Deep learning-based question classification model training method and apparatus, and question classification method and apparatus

Abstract

The invention discloses a deep learning-based question classification model training method and apparatus, and a question classification method and apparatus. The question classification model training method comprises the steps of extracting feature information samples in question text samples, and generating corresponding first eigenvector samples; performing spatial transformation on the first eigenvector samples to obtain second eigenvector samples; inputting the second eigenvector samples to a plurality of convolutional layers and a plurality of pooling layers in a multilayer convolutional neural network, and by superposing convolution operation and pooling operation, obtaining first fusion eigenvector samples; inputting the first fusion eigenvector samples to a full connection layer in the multilayer convolutional neural network to obtain global eigenvector samples; and training a Softmax classifier according to the global eigenvector samples to obtain a question classification model. The method can avoid a large amount of overheads of manual design of features; and through the question classification model, a more accurate classification result can be obtained, so that locating of standard question and answer is improved.

Description

technical field [0001] The present invention relates to the technical fields of computers and the Internet, in particular to a deep learning-based problem classification model training method, problem classification method and device. Background technique [0002] The current question answering system faces defects such as large consumption of human resources and untimely response. The goal of an automatic question answering system is to give short and precise answers to a given question. Whether it is an industry application or an academic research, the analysis of the true intention of the question and the identification of the matching relationship between the question and the answer are still constraints on automatic question answering. Two key puzzles in the performance of question answering systems. As we all know, question classification classifies questions according to expected answers, which can quickly locate the approximate location of standard questions and ans...

AI Technical Summary

Problems solved by technology

[0004] However, the current problem is that machine learning requires manual extraction of features, and the accuracy of the model is very dependent on the design of features. In addition, the user's input questions in the question answering system are usually short texts with diverse, random, and irregular words. It is difficult to extract rich semantic features, and the designed features are not universal, which leads to inaccurate classification results of questions, which in turn affects the positioning of standard questions and answers, resulting in poor user experience

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