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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Self-Taught convolutional neural networks for short text clustering
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| 英文论文题目: |
Self-Taught convolutional neural networks for short text clustering
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| 论文题目英文: |
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| 作者: |
Xu, Jiaming
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| 论文出处: |
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| 刊物名称: |
NEURAL NETWORKS
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| 年: |
2017
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| 卷: |
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| 期: |
88 |
| 页: |
22-31 |
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| 影响因子: |
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| 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. |
| 英文摘要: |
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