Semantic analysis method and device based on machine learning, medium and electronic equipment

Abstract

The invention relates to a semantic analysis method and device based on machine learning, a medium and electronic equipment, and belongs to the technical field of machine learning application, and themethod comprises the steps: converting to-be-processed input information into pre-input information when the to-be-processed input information is received; inputting the pre-input information into apre-trained machine learning model to obtain a prediction semantic template corresponding to the to-be-processed input information; obtaining semantic template constraint information and a predictionsemantic template, inputting the semantic template constraint information and the prediction semantic template into the prediction semantic template constraint model, and outputting a constrained prediction semantic template; converting the input information to be processed into pre-analysis data according to the constrained semantic template; and according to the pre-analysis data, obtaining a semantic analysis result of the to-be-processed input information. On the basis of the preset machine learning model, the predicted semantic template is obtained through analysis according to various input information, so that the semantic analysis accuracy and efficiency are effectively guaranteed.

Description

technical field [0001] The present application relates to the technical field of machine learning applications, in particular, to a semantic analysis method, device, medium and electronic equipment based on machine learning. Background technique [0002] Semantic analysis is to analyze the semantic analysis result that the user wants to output for an object from a piece of information output by the user. [0003] At present, when performing semantic analysis, for the same semantic analysis result, different legal entity users usually output different initial information, and the same user may have various expressions at different times. For example, when the air conditioner needs to be turned on, different users, even the same user, will have many ways of expressing it. For the analysis of the initial information output by the user, there are multiple semantic analysis results, and the efficiency is difficult to guarantee. Therefore, in the prior art, there are problems of ...

AI Technical Summary

Problems solved by technology

For example, when the air conditioner needs to be turned on, different users, or even the same user, will have many ways of expressing it. For the analysis of the initial information output by the user, there are multiple semantic analysis results, and the efficiency is difficult to guarantee.

Therefore, there are problems of low efficiency and low accuracy when using a fixed analysis method to analyze user semantics in the prior art

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