Skip to main content
SpringerLink
Log in

Personalized approach for automated question answering in restricted domain

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Question answering personalization is an emergent research area with the intention to make possible for the user to have control over the rendering of answers according to their topic of interest. This paper presents a personalized approach to question answering based on end user modelling. The personalization of the retrieved data is done using implicit user information and interest area. As the customized data is refined using attributes and values, we implement several similarity metrics. These metrics consider both semantic and syntactic user information. Our evaluation with respect to a baseline QA system gives encouraging result in personalization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Maybury MT (2003) Towards a question answering roadmap. In: New directions in question answering, pp 8–11

  2. Voorhees EM (2003) Overview of the TREC 2003 question answering track. In: Proceedings of TREC’03, pp 1–13

  3. Pitkow J, Schuetze H, Cass T, Cooley R, Turnbull D, Edmonds A, Adar E, Breuel T (2002) Personalized search. Commun ACM 45(9):50–55

    Article  Google Scholar 

  4. Teevan J, Dumais ST, Horvitz E (2005) Personalizing search via automated analysis of interests and activities. In: Proceedings of SIGIR’05. ACM Press, , New York, pp 449–456

  5. Ardissono L, Console L, Torre I (2001) An adaptive system for the personalized access to news. AI Commun 14(3):129–147

    MATH  Google Scholar 

  6. Magnini B, Strapparava C (2001) Improving user modelling with content-based techniques. In: User modelling: proceedings of the 8th international conference, LNCS, vol. 2109. Springer, Berlin, Heidelberg, pp 74–83

    Chapter  Google Scholar 

  7. Miller B, Albert I, Lam SK, Konstan J, Riedl J (2003) MovieLens unplugged: experiences with a recommender system on four mobile devices. In: People people and computers XVII—designing for society. Springer, London, pp 263–279

    Chapter  Google Scholar 

  8. Person NK, Craig S, Price P, Hu X, Gholson B, Graesser AC (2000) Incorporating human-like conversational behaviors into autotutor. In: Agents 2000 proceedings of the workshop on achieving human-like behavior in the interactive animated agents, pp 85–92

  9. Romero C, Ventura S, de Bra P, de Castro C (2003) Discovering prediction rules in AHA! Courses. In: User modelling: proceedings of the 9th international conference, LNAI/LNCS, vol. 2702. Springer, Berlin, Heidelberg, pp 25–34

    Chapter  Google Scholar 

  10. Linton Fr, Goodman B, Gaimar R, Zarrella J, Ross H (2003) Student modeling for an intelligent agent in a collaborative learning environment. In: Proceedings of UM’03. Springer, Berlin, Heidelberg, pp 342–351

    Chapter  Google Scholar 

  11. Baeza-Yates R, Boldi P, Bozzon A et al (2011) Trends in search interaction. In: Ceri S, Brambilla M (eds) Search computing. Springer, Heidelberg, pp 26–32

    Chapter  Google Scholar 

  12. Koutrika G, Ioannidis Y (2005) A unified user profile framework for query disambiguation and personalization. In: Proceedings of workshop on new technologies for personalized information access, pp 44–53

  13. Kim HR, Chan PK (2003) Learning implicit user interest hierarchy for context in personalization. In: Proc. 8th international conference on intelligent user interfaces, pp 101–108

  14. Mobasher B (2007) Data mining for web personalization. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web. Springer, Heidelberg, pp 90–135

    Chapter  Google Scholar 

  15. Perkowitz M, Etzioni O (1998) Adaptive web sites: automatically synthesizing web pages. In: Proc. 15th national/10th conference on artificial intelligence/innovative applications of artificial intelligence, pp 727–732

  16. Gauch S, Speretta M, Chandramouli A, Micarelli A (2007) User profiles fo personalized information access. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web. Springer, Heidelberg, pp 54–89

    Chapter  Google Scholar 

  17. Mianowska B, Nguyen NT (2011) Using knowledge integration techniques for user profile adaptation method in document retrieval systems. In: Transactions on computational collective intelligence. Springer, Berlin, pp 140–156

    Chapter  Google Scholar 

  18. Kumar R, Sharan A (2014) Personalized web search using browsing history and domain knowledge. In: Proc. international conference on issues and challenges in intelligent computing technique. IEEE, pp 493–497

  19. Hickl A, Harabagiu S (2006) Enhanced interactive question answering with conditional random fields. In: Proceedings of IQA’06 Workshop at HLT-NAACL 2006. Association for Computational Linguistics, pp 25–32

  20. Thai V, O’Riain S, Davis B, O’Sullivan D (2006) Personalized question answering: a use case for business analysis. In: Proc. international conference on applications and business aspects of the semantic Web. CEUR-WS. org, 226, pp 61–73

  21. Zhang P, Wu C, Wang C, Huang X (2006) Personalized question answering system based on ontology and semantic web. In: 2006 4th IEEE international conference on industrial informatic. IEEE, pp 1046–1051

  22. Hadjouni M, Haddad MR, Baazaoui H, Ghezala HB (2009) Personalized information retrieval approach. In : WISM in conjunction with the 21st Int. Conf. on Advanced Information Systems: CAiSE'09

  23. Quarteroni S, Manandhar S (2009) Designing an interactive open-domain question answering system. Nat Lang Eng 15(01):73–95

    Article  Google Scholar 

  24. Wu Z, Palmer M (1994) Verb semantics and lexical selection. In: Proceedings of 32nd annual meeting of the association for computational linguistics, Las Cruces, New Mexico

  25. Singh V, Dwivedi SK (2015) Two way question classification in higher education domain. Int J Mod Educ Comput Sci 7(9):59

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. B. B. Ambedkar University, Lucknow, India

    Vaishali Singh & Sanjay K. Dwivedi

Authors
  1. Vaishali Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjay K. Dwivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vaishali Singh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, V., Dwivedi, S.K. Personalized approach for automated question answering in restricted domain. Int. j. inf. tecnol. 12, 223–229 (2020). https://doi.org/10.1007/s41870-018-0200-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-018-0200-6

Keywords

Search

Navigation