Character string multimode fuzzy matching method based on AC automaton

Abstract

The invention discloses a character string multimode fuzzy matching method based on an AC automaton, and the method comprises the following steps: defining a plurality of groups of mode string sets with labels through employing a rule, and adding defined mode strings with labels into a database; judging whether the text content is inquired for the first time; if the text content is inquired for the first time, reading all mode strings with labels in the database, and constructing a Trie tree through a pre-configured method; completing the construction of a fail pointer on the Trie tree by adopting a preset rule; and adopting a preset method to achieve query matching between the text content and the plurality of groups of pattern string sets with the labels. The method has the beneficial effects that a fuzzy matching function is added on the basis of the AC automaton, the common prefix in the multimode character string can be effectively utilized, the comparison frequency is reduced, fuzzy matching can be supported, and the method has certain robustness and is simple and efficient.

Description

technical field [0001] The invention relates to the field of multi-mode fuzzy matching of character strings, in particular to an AC automaton-based multi-mode fuzzy matching method for character strings. Background technique [0002] The Internet is flooded with a large amount of text data, and quickly extracting key information from the text helps to quickly locate and find the text and make it easier for users to make decisions. Text labels are often a high-level summary of text information and are used to present key information in the text. Text tagging is a process of transforming unstructured text into structured tags, which is crucial for text processing systems. A text may have multiple tags, so in the process of text tagging, it is necessary to perform multi-mode matching on the text and a large number of custom rule strings, and in order to achieve strong robustness, fuzzy matching needs to be implemented when string matching function, so there is an urgent need ...

AI Technical Summary

Problems solved by technology

For a large number of strings, when the similarity calculation needs to be matched, the number of calculations equal to the number of strings in the database is performed, which has low real-time performance, and the calculation of the similarity does not fully utilize the existence of equality in multi-mode strings. Partial features, duplication and inefficiency

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