Earticle

초록

영어

Representing words as continuous vectors enables the quantification of semantic relationships of words by vector operations, thereby has attracted much attention recently. This paper proposes an approach to combine continuous word representation and topic modeling, by encoding words based on their topic distributions in the hierarchical softmax, so as to introduce the prior semantic relevance information into the neural networks. The word vectors generated by our model are evaluated with respect to word relevance and the document relevance. Experimental results show that our approach is promising for further improving the quality of word vectors.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Approach
  3.1. Word Encoding via Topic Distributions 
  3.2. Topic Modeling
 4. Experiments
  4.1. Experimental Setting
  4.2. Evaluation Tasks
  4.3. Results and Discussion
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Xin Zhang [ School of Computer Science and Technology, Harbin Institute of Technology, 92 West Da Zhi St, Harbin, China ]
• Bingquan Liu [ School of Computer Science and Technology, Harbin Institute of Technology, 92 West Da Zhi St, Harbin, China ]
• Baoxun Wang [ Application and Service Group, Microsoft, Beijing, China ]
• Xiaolong Wang [ School of Computer Science and Technology, Harbin Institute of Technology, 92 West Da Zhi St, Harbin, China ]
• Deyuan Zhang [ School of Computer, Shenyang Aerospace University, Shenyang, China ]

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