Regularized Graph Convolutional Networks for Short Text Classification
Kshitij Tayal, Nikhil Rao, Saurabh Kumar Agarwal, Xiaowei Jia, Karthik Subbian, Vipin Kumar · 2020
Short text classification is a fundamental problem in natural language processing, social network analysis, and e-commerce.The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning.Simpler methods that rely on bag-of-words representations tend to perform on par with complex deep learning methods.To tackle the limitations of textual features in short text, we propose a Graph-regularized Graph Convolution Network (GR-GCN), which augments graph convolution networks by incorporating label dependencies in the output space.Our model achieves state-of-the-art results on both proprietary and external datasets, outperforming several baseline methods by up to 6% .Furthermore, we show that compared to baseline methods, GR-GCN is more robust to noise in textual features.