Calculation method and device for English sentence similarity

Abstract

The invention discloses a method and device for calculating the similarity of English sentences, wherein the method includes the following steps: obtaining English sentences; obtaining a word vector sequence according to the English sentence, and expanding the dimension of the word vector sequence to obtain a three-dimensional tensor Sentence feature model; input the three-dimensional tensor of the sentence feature model as multi-channel image data to the convolutional neural network model to extract feature vector groups; calculate the similarity of English sentence pairs based on the cosine distance of the feature vector groups. The method can calculate sentence similarity through a neural network model with a simple model structure, short training time, and good convergence, so that the training model can be effectively simplified, the training time can be shortened, and the test effect is good.

Description

technical field [0001] The invention relates to the technical field of text processing, in particular to a method and device for calculating English sentence similarity. Background technique [0002] There are roughly three types of word vector-based neural network models related to the sentence similarity problem. One is to use the LSTM (Long Short-Term Memory, long-term short-term memory network) network as the main model to process sentence similarity. First, the words in the sentence are represented by the corresponding word vectors; then, the word vectors are input in turn Go to the LSTM network for further processing, and finally get the similarity of sentences. One is to use a CNN (Convolutional Neural Network, Convolutional Neural Network) network to classify sentences. To deal with the sentence classification problem, it is necessary to set the category for the sentence and use the tagged sentence to train the model. The word vector is a two-dimensional matrix co...

AI Technical Summary

Problems solved by technology

[0003] However, the existing models for processing sentence similarity, whether it is an LSTM model or a hybrid model of LSTM and CNN, are all deep neural networks, with huge parameters, slow training time, and complex models are their common shortcomings.

Complex and large-scale networks may achieve good results, but their complexity directly restricts the practical application of such models

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