Short Text Classification Based on Cross-Connected GRU Kernel Mapping Support Vector Machine
Qi Wang, Zhaoying Liu, Ting Zhang, Yujian Li · 2021
Support vector machine (SVM) has achieved excellent results in short text classification. However, its performance is limited in the kernel function. This paper presents a short text classification method based on Cross-connected GRU Kernel Mapping Support Vector Machine (C-GRUKMSVM), to further improve the accuracy of short text classification. The method consists of a feature mapping module and a classification module. The feature mapping module first represents the text as a word vector using the glove method, and then explicitly maps the low-dimensional word vector to a high-dimensional space using a three-layer cross-connected GRU; the classification module uses a soft-margin support vector machine for classification. Experimental results on five publicly available short text datasets show that C-GRUKMSVM achieves better text classification performance than convolutional networks, support vector machines and Naïve Bayes. Additionally, different cross-connected methods, recurrent units and recurrent structures have an impact on the performance of C-GRUKMSVM.