Chinese Long-Text Classification Strategy Based on Fusion Features
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초록
In the process of Chinese long-text classification, due to the large amount of text data and complex features,methods suitable for ordinary text classification often lack sufficient accuracy, which directly leads to frequentclassification failures in long-text environments. To solve this problem, the research designed a bi-directionallong short-term memory (Bi-LSTM) model that combines forward and backward operations and utilizedattention mechanisms to improve fusion. At the same time, the bi-directional encoder representations fromtransformers (BERT) model was introduced into the text processing to form a long-text classification model. Finally, different datasets were tested to verify the actual classification effect of the model. The research resultsshowed that under different dataset environments, the classification accuracy rates of the designed models were92.93% and 93.77%, respectively, which are the models with the highest classification accuracy rates amongthe same type of models. The calculation time was 85.42 seconds and 117.51 seconds, respectively, which arethe models with the shortest calculation time among the same type of models. It can be seen that the researchdesigned long-text classification model innovatively combined the BERT model, convolutional neural networkmodel, Bi-LSTM model, and attention mechanism structure based on the data characteristics of long-textclassification, enabling the model to achieve higher classification accuracy in a shorter computational time. Moreover, it has better classification results in actual long-text classification, overcomes the classificationfailure problem caused by complex text features in the long-text classification environment, and provides apossibility for long-text specific classification paths.
Abstract
In the process of Chinese long-text classification, due to the large amount of text data and complex features,methods suitable for ordinary text classification often lack sufficient accuracy, which directly leads to frequentclassification failures in long-text environments. To solve this problem, the research designed a bi-directionallong short-term memory (Bi-LSTM) model that combines forward and backward operations and utilizedattention mechanisms to improve fusion. At the same time, the bi-directional encoder representations fromtransformers (BERT) model was introduced into the text processing to form a long-text classification model. Finally, different datasets were tested to verify the actual classification effect of the model. The research resultsshowed that under different dataset environments, the classification accuracy rates of the designed models were92.93% and 93.77%, respectively, which are the models with the highest classification accuracy rates amongthe same type of models. The calculation time was 85.42 seconds and 117.51 seconds, respectively, which arethe models with the shortest calculation time among the same type of models. It can be seen that the researchdesigned long-text classification model innovatively combined the BERT model, convolutional neural networkmodel, Bi-LSTM model, and attention mechanism structure based on the data characteristics of long-textclassification, enabling the model to achieve higher classification accuracy in a shorter computational time. Moreover, it has better classification results in actual long-text classification, overcomes the classificationfailure problem caused by complex text features in the long-text classification environment, and provides apossibility for long-text specific classification paths.
참고문헌 (15)
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