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https://doi.org/10.3115/v1/P14-1105

has been cited by the following article:

• TITLE: Falcon: A Novel Chinese Short Text Classification Method

  AUTHORS: Haiming Li, Haining Huang, Xiang Cao, Jingu Qian

  KEYWORDS: Short Text Classification, Word Vector Representation, One-Hot, Densenet Networks, Convolutional Neural Networks

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.11, November 27, 2018

  ABSTRACT: 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.