Text classification method and system based on Attention graph attention network
The invention provides a text classification method based on an Attention graph attention network, belongs to the field of natural language processing, and aims to solve the problems that unstructured texts contained in geographic texts are obscure and inaccurate, and a large amount of data is diffi...
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| creator | SONG XIANYANG LIU PENG JING WEIPENG CHEN GUANGSHENG |
| description | The invention provides a text classification method based on an Attention graph attention network, belongs to the field of natural language processing, and aims to solve the problems that unstructured texts contained in geographic texts are obscure and inaccurate, and a large amount of data is difficult to acquire and classify in the prior art. According to the method, an attention mechanism is introduced into the text graph convolution network, so that different weights are given to a common normalization process in convolution operation, and nodes (texts) to be classified can learn features with different weights according to the importance degree of the context to the nodes (texts). According to the method, feature aggregation is carried out in a self-established geographic text data set according to a context relation, and under the action of marked data, whether data pairs with unknown tags belong to geographic texts or not is classified. According to the text classification method based on the Attention |
| format | Patent |
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According to the method, an attention mechanism is introduced into the text graph convolution network, so that different weights are given to a common normalization process in convolution operation, and nodes (texts) to be classified can learn features with different weights according to the importance degree of the context to the nodes (texts). According to the method, feature aggregation is carried out in a self-established geographic text data set according to a context relation, and under the action of marked data, whether data pairs with unknown tags belong to geographic texts or not is classified. 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According to the method, an attention mechanism is introduced into the text graph convolution network, so that different weights are given to a common normalization process in convolution operation, and nodes (texts) to be classified can learn features with different weights according to the importance degree of the context to the nodes (texts). According to the method, feature aggregation is carried out in a self-established geographic text data set according to a context relation, and under the action of marked data, whether data pairs with unknown tags belong to geographic texts or not is classified. 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Text classification method and system based on Attention graph attention network |
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