Loss function optimization method and device of classification model and sample classification method

Abstract

The embodiment of the invention provides a loss function optimization method and device of a classification model and a sample classification method. The optimization method comprises: generating a filter vector corresponding to a classification label vector, wherein the classification label vector and the filter vector both comprise a dimension corresponding to a first type of classification anda dimension corresponding to a second type of classification, and a dimension value corresponding to the second type of classification in the filter vector is zero; Generating an original loss function according to the classification label vector and the output result of the classification model; Filtering the original loss function by using the filter vector to remove a component of a second class of classification in the original loss function to obtain a loss filtering function; And performing post-processing on the loss filtering function according to a preset rule to obtain a loss optimization function. Therefore, the optimized loss function can improve the learning weight of the classification model to the text features of the first classification, does not learn the text features ofthe second classification, reduces the generalization of the classification model, and improves the text classification accuracy.

Description

technical field [0001] The present application relates to the technical field of natural language processing, in particular to a method and device for optimizing a loss function of a classification model, and a sample classification method. Background technique [0002] In the field of natural language processing technology, the TextCNN (TextConvolutional Neural Network, text convolutional neural network) model is a mainstream solution for text classification models. The principle of the TextCNN model is to convolve text features through a convolutional neural network. During the convolution process, multiple convolution kernels are used to extract important text features from the text, and text classification is performed based on the extracted text features. [0003] In the prior art, the TextCNN model has no difference in the feature extraction of the input text, that is to say, every time each text enters the TextCNN model, the TextCNN model will learn the text once and ...

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present application provides a classification model loss function optimization method, device and sample classification method to solve the problem of the generalization ability of the classification model in the prior art due to the indiscriminate learning of classification features.

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