Earticle

초록

영어

The hidden topic model of Chinese text, which possesses complicated semantics, is urgently needed, since China has occupied an increasingly significant role during the booming development of globalization over recent years. This paper details and elaborates the basic process of extracting latent Chinese topics by demonstrating a Chinese topic extraction schema based on Latent Dirichlet Allocation (LDA) model. Furthermore, the application was practiced in CCL, an authoritative Chinese corpus, to extract topics for its nine categories. With rigorous empirical analysis, extracting the LDA results has a considerably higher average precision rate as opposed to other three comparable Chinese topic extraction techniques; however the average recall rate is worse than KNN and almost the same with the PLSI model. Moreover, the recall rate and precision rate of LDA-CH is worse than LDA-EH. Therefore, the LDA model should be improved to adapt to the distinctive feature of Chinese words with the purpose of making it better for Chinese topic extraction.

목차

Abstract
 1. Introduction
 2. Literature Review
  2.1. Research of Topic Extraction for English Text
  2.2. Research of Topic Extraction for Chinese Text
 3. Methodology
  3.1. LDA Model
  3.2. Topic Extraction Model for Chinese Text Based on LDA
 4. Experiments
  4.1. Evaluation Merits
  4.2. The Value of Observed Parameters
  4.3. Other Compared Topic Modeling Techniques
 5. Results
  5.1. Comparison with Other Techniques
  5.2 Comparison between LDA-CH and LDA-EH
 6. Conclusions and Discussion
 Acknowledgements
 References

키워드

저자

• Qihua Liu [ School of Information Technology, Jiangxi University of Finance and Economics ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605