Papers › Light-Weighted CNN for Text Classification

Light-Weighted CNN for Text Classification

16 Apr 2020arXiv:2004.07922archive 2025-07-28

Ritu Yadav

For management, documents are categorized into a specific category, and to do these, most of the organizations use manual labor. In today's automation era, manual efforts on such a task are not justified, and to avoid this, we have so many software out there in the market. However, efficiency and minimal resource consumption is the focal point which is also creating a competition. The categorization of such documents into specified classes by machine provides excellent help. One of categorization technique is text classification using a Convolutional neural network(TextCNN). TextCNN uses multiple sizes of filters, as in the case of the inception layer introduced in Googlenet. The network provides good accuracy but causes high memory consumption due to a large number of trainable parameters. As a solution to this problem, we introduced a whole new architecture based on separable convolution. The idea of separable convolution already exists in the field of image classification but not yet introduces to text classification tasks. With the help of this architecture, we can achieve a drastic reduction in trainable parameters.

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Code

RituYadav92/Lightweighted-CNN-for-Document-Classification mentioned on GitHubtf report

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Tasks

Classification Document Classification Document Image Classification Document Text Classification General Classification Management Text Classification image-classification text-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Document Text Classification	Tobacco small-3482	Optimized Text CNN	Accuracy	84	#1 of 3	Archive leaderboard	report
Document Text Classification	Tobacco small-3482	Optimized Text CNN	Training time (min)	9	#1 of 3	Archive leaderboard	report
Document Text Classification	Tobacco small-3482	Lightweight TextCNN with Dual Optimizer	Accuracy	83	#2 of 3	Archive leaderboard	report
Document Text Classification	Tobacco small-3482	Lightweight TextCNN with Dual Optimizer	Training time (min)	2	#2 of 3	Archive leaderboard	report
Document Text Classification	Tobacco small-3482	Lightweight Text CNN	Accuracy	82.5	#3 of 3	Archive leaderboard	report
Document Text Classification	Tobacco small-3482	Lightweight Text CNN	Training time (min)	5	#3 of 3	Archive leaderboard	report
Document Text Classification	Tobacco-3482	Optimized Text CNN	Accuracy	46	#1 of 3	Archive leaderboard	report
Document Text Classification	Tobacco-3482	Optimized Text CNN	Training time (hours)	2	#1 of 3	Archive leaderboard	report
Document Text Classification	Tobacco-3482	Lightweight TextCNN with Dual Optimizer	Accuracy	43.5	#2 of 3	Archive leaderboard	report
Document Text Classification	Tobacco-3482	Lightweight TextCNN with Dual Optimizer	Training time (hours)	0.43	#2 of 3	Archive leaderboard	report
Document Text Classification	Tobacco-3482	Lightweight Text CNN	Accuracy	42	#3 of 3	Archive leaderboard	report
Document Text Classification	Tobacco-3482	Lightweight Text CNN	Training time (hours)	1	#3 of 3	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution Depthwise Convolution Depthwise Dilated Separable Convolution Depthwise Separable Convolution Dilated Convolution Pointwise Convolution

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