Text theme mining method based on intra-sentence association graph

Abstract

The invention provides a text theme mining method based on an intra-sentence association graph and relates to the technical field of data mining. The technical problems that an existing mining method is low in quality and poor in universality can be solved by the text theme mining method. The method includes the steps that a target text is firstly divided according to sentences, a sentence sequence table of the text is acquired, then, a sentence association matrix of the target text is established, the weight of each element in the sentence sequence table is calculated, theme sentences are selected according to the calculated weights, the weights of all the non-theme sentences are adjusted each time the theme sentences are selected, theme sentences are selected again according to the adjusted weights, the operation is conducted repeatedly until the sum of character sizes of all the theme sentences reaches a preset character number threshold value, and finally, all the theme sentences serve as the theme content mined from the target text. The method is suitable for text documents of various forms of literature, styles and types.

Description

technical field [0001] The invention relates to data mining technology, in particular to a text topic mining method based on an inter-sentence correlation graph. Background technique [0002] Text data topic mining technology mainly refers to the technology of using computer to automatically extract key sentences that best represent the subject content from the text collection to form concise and coherent essays. With the exponential growth of the number of documents on the web, the topic of quickly discovering text becomes more and more important. Refined and accurate subject content can save user information filtering time and improve user work efficiency. [0003] Among the existing topic mining methods, the topic mining methods based on text structure features and sentence positions are related to the genre of the target text, and the practical methods for scientific and technological literature are not necessarily suitable for news literature; with the development of n...

AI Technical Summary

Problems solved by technology

Although the application of these technologies has improved the quality of the theme to a certain extent, the development of these technologies is still immature

It cannot meet the needs of large-scale text data processing; topic mining methods based on shallow feature statistics (such as word frequency statistics in sentences) are universal, but some existing statistical methods are too simple, and the quality of the extracted topics is not good. high

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