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- W4361762397 abstract "The existing text classification algorithms can not effectively use the instance information in the training set for feature extraction, so a text classification model based on artificial intelligence algorithm is proposed. Firstly, KNN is used to extract K training text instances adjacent to the original text. Then Attention is used to calculate the attention score of the adjacent text, and the weighted labels and instance information of the training text are obtained according to the score value. Secondly, GCNN is used to extract semantic information from the original text to obtain contextual semantic information and local key information. Finally, the output of KNN-Attention is fused with the output of GCNN, so as to realize the purpose of effectively utilizing the instance information of training text in text classification task. Experimental results show that the proposed model performs better than the benchmark model and improves the performance of classification tasks effectively." @default.
- W4361762397 created "2023-04-04" @default.
- W4361762397 creator A5078465675 @default.
- W4361762397 date "2023-01-01" @default.
- W4361762397 modified "2023-09-27" @default.
- W4361762397 title "Text Classification Model Based on Artificial Intelligence Algorithm" @default.
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- W4361762397 doi "https://doi.org/10.1007/978-981-99-0923-0_88" @default.
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