Semantic analysis method

Abstract

The invention discloses a semantic analysis method. The method comprises the steps of receiving data input by a user, determining the task type of the data, determining the semantic parsing mode of the data and parsing the data, determining the semantic analysis result and the like. According to the semantic analysis method, asynchronous and concurrent semantic analysis is conducted in a basic template library of a semantic template library, the data analysis result is obtained rapidly and efficiently, when the semantic analysis mode of current data does not exist in the basic template library, the piece of data is automatically transferred to a network template library to be parsed, if related information cannot be parsed, a corresponding parsing mode is automatically and intelligently extracted from a search engine to conduct parsing, and thus the intelligence and accuracy of searching can be improved.

Description

technical field [0001] The invention relates to the field of network technology, in particular to a semantic analysis method. Background technique [0002] With the rise of the Internet, network activities have penetrated into people's daily life. Users often search for the information they want through keywords. For example, users enter keywords on a search engine, and then the search engine uses keyword matching algorithms etc. to retrieve information such as links, videos, and images. However, due to the wide variety of ambiguity or polysemy in natural language at various levels, in addition to the original meaning of the word, it is likely to contain some other hidden semantics, only through the method of keyword matching , it is difficult to provide users with accurate search information, and even irrelevant results may be obtained. [0003] In order to strengthen the relevance of matching and improve the accuracy of search, many semantic analysis methods have emerged...

AI Technical Summary

Problems solved by technology

[0005] 1. When there are many implicit semantics to be analyzed, the memory requirements for a single process are high, and the training efficiency is low

[0006] 2. The semantic knowledge base needs to be written manually, which is time-consuming and labor-intensive, and it is not very tolerant to the redundancy and noise contained in the text, and the reusability is poor

[0007] 3. When the relevant information of a data set cannot be queried in the semantic knowledge base, it is necessary to manually enter the relevant information of the data set, which is insufficient in intelligence and brings a lot of inconvenience to the use

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