Ranking community answers via analogical reasoning

ABSTRACT

Due to the lexical gap between questions and answers, automatically detecting right answers becomes very challenging for community question-answering sites. In this paper, we propose an analogical reasoning-based method. It treats questions and answers as relational data and ranks an answer by measuring the analogy of its link to a query with the links embedded in previous relevant knowledge; the answer that links in the most analogous way to the new question is assumed to be the best answer. We based our experiments on 29.8 million Yahoo!Answer question-answer threads and showed the effectiveness of the approach.

REFERENCES

• 1: Jiang Bian , Yandong Liu , Eugene Agichtein , Hongyuan Zha, Finding the right facts in the crowd: factoid question answering over social media, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367561]
• 2: Ghahramani, Z., and Heller, K.A. Bayesian Sets. In Proc. NIPS, 2005.
• 3: Jiwoon Jeon , W. Bruce Croft , Joon Ho Lee , Soyeon Park, A framework to predict the quality of answers with non-textual features, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148212]
• 4: Silva, R., Heller, K.A., et al. Analogical Reasoning with Relational Bayesian Sets. In Proc. AISTATS, 2007.

INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Correlation and regression analysis

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.5 On-line Information Services
          Subjects: Web-based services

General Terms:
Algorithms, Experimentation

Keywords:
analogical reasoning, community question answering