Falcon: A Novel Chinese Short Text Classification Method

• 作者: Haiming Li Haining Huang Jingu Qian Xiang Cao
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: SHORT TEXT Classification Word VECTOR Representation One-Hot Densenet NETWORKS Convolutional NEURAL NETWORKS
• 年，卷（期）: 2018,（11）
• 页码范围: 216-226
• 页数: 11页
• 分类号: TP39

摘要

For natural language processing problems, the short text classification is still a research hot topic, with obviously problem in the features sparse, high-dimensional text data and feature representation. In order to express text directly, a simple but new variation which employs one-hot with low-dimension was proposed. In this paper, a Densenet-based model was proposed to short text classification. Furthermore, the feature diversity and reuse were implemented by the concat and average shuffle operation between Resnet and Densenet for enlarging short text feature selection. Finally, some benchmarks were introduced to evaluate the Falcon. From our experimental results, the Falcon method obtained significant improvements in the state-of-art models on most of them in all respects, especially in the first experiment of error rate. To sum up, the Falcon is an efficient and economical model, whilst requiring less computation to achieve high performance.