Question Answering Based on Answer Trustworthiness

  • Hyo-Jung Oh
  • Chung-Hee Lee
  • Yeo-Chan Yoon
  • Myung-Gil Jang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5839)


Nowadays, we are faced with finding “trustworthy” answers not only “relevant” answers. This paper proposes a QA model based on answer trustworthiness. Contrary to the past researches which focused simple trust factors of a document, we identified three different answer trustworthiness factors: 1) incorporating document quality at the document layer; 2) representing the authority and reputation of answer sources at the answer source layer; 3) verifying the answers by consulting various QA systems at the sub-QAs layer. In our experiments, the proposed method using all answer trustworthiness factors shows improvement: 237% (0.150 to 0.506 MRR) for answering effectiveness and 92% (28,993 to 2,293 min.) for indexing efficiency.


Question answering answer trustworthiness document quality 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ko, J.W., Si, L., Eric, N.: A Probabilistic Graphical Model for Joing Answer Ranking in Question Answering. In: The 30th annual international ACM SIGIR conference, pp. 343–350. ACM Press, New York (2007)Google Scholar
  2. 2.
    Jeon, J.W., Croft, W.B., et al.: A framework to predict the quality of answers with non-textual features. In: The 29th annual international ACM SIGIR conference, pp. 228–235. ACM Press, New York (2006)Google Scholar
  3. 3.
    Su, Q., Pavlov, D., Chow, J.-H., Baker, W.C.: Internet-scale collection of human-reviewed data. In: The 16th international conference on WWW conference, pp. 231–240. ACM Press, New York (2007)Google Scholar
  4. 4.
    Agichtein, E., Castillo, C., Donato, D.: Finding High-Quality Content in Social Media. In: Web Search and Data Mining (WSDM), pp. 183–194. ACM Press, Stanford (2008)Google Scholar
  5. 5.
    Cruchet, S., Gaudinat, A., Rindflesch, T., Boyer, C.: What about trust in the Question Answering world? In: AMIA 2009 Annual Symposium (2009)Google Scholar
  6. 6.
    Lee, C.K., Hwang, Y.G., Lim, S.J., et al.: Fine-Grained Named Entity Recognition Using Conditional Random Fields for Question Answering. In: Ng, H.T., Leong, M.-K., Kan, M.-Y., Ji, D. (eds.) AIRS 2006. LNCS, vol. 4182, pp. 581–587. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Choi, M.R., Hur, J., Jang, M.G.: Constructing Korean lexical concept network for encyclopedia question answering system. In: IEEE IECON 2004, pp. 3115–3119. IEEE Press, New York (2004)Google Scholar
  8. 8.
    Oh, H.J., Myaeng, S.H., Jang, M.G.: Strategy-driven Question Answering with Multiple Techniques. ETRI Journal 31(4) (2009)Google Scholar
  9. 9.
    Lee, H.G., Kim, M.J., Rim, H.C., et al.: Document Quality Evaluation for Question Answering System. In: 20th Conference of Hangul and Korean Information Processing (in Korean), pp. 176–181 (2008)Google Scholar
  10. 10.
    Lee, C.K., Jang, M.G.: Fast Training of Structured SVM Using Fixed-Threshold Sequential Minimal Optimization. ETRI Journal 31(2), 121–128 (2009)CrossRefGoogle Scholar
  11. 11.
    Lin, J.: Is question answering better than information retrieval? A task-based evaluation framework for question series. In: HLT/NAACL 2007, pp. 212–219 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hyo-Jung Oh
    • 1
  • Chung-Hee Lee
    • 1
  • Yeo-Chan Yoon
    • 1
  • Myung-Gil Jang
    • 1
  1. 1.Electronics and Telecommunications Research Institute (ETRI)DaejeonKorea

Personalised recommendations