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

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Abstract

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.

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Acknowledgments

We would like to thank ICT big data system team members and SOHU mobile research development team members, especially Jian Lin and Wei Liu to help give good suggestions in this paper. This research is supported partly by the Hi-Tech Research and Development (863) Program of China (Grant No. 2013AA01A213).

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Zhaoxin Fan & Shuoying Chen

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Li Zha

  3. Sohu.com Inc, Beijing, China

    Jiadong Yang

Authors
  1. Zhaoxin Fan
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  2. Shuoying Chen
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  3. Li Zha
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  4. Jiadong Yang
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Correspondence to Zhaoxin Fan.

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Fan, Z., Chen, S., Zha, L. et al. A Text Clustering Approach of Chinese News Based on Neural Network Language Model. Int J Parallel Prog 44, 198–206 (2016). https://doi.org/10.1007/s10766-014-0329-2

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  • DOI: https://doi.org/10.1007/s10766-014-0329-2

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