CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Self-Taught convolutional neural networks for short text clustering
Xu, Jiaming1; Xu, Bo1; Wang, Peng1; Zheng, Suncong1; Tian, Guanhua1; Zhao, Jun1,2; Xu, Bo1,3
Source PublicationNEURAL NETWORKS
2017-04-01
Issue88Pages:22-31
SubtypeArticle
AbstractShort text clustering is a challenging problem due to its sparseness of text representation. Herewepropose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction method. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets. (C) 2017 Elsevier Ltd. All rights reserved.
KeywordSemantic Clustering Neural Networks Short Text Unsupervised Learning
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1016/j.neunet.2016.12.008
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61602479 ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02070005) ; 61303172 ; 61403385
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000397959900003
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15079
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.NLPR, Beijing, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Xu, Jiaming,Xu, Bo,Wang, Peng,et al. Self-Taught convolutional neural networks for short text clustering[J]. NEURAL NETWORKS,2017(88):22-31.
APA Xu, Jiaming.,Xu, Bo.,Wang, Peng.,Zheng, Suncong.,Tian, Guanhua.,...&Xu, Bo.(2017).Self-Taught convolutional neural networks for short text clustering.NEURAL NETWORKS(88),22-31.
MLA Xu, Jiaming,et al."Self-Taught convolutional neural networks for short text clustering".NEURAL NETWORKS .88(2017):22-31.
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