Legal document feature extraction method, related device and storage medium
A feature extraction and document technology, applied in the electronic field, can solve problems such as lack of legal logic, difficulty in providing case information, and achieve the effect of improving the efficiency of feature extraction
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Embodiment 1
[0043] The embodiment of the present application provides a feature extraction method of legal documents, the feature extraction method of legal documents is applied to an electronic device, and the electronic device can be a smart phone, a tablet computer, a computer and other devices that can install application programs, and the above-mentioned electronic device The operating system may be ios, android, windows system or other operating systems, which are not limited here.
[0044] see figure 1 , the feature extraction method of the legal document mainly includes the following steps:
[0045] 101. Perform pre-identification on legal documents, and determine the paragraph division model and feature extraction model corresponding to the legal documents;
[0046] Pre-identify the legal document, and determine the paragraph division model and feature extraction model corresponding to the legal document; wherein, the feature extraction model includes the corresponding relations...
Embodiment 2
[0065] The embodiment of the present application mainly describes the scheme of extracting document elements through the machine learning model of rule extraction, wherein each document element corresponds to a document element rule, and the document element rule is used to read the features corresponding to the document element in the sentence .
[0066] Step 1. Obtain the document paragraphs after the legal documents are divided into paragraphs, and use the document paragraphs as the input object of the feature extraction model; the feature extraction model includes a plurality of document element rules.
[0067] Step 2, segmenting the document paragraph according to the punctuation marks, and cutting a plurality of sentences obtained to form a sequence of sentences;
[0068] Step 3. According to the document paragraphs after the paragraphs are divided, filter the document element rules corresponding to the document paragraphs in the feature extraction model;
[0069] Step ...
Embodiment 3
[0078] The embodiment of the present application mainly describes the scheme of implementing document element extraction with a supervised neural network model, specifically including:
[0079] First, the training of the neural network model;
[0080] The neural network model of the embodiment of the present application integrates three single models, 1: Based on the TextCNN network, BatchNormal is added, and two layers of fully connected layers are used for classification. Model 2: Based on the TextRNN network, using bidirectional long-term short-term memory (LSTM, Long Short-Term Memory), the hidden vector is classified after K-MaxPooling during classification; Model 3: TextRCNN network. The above three model networks are fused, that is, the outputs of the three networks are added, and then the corresponding legal document samples (marked with document elements) are used for model training.
[0081] The TextCNN network, the three networks of the TextRNN network and the Text...
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