Text feature extraction method based on machine learning

Abstract

The invention discloses a text feature extraction method based on machine learning, and the method comprises the following steps: 1, initializing a system; 2, inputting data; 3, carrying out part-of-speech tagging; 4, training a machine learning block model; 5, carrying out text partitioning; and 6, carrying out text output; wherein in the step 1), a display is arranged on the outer wall of one side of the SVO block text extractor, in the step 2), data of external equipment can be input into the SVO block text extractor through an interactive network module, and in the step 5), SVO block texts are semantically related mark groups.According to the method, a plurality of part-of-speech marks are identified in an unstructured text; a plurality of SVO block texts are determined from a plurality of part-of-speech tags by using a machine learning block model, and the machine learning block model is trained on training data marked with a subject-verb-object (SVO), so that the time cost of text feature extraction is reduced, and manual development and rule updating are not needed.

Description

technical field [0001] The invention relates to the technical field of text feature mining and extraction, in particular to a text feature extraction method based on machine learning. Background technique [0002] Natural language text is one of the important means of human knowledge storage. In an increasingly digital world, the number of texts that can be obtained is increasing exponentially. Although the network contains a large number of unstructured text repositories, it is very difficult to find relevant information from them. Difficult, while heuristic algorithms incur an increased extraction time cost, and rules must be manually developed and updated. Contents of the invention [0003] The purpose of the present invention is to provide a text feature extraction method based on machine learning to solve the problems raised in the above-mentioned background technology. [0004] In order to solve the above-mentioned technical problems, the present invention provides ...

AI Technical Summary

Problems solved by technology

[0002] Natural language text is one of the important means of human knowledge storage. In an increasingly digital world, the number of texts that can be obtained is increasing exponentially. Although the network contains a large number of unstructured text repositories, it is very difficult to find relevant information from them. Difficult, while heuristic algorithms incur a cost in extraction time, and rules must be manually developed and updated

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