Deep Learning for Decision Element Extraction in Fact Description of Legal Documents

Abstract

In order to improve the work efficiency of judicial personnel and solve the problem of waste of judicial resources, this topic proposes a method of decision element extraction in the fact description of legal documents based on in-depth learning. Firstly, this paper briefly introduces the basic theory of deep learning, text mining technology, a neural network, and other theories and technologies, then expounds the decision element extraction model in the fact description of legal documents based on deep learning, such as the HMM model, the CRF model, and the Bert model, and finally expounds the establishment and implementation method of the decision element extraction model in the fact description of legal documents, so as to provide a guarantee for the work quality and efficiency of judicial personnel. The samples are according to the sample label frequency to obtain more balanced data, and we manually label keywords to obtain feature vectors to assist the model in improving the prediction results, but it also increases the statistical quantity mode of label co-occurrence. Although all modes can be included by using a larger matrix, the amount of calculation increases significantly. Therefore, the follow-up work mainly studies the important co-occurrence features that can be used and then adopts better dimensionality reduction methods to improve the final prediction results.