Short text classification based on convolutional upsampling feature enhancement
Authors: 
Yukui Lian, Yanzhao Sun, Li Ding, Han Zhang, Jiahui Lu, Shuai GuAuthors Info & Claims
Yukui Lian, Yanzhao Sun, Li Ding, Han Zhang, Jiahui Lu, Shuai GuAuthors Info & ClaimsArticle No.: 19, Pages 1 - 5
Abstract
Short text can lead to sparse feature representation and classification inaccuracies due to noise and other issues. To address this, we propose a short text classification model that uses convolutional upsampling feature enhancement. Our approach involves using a multi-scale convolutional neural network to extract deep features of different dimensions. Secondly, we enhance the deep features by upsampling convolution and obtain more discriminative features for text classification by downsampling. Finally, we use an end-to-end approach to output the text categories. Experimental validation on a public dataset shows that our proposed feature enhancement approach significantly improves the model's performance in short text categorization.
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Published In
April 2024
381 pages
ISBN:9798400716393
DOI:10.1145/3661725
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 20 June 2024
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CMLDS 2024
CMLDS 2024: 2024 International Conference on Computing, Machine Learning and Data Science
April 12 - 14, 2024
Singapore, Singapore
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