Topic model-based judgment document similarity analysis method

Abstract

The invention discloses a topic model-based judgment document similarity analysis method. A semantic-based semi-automatic and universal similarity analysis method is proposed for judgment documents by adopting an LDA (Latent Dirichlet Allocation) topic model in machine learning. The method mainly comprises the steps of selecting corpora; establishing a similarity tag; performing text preprocessing; performing input selection; performing parameter setting; performing iterative training; generating a model; applying the model; and the like. Based on a general similarity analysis method, the characteristics of rich specialized vocabularies and complex semantics in contents of the judgment documents are fully considered, and the semi-structured characteristics of the judgment documents are utilized, so that the accuracy and applicability of judgment document similarity analysis are improved.

Description

technical field [0001] The invention is a text similarity classification method, aimed at the court's internal judgment documents, and belongs to the technical fields of machine learning and text mining. Background technique [0002] The China Judgment Documents Network started construction in 2013. As of May 14, 2017, it has accumulated more than 29 million documents and has gradually grown into the world's largest website for sharing judicial documents. Based on these data, a series of judicial big data research and analysis work has been carried out one after another. While achieving remarkable results, there are still many problems and challenges. Some of the problems focus on the inadequacy of court data mining and analysis capabilities and related research. [0003] Judgment documents, as an important part of the court's work, record the process and results of the people's court's trial. It is not only the carrier of the results of the court's litigation activities, ...

AI Technical Summary

Problems solved by technology

In comparison, the variational EM method has a faster training speed than the Gibbs Sampling method, but the result obtained by the variational EM method is a local optimum, not necessarily equal to the global optimum

At the same time, although the program logic of Gibbs Sampling is relatively simple, it cannot support distributed computing like the variational EM method.

Images