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	Compositional Recurrent Neural Networks for Chinese Short Text Classification
	Zhou, Yujun1,2,3; Xu, Bo1; Xu, Jiaming1; Yang, Lei1,2,3; Li, Changliang1; Xu, Bo1
	2016
会议日期	October 13-16, 2016
会议地点	Omaha, Nebraska, USA
关键词	Chinese Short Text Text Classification Convolutional Neural Network Recurrent Neural Network Word And Character Embeddings
英文摘要	Word segmentation is the first step in Chinese natural language processing, and the error caused by word segmentation can be transmitted to the whole system. In order to reduce the impact of word segmentation and improve the overall performance of Chinese short text classification system, we propose a hybrid model of character-level and word-level features based on recurrent neural network (RNN) with long short-term memory (LSTM). By integrating character-level feature into word-level feature, the missing semantic information by the error of word segmentation will be constructed, meanwhile the wrong semantic relevance will be reduced. The final feature representation is that it suppressed the error of word segmentation in the case of maintaining most of the semantic features of the sentence.The whole model is finally trained end-to-end with supervised Chinese short text classification task. Results demonstrate that the proposed model in this paper is able to represent Chinese short text effectively, and the performances of 32-class and 5-class categorization outperform some remarkable methods.
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/15618]
专题	数字内容技术与服务研究中心_听觉模型与认知计算
作者单位	1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Jiangsu Jinling Science and Technology Group Co., Ltd, Nanjing
推荐引用方式 GB/T 7714	Zhou, Yujun,Xu, Bo,Xu, Jiaming,et al. Compositional Recurrent Neural Networks for Chinese Short Text Classification[C]. 见:. Omaha, Nebraska, USA. October 13-16, 2016.

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