A Text Clustering Approach of Chinese News Based on Neural Network Language Model

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• 作者：Zhaoxin Fan ; Shuoying Chen ; Li Zha…
• 关键词：Data mining ; Fuzzy k ; means ; Language model ; Chinese news
• 刊名：International Journal of Parallel Programming
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：44
• 期：1
• 页码：198-206
• 全文大小：391 KB
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• 作者单位：Zhaoxin Fan (1)
  Shuoying Chen (1)
  Li Zha (2)
  Jiadong Yang (3)
  
  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
  3. Sohu.com Inc, Beijing, China
  
• 刊物类别：Computer Science
• 刊物主题：Theory of Computation
  Processor Architectures
  Software Engineering, Programming and Operating Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7640

文摘

Text clustering plays an important role in data mining and machine learning. After years of development, clustering technology has produced a series of theories and methods. However, in the text clustering of Chinese news, the mainstream LDA method suffers a high time complex. In order to improve the speed, this paper puts forward a new method in which neural network language model is first applied to text clustering. Text clustering is first converted to its dual problem called word clustering. With neural network language model, we can get word vector which can be used in the fuzzy k-means of the Chinese news keyword set. Based on the keyword clustering result, we can get text clustering result of Chinese news by a single transition. Experiments have show this method’s running speed is five times faster than LDA. This method has been successfully used in the Sohu news recommendation system currently. Keywords Data mining Fuzzy k-means Language model Chinese news