Skip to main content
SpringerLink
Log in

DSL Based Automatic Generation of Q&A Systems

  • Conference paper
  • First Online:
Book cover New Knowledge in Information Systems and Technologies (WorldCIST'19 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 930))

Included in the following conference series:

Abstract

In order to help the user to search for relevant information, Question and Answering (Q&A) Systems provide the possibility to formulate the question freely in a natural language, retrieving the most appropriate and concise answers. These systems interpret the user’s question to understand his information needs and return him the more adequate replies in a semantic sense; they do not perform a statistical word search, thus differing from the existing search engines. There are several approaches to develop and deploy Q&A Systems, making hard to choose the best way to build the system. To turn easier this process, we are proposing a way to create automatically Q&A Systems based on DSLs (Domain-specific Languages), thus allowing the setup and the validation of the Q&A System to be independent of the implementation techniques. With our proposal, we want the developers to focus on the data and contents, instead of implementation details.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.google.com.

  2. 2.

    https://www.bing.com.

References

  1. Adam, S., Schultz, U.P.: Towards tool support for spreadsheet-based domain-specific languages. In: ACM SIGPLAN Notices, vol. 51, pp. 95–98. ACM (2015)

    Google Scholar 

  2. Ansari, A., Maknojia, M., Shaikh, A.: Intelligent question answering system based on Artificial Neural Network. In: 2016 IEEE International Conference on Engineering and Technology (ICETECH), pp. 758–763. IEEE, March 2016. http://ieeexplore.ieee.org/document/7569350/

  3. Azevedo, R., Henriques, P.R., Pereira, M.J.V.: Extending PythonQA with knowledge from StackOverflow. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds.) Trends and Advances in Information Systems and Technologies, WorldCist 2018, Advances in Intelligent Systems and Computing, 1st edn., vol. 745, pp. 568–575. Springer, Heidelberg (2018)

    Google Scholar 

  4. Balakrishna, M., Werner, S., Tatu, M., Erekhinskaya, T., Moldovan, D.: K-extractor: automatic knowledge extraction for hybrid question answering. In: Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016 (2016)

    Google Scholar 

  5. Ben Abacha, A., Zweigenbaum, P.: MEANS: a medical question-answering system combining NLP techniques and semantic Web technologies. Inf. Process. Manag. 51(5), 570–594 (2015). https://doi.org/10.1016/j.ipm.2015.04.006

    Article  Google Scholar 

  6. Besbes, G., Baazaoui-Zghal, H., Ghezela, H.B.: An ontology-driven visual question-answering framework. In: Proceedings of the International Conference on Information Visualisation, September 2015, pp. 127–132 (2015)

    Google Scholar 

  7. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python, 1st edn. O’Reilly Media, Inc., Newton (2009)

    Google Scholar 

  8. Cao, Y.G., et al.: AskHERMES: an online question answering system for complex clinical questions. J. Biomed. Inf. 44(2), 277–288 (2011). https://doi.org/10.1016/j.jbi.2011.01.004

    Article  Google Scholar 

  9. Clark, A., Fox, C., Lappin, S.: The Handbook of Computational Linguistics and Natural Language Processing. Wiley-Blackwell, Hoboken (2010)

    Book  Google Scholar 

  10. Cointe, P.: Towards generative programming. In: Unconventional Programming Paradigms, pp. 315–325. Springer (2005)

    Google Scholar 

  11. Czarnecki, K.: Generative programming: principles and techniques of software engineering based on automated configuration and fragment-based component models. Ph.D. thesis, Technical University of Ilmenau (1999)

    Google Scholar 

  12. Czarnecki, K.: Overview of generative software development. In: Unconventional Programming Paradigms, pp. 326–341. Springer (2005)

    Google Scholar 

  13. Etworks, S.E.L.F.A.N.: R-Net: Machine Reading Comprehension With Self-Matching Networks*, pp. 1–11 (2017). https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf

  14. Fang, H., Gupta, S., Iandola, F., Srivastava, R.K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J.C., Zitnick, C.L., Zweig, G.: From captions to visual concepts and back. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07–12 June, pp. 1473–1482 (2015)

    Google Scholar 

  15. Ferrucci, D.: Build Watson: an overview of DeepQA for the Jeopardy! Challenge. In: 2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT), p. 1 (2010)

    Google Scholar 

  16. Fortnow, L., Homer, S.: A short history of computational complexity. Technical report, Boston University Computer Science Department (2003)

    Google Scholar 

  17. Fowler, M.: Domain-Specific Languages. Pearson Education, London (2010)

    Google Scholar 

  18. Ghosh, D.: DSLs in Action. Manning Publications Co., Shelter Island (2010)

    Google Scholar 

  19. Hoque, M.M., Quaresma, P.: A content-aware hybrid architecture for answering questions from open-domain texts. In: 19th International Conference on Computer and Information Technology (2016)

    Google Scholar 

  20. Huang, X., Wei, B., Zhang, Y.: Automatic question-answering based on Wikipedia data extraction. In: 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015, Taipei, Taiwan, 24–27 November 2015, pp. 314–317 (2015). https://doi.org/10.1109/ISKE.2015.78

  21. Wolfram Research Inc.: Wolfram Alpha (2018)

    Google Scholar 

  22. Jain, A., Kulkarni, G., Shah, V.: Natural language processing. Int. J. Comput. Sci. Eng. (2018)

    Google Scholar 

  23. Jayalakshmi, S., Sheshasaayee, A.: Automated question answering system using ontology and semantic role. In: International Conference on Innovative Mechanisms for Industry Applications (ICIMIA 2017), pp. 528–532. No. Icimia (2017)

    Google Scholar 

  24. Kaisser, M., Becker, T.: Question answering by searching large corpora with linguistic methods. In: TREC (2004)

    Google Scholar 

  25. Kalaivani, S., Duraiswamy, K.: Comparison of question answering systems based on ontology and semantic web in different environment. J. Comput. Sci. 8(8), 1407–1413 (2012)

    Google Scholar 

  26. Lende, S.P., Raghuwanshi, M.M.: Question answering system on education acts using NLP techniques. In: IEEE WCTFTR 2016 - Proceedings of 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (2016)

    Google Scholar 

  27. Mernik, M., Heering, J., Sloane, A.M.: When and how to develop domain-specific languages. ACM Comput. Surv. (CSUR) 37(4), 316–344 (2005)

    Article  Google Scholar 

  28. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). http://portal.acm.org/citation.cfm?doid=219717.219748

    Article  Google Scholar 

  29. Mochalova, V.A., Kuznetsov, V.A.: Ontological-semantic text analysis and the question answering system using data from ontology. ICACT Trans. Adv. Commun. Technol. (TACT) 4(4), 651–658 (2015)

    Google Scholar 

  30. Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., Deng, L.: MS MARCO: a human generated MAchine reading COmprehension dataset. In: CEUR Workshop Proceedings 1773 (Nips), pp. 1–10 (2016)

    Google Scholar 

  31. Och, F.: Minimum error rate training in statistical machine translation. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 1, pp. 160–167 (2003). http://dl.acm.org/citation.cfm?id=1075117

  32. Packowski, S., Lakhana, A.: Using IBM watson cloud services to build natural language processing solutions to leverage chat tools. In: Proceedings of the 27th Annual International Conference on Computer Science and Software Engineering, No. November, IBM Corp., Markham, Ontario, Canada, pp. 211–218 (2017). http://dl.acm.org/citation.cfm?id=3172795.3172819

  33. Rajendran, P.S., Sharon, R.: Dynamic question answering system based on ontology. In: 2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp), pp. 1–6. IEEE, December 2017. http://ieeexplore.ieee.org/document/8280094/

  34. Ramos, M., Pereira, M.J.V., Henriques, P.R.: A QA system for learning Python. In: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, 3–6 September 2017, pp. 157–164 (2017). https://doi.org/10.15439/2017F157

  35. Sasikumar, U., Sindhu, L.: A survey of natural language question answering system. Int. J. Comput. Appl. 108(15) (2014). ISSN 0975-8887

    Google Scholar 

  36. Shen, Y., Huang, P.S., Chang, M.W., Gao, J.: Modeling large-scale structured relationships with shared memory for knowledge base completion. In: Proceedings of the 2nd Workshop on Representation Learning for NLP (2017). http://arxiv.org/abs/1611.04642

  37. Vargas-Vera, M., Lytras, M.D.: AQUA: a closed-domain question answering system. Inf. Syst. Manag. 27(3), 217–225 (2010)

    Article  Google Scholar 

  38. Weissenborn, D., Wiese, G., Seiffe, L.: FastQA: a simple and efficient neural architecture for question answering. arXiv preprint arXiv:1703.04816 (2017)

Download references

Acknowledgement

This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.

Author information

Authors and Affiliations

  1. Centro Algoritmi (CAlg-CTC), Dep. Informática, Universidade do Minho, Braga, Portugal

    Renato Preigschadt de Azevedo & Pedro Rangel Henriques

  2. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal

    Maria João Varanda Pereira

Authors
  1. Renato Preigschadt de Azevedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria João Varanda Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro Rangel Henriques
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Renato Preigschadt de Azevedo .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Faculdade de Engenharia/LIACC, Universidade do Porto, Porto, Portugal

    Luís Paulo Reis

  4. DIMES, Università della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

de Azevedo, R.P., Pereira, M.J.V., Henriques, P.R. (2019). DSL Based Automatic Generation of Q&A Systems. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-030-16181-1_44

Download citation

Publish with us

Policies and ethics

Search

Navigation