked in electronic ...

AI Technical Summary

Problems solved by technology

The disadvantage of this technology is that the word2vec word vector training method cannot encode the sequence relationship between words into the vector, so the expressiveness of the vector is insufficient; secondly, after getting the word vector, often only a small number of keywords can be selected for the case (2-3) Calculate the case vector, so the case vector is often not accurate

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