Semantic-based self-adaption text classification method under cloud computing environment

Abstract

The invention relates to a semantic-based self-adaption text classification method under a cloud computing environment. The method comprises the following steps: (1) extracting keywords of all texts and corresponding attributes of the keywords by a local agent end, and uploading to a center end; (2) performing summarization of data according to the received keywords and the corresponding attributes of the keywords; matching a credit value for each keyword to generate a keyword list, and transmitting to the local agent end; (3) performing classification on the texts by the local agent end according to the keyword list, and transmitting a classification result to the center end; (4) outputting the classification result by the center end. Compared with the prior art, the semantic-based self-adaption text classification method provided by the invention has the advantages of being high in text classification efficiency, high in accuracy, and the like.

Description

technical field [0001] The invention relates to a text classification method, in particular to an adaptive text classification method based on semantics in a cloud computing environment. Background technique [0002] With the development of the Internet and cloud technology, more and more applications are deployed to the cloud, which accommodates a large amount of various types of original information, including text information, sound information, image information, and so on. How to grasp the most effective information in the vast and complicated texts is always a major goal of information processing. The text classification system based on artificial intelligence technology can automatically classify a large number of texts according to the semantics of the text, so as to better help people grasp text information. In recent years, text classification technology has been gradually combined with information processing technologies such as search engines, information push, ...

AI Technical Summary

Problems solved by technology

From the 1960s to the end of the 1980s, knowledge engineering technology was the most important and most effective content text classification system during this period. It mainly used artificial methods to build classifiers, which was labor-intensive and prone to human errors.

[0003] The traditional statistics-based Chinese semantic classification method is based on a statistical method or model to extract keywords from the text. The efficiency of text classification in the cloud computing environment is low, and its efficiency will decrease significantly as the number of categories increases.

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