Earticle

초록

영어

Every day, there are a large number of new questions produce in question answering community, how to find the answer user for the question and sort candidate answers is the research content of this paper. First of all ,we use statistical language model to model for user interest, make full use of the abundant personalized information in question answering community to find out user interest distribution, and obtain the user list of question recommendation by introducing the query likelihood language model to calculate the degree of user interest to the new question. Secondly, we calculate the matching degree of question and candidate answers through fusing the feature of word form, word order, distance and semantic. The candidate answers of question will be sorted automatically, making it easier for users to choose the best answer. Experiments are performed on data sets extracted from the Baidu know, experimental results show that the method proposed in this paper has better performance.

목차

Abstract
 1. Introduction
 2. Question Recommendation Based on User Interest
  2.1 Definition of Question Recommendation
  2.2 Construction of User Interest Model
  2.3 Estimation of Language Model
 3 Answer Extraction Based on Feature Fusion
  3.1. Definition of Answer Extraction
  3.2. Similarity Calculation
 4 Experimental Results and Analysis
  4.1. Experimental Data
  4.2. Evaluation Tools
  4.3. Result Analysis
 5. Conclusion
 References

키워드

저자

• Yang Xianfeng [ Yang Xianfeng, School of Information Engineering, Henan Institute of Science and Technology, Henan Xinxiang, China ]
• Liu Pengfei [ HEBI Colleges of Vocation and Technology, Henan Hebi, 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