Semantic analysis method, device and equipment and readable storage medium

Abstract

The invention discloses a semantic analysis method. According to the method, a second model with a simple model structure is called to carry out rapid semantic analysis on a long text vector with simple semantics, wherein when compared with a short text vector, the long text vector can ensure the integrity and correlation of text content and reduce the loss of text information, meanwhile, the complexity of the whole extraction model can be reduced, the reasoning speed is increased, and comprehensive and rapid semantic feature extraction is achieved; meanwhile, a first model with a complex model structure is called to perform semantic analysis on short text vectors with complex semantics, semantic information can be expressed and extracted more accurately, and a more accurate semantic analysis result is obtained; and semantic information combination is performed on the first analysis result and the second analysis result to realize multi-granularity semantic analysis of short text + long text and simple model + complex model, and the integrity and the accuracy of an analysis result are ensured. The invention further discloses a semantic analysis device and equipment and a readable storage medium, which have corresponding technical effects.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a semantic analysis method, device, equipment and readable storage medium. Background technique [0002] Natural Language Processing (NLP) is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that can realize effective communication between humans and computers using natural language. Long text data is a very common processing object in natural language processing tasks. Long text data refers to text with a large number of characters, such as search records with a cumulative length of more than 3,600 characters entered by users within half a year. By performing semantic analysis on these long text data (Semantic Analysis), the real semantics expressed in the entire text can be understood, which will help promote the rapid development of other natural language processing tasks, and ha...

AI Technical Summary

Problems solved by technology

[0003] At present, in the semantic analysis modeling method of long text data, it is common to use some very simple models (such as text_cnn, fasttext, etc.) to directly identify the semantics of long text data. Although this method retains semantic and timing information, The model cannot accurately express the information in the text, and the accuracy of semantic analysis is poor; at the same time, there is also a method of truncating long text data and then calling a complex model to perform semantic recognition on the truncated long text data. Better semantic and timing expression results can be obtained, but truncating the input will cause the model to lose a lot of information, resulting in incomplete extraction objects, and the results of model recognition are relatively one-sided

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