CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Hierarchical Hybrid Attention Networks for Chinese Conversation Topic Classification
Zhou, Yujun1,2,3; Li, Changliang1; Xu, Bo1; Xu, Jiaming1; Cao, Jie1,2,3; Xu, Bo1
2017
Conference Namethe 24th International Conference On Neural Information Processing (ICONIP)
Conference DateNovember 14-18, 2017
Conference PlaceGuangzhou, China
AbstractTopic classification is useful for applications such as forensics analysis and cyber-crime investigation. To improve the overall performance on the task of Chinese conversation topic classi cation, we propose a hierarchical neural network with automatic semantic features selection, which is a hierarchical architecture that depicts the structure of conversations. The model firstly incorporates speaker information into the character- and word-level attentions and generates sentence representation, then uses attention-based BLSTM to construct the conversation representation. Experimental results on three datasets demonstrate that our model achieves better performance than multiple baselines. It indicates that the proposed architecture can capture the informative and salient features related to the meaning of a conversation for topic classification. And we release the dataset of this paper that can be obtained from https://github.com/njoe9/H-HANs.
KeywordHierarchical Attention Networks Chinese Conversation Topic Classification Recurrent Neural Networks
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15459
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorLi, Changliang
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,Li, Changliang,Xu, Bo,et al. Hierarchical Hybrid Attention Networks for Chinese Conversation Topic Classification[C],2017.
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