TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Papers/Light-Weighted CNN for Text Classification

Light-Weighted CNN for Text Classification

Ritu Yadav

2020-04-16Text ClassificationDocument Text ClassificationDocument Image ClassificationDocument Classificationtext-classificationManagementGeneral ClassificationClassification
PaperPDFCode

Abstract

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.

Results

TaskDatasetMetricValueModel
Document Text ClassificationTobacco-3482Accuracy46Optimized Text CNN
Document Text ClassificationTobacco-3482Training time (hours)2Optimized Text CNN
Document Text ClassificationTobacco-3482Accuracy43.5Lightweight TextCNN with Dual Optimizer
Document Text ClassificationTobacco-3482Training time (hours)0.43Lightweight TextCNN with Dual Optimizer
Document Text ClassificationTobacco-3482Accuracy42Lightweight Text CNN
Document Text ClassificationTobacco-3482Training time (hours)1Lightweight Text CNN
Document Text ClassificationTobacco small-3482Accuracy84Optimized Text CNN
Document Text ClassificationTobacco small-3482Training time (min)9Optimized Text CNN
Document Text ClassificationTobacco small-3482Accuracy83Lightweight TextCNN with Dual Optimizer
Document Text ClassificationTobacco small-3482Training time (min)2Lightweight TextCNN with Dual Optimizer
Document Text ClassificationTobacco small-3482Accuracy82.5Lightweight Text CNN
Document Text ClassificationTobacco small-3482Training time (min)5Lightweight Text CNN

Related Papers

Making Language Model a Hierarchical Classifier and Generator2025-07-17Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management2025-07-17Autonomous Resource Management in Microservice Systems via Reinforcement Learning2025-07-17Adversarial attacks to image classification systems using evolutionary algorithms2025-07-17Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation2025-07-16Safeguarding Federated Learning-based Road Condition Classification2025-07-16AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)2025-07-13GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation2025-07-10