Animal product safety event text classification method based on multi-level structure dictionary

Abstract

The invention relates to an animal product safety event text classification method based on a multi-level structure dictionary. The method comprises the steps of performing word segmentation and stopword removal processing on a to-be-processed text; distributing a counter for each residual vocabulary; matching the residual word segmentation result of each text with vocabularies in a constructed multi-level structure dictionary of the animal product safety event, and adding 1 to the count value of the successfully matched vocabularies in an accumulated manner; and finally, performing descending sorting according to the counting values of the vocabularies, and classifying the text into the hierarchy and category of the dictionary where the vocabulary with the highest frequency is located. The method can assist a word segmentation tool in word segmentation of texts to improve the accuracy of entity recognition, can classify Chinese texts according to a hierarchical structure of an animalproduct safety event dictionary, and can also realize hierarchical classification under different requirements to obtain hierarchical and category relationships among the texts. In addition, a largeamount of manpower and time are saved, and the accuracy is obviously improved.

Description

technical field [0001] The invention relates to the fields of artificial intelligence, machine learning and computer technology, in particular to a text classification method for animal product safety events based on a multi-level structure dictionary. Background technique [0002] At present, the number of texts on the Internet is very large, and the categories are very rich, involving various fields and aspects. People can select the texts they are interested in from these texts to learn more information, facilitate their lives, and learn more knowledge. But it also brings troubles to people to find the target text. In the face of messy texts, it is time-consuming and laborious to find out the category texts they are interested in, which seriously affects people's efficiency. At the same time, the results obtained by simply searching for category keywords often contain a large amount of meaningless text, and these meaningless texts are only because they contain search keyw...

AI Technical Summary

Problems solved by technology

[0005] Therefore, for the different needs of Chinese text classification, there is a lack of classification methods that can meet different needs and clearly and conveniently define the levels and categories of classification, and can achieve high accuracy without spending any manpower and time to label any data.

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