Class case pushing method and device based on case description
A technology for cases and judgments, applied in text database clustering/classification, biological neural network models, semantic analysis, etc., can solve the problems of reducing accuracy and impact, and achieve the effect of improving results
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Embodiment 1
[0106] Select 280 judgments from eight provinces and municipalities directly under the central government as an example to demonstrate the processing process of this method, and use several query examples to compare with the query results of the conventional method (using the full text of judgments to construct a latent semantic space), and the indicators include similarity and ranking.
[0107] Executing S11: operations including word segmentation, removing spaces and stop words, etc. First remove spaces, then remove stop words according to the stop word list, and use Stanford NLP for word segmentation;
[0108] Execute S12221: set the word vector dimension of Word2Vec to 100;
[0109] Execute S12222: set the hidden layer dimension of Bi-LSTM to 100;
[0110] Execute S12223: splicing together the output of the forward / backward LSTM terminal, the dimension of the formed vector is 200;
[0111] Execute S1225: set the hidden layer dimension of Bi-LSTM to 200;
[0112] Execut...
example 1
[0141] Example 1: Hu Moutong stole a red motorcycle of a certain brand
[0142] Location: 35 copies of H Province / (2014) Cheng Xing Chu Zi No. 883 Hu Moutong Criminal Judgment for Theft.txt,
[0143] Similarity: 0.38114216172268367->0.9715493492582427
[0144] Rank: 9->1
example 2
[0145] Example 2: Zhang and Liu discussed going to sea at night to steal sea cucumbers
[0146] Location: 35 copies of S Province / Criminal Judgment of the Second Instance of Zhang’s Theft Case.txt
[0147] Similarity: less than 0.49 -> 0.9974174977991768
[0148] Rank: Out of 15 -> 1
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