A multiple learning model based voting system for news headline classification

Abstract

This paper presents the framework and methodologies of Soochow university team’s news headline classification system for NLPCC 2017 shared task 2. The submitted systems aim to automatically classify each Chinese news headline into one or more predefined categories. We develop a voting system based on convolutional neural networks (CNN), gated recurrent units (GRU), and support vector machine (SVM). Experimental results show that our method achieves a Macro-F1 score of about 81%, outperforming most strong competitors, and ranking at 6th in the 32 participants.