Text correlation detection method and device, equipment and storage medium

Abstract

The invention relates to the technical field of speech semantics, and discloses a text correlation detection method, device and equipment and a storage medium. The method comprises the following steps: respectively obtaining a first keyword of a first text and a second keyword of a second text, and constructing a topic map based on the first keyword and the second keyword; obtaining a receiving matrix of the topic map based on the matching features, and inputting the matching features and the receiving matrix into a map convolution model for analysis to obtain the correlation between the first text and the second text. The global matching between texts is realized, and the flexibility and accuracy of correlation matching are improved.

Description

technical field [0001] The present application relates to the technical field of speech semantics, in particular to a text correlation detection method, device, equipment and storage medium. Background technique [0002] Text matching is to calculate the correlation between two texts. At present, text matching is often used to check the correlation between the business text provided by the staff and the industry standard text, so as to determine the business ability of the corresponding staff. For example, the common testing process of customer service speech quality is the process of correlating the call text between customer service and users with industry standard speech. Traditional detection methods are mainly rule-based face-to-face notification matching or tf-idf-based matching, but the rule-based face-to-face notification matching method lacks flexibility and incompleteness, and the matching algorithm based on tf-idf can only achieve accurate matching. It is diffic...

AI Technical Summary

Problems solved by technology

Traditional detection methods are mainly rule-based face-to-face notification matching or tf-idf-based matching, but the rule-based face-to-face notification matching method lacks flexibility and incompleteness, and the matching algorithm based on tf-idf can only achieve accurate matching. It is difficult to accurately assess the degree of similarity for texts with similar speech and large differences in glyphs

[0003] To sum up, the existing text correlation matching methods lack flexibility, and there is a problem that global matching cannot be performed

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