CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
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
Conference Namethe 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI'16)
Conference DateOctober 13-16, 2016
Conference PlaceOmaha, Nebraska, USA
AbstractWord 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.
KeywordChinese Short Text Text Classification Convolutional Neural Network Recurrent Neural Network Word And Character Embeddings
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15618
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Jiangsu Jinling Science and Technology Group Co., Ltd, Nanjing
Recommended Citation
GB/T 7714
Zhou, Yujun,Xu, Bo,Xu, Jiaming,et al. Compositional Recurrent Neural Networks for Chinese Short Text Classification[C],2016.
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