Self-Taught convolutional neural networks for short text clustering | |
Xu, Jiaming; Xu, Bo; Wang, Peng; Zheng, Suncong; Tian, Guanhua; Zhao, Jun; Xu, Bo | |
刊名 | NEURAL NETWORKS |
2017-04-01 | |
期号 | 88页码:22-31 |
关键词 | Semantic Clustering Neural Networks Short Text Unsupervised Learning |
DOI | 10.1016/j.neunet.2016.12.008 |
文献子类 | Article |
英文摘要 | Short 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. |
WOS研究方向 | Computer Science ; Neurosciences & Neurology |
语种 | 英语 |
WOS记录号 | WOS:000397959900003 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40804] |
专题 | 数字内容技术与服务研究中心_听觉模型与认知计算 |
推荐引用方式 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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