Semantic similarity calculation method based on deep learning

Abstract

The invention discloses a semantic similarity calculation method based on deep learning, and relates to the field of semantic similarity calculation. The method comprises the following steps: step 1,constructing a training data set, and preprocessing training data to obtain one-hot sparse vectors; step 2, constructing a semantic similarity calculation network model comprising N layers of BI-LSTMnetworks, a residual network, a similarity matrix, a CNN convolutional neural network, a pooling layer and a full connection layer; step 3, inputting the one-hot sparse vector into the network model,and training parameters by using a training data set to complete supervised training; and step 4, inputting a text to be tested into the trained network model, judging whether the text to be tested isa similar text or not, and outputting a result. The semantic similarity calculation network model comprises a multi-layer BI-LSTM network, a residual network, a CNN convolutional neural network, a pooling layer and a full connection layer. Meanwhile, a BI-LSTM network and a CNN convolutional neural network are used, and a residual network is added into the BI-LSTM network, so that the problem ofgradient disappearance caused by a multi-layer network is solved, and the feature extraction capability of the model is enhanced.

Description

technical field [0001] The invention relates to the field of semantic similarity calculation, in particular to a deep learning-based semantic similarity calculation method. Background technique [0002] Semantic similarity calculation is a basic task in the field of natural language processing. With the advent of the era of artificial intelligence, more and more scientists and scholars are focusing on the field of natural language processing. The task of semantic similarity calculation is because of its Document copy checking, information retrieval, and machine translation are widely used in fields such as machine translation, and more and more researchers are devoting themselves to the study of semantic similarity calculation. In recent years, due to the rise of deep learning technology, semantic similarity calculation has also developed by leaps and bounds. Compared with traditional methods, deep learning technology can extract deep semantics and obtain richer feature exp...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is: the present invention provides a semantic similarity calculation method based on deep learning, which solves the problem of imperfect feature extraction of existing models and shallow network layers The problem that leads to low accuracy of similarity calculation

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