Document Open Access Logo

Development of Q&A Systems Using AcQA

Authors Renato Preigschadt de Azevedo , Maria João Varanda Pereira , Pedro Rangel Henriques

PDF
Thumbnail PDF

File

OASIcs.SLATE.2020.8.pdf
  • Filesize: 1 MB
  • 15 pages

Document Identifiers

Author Details

Renato Preigschadt de Azevedo
  • Centro Algoritmi (CAlg-CTC), Department of Informatics, University of Minho, Braga, Portugal
Maria João Varanda Pereira
  • Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Portugal
Pedro Rangel Henriques
  • Centro Algoritmi (CAlg-CTC), Department of Informatics, University of Minho, Braga, Portugal

Funding

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Projects Scopes: UIDB/05757/2020 and UIDB/00319/2020.

Cite AsGet BibTex

Renato Preigschadt de Azevedo, Maria João Varanda Pereira, and Pedro Rangel Henriques. Development of Q&A Systems Using AcQA. In 9th Symposium on Languages, Applications and Technologies (SLATE 2020). Open Access Series in Informatics (OASIcs), Volume 83, pp. 8:1-8:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/OASIcs.SLATE.2020.8

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 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 like happens in the existing search engines. There are several approaches to developing and deploying Q&A Systems, making it hard to choose the best way to build the system. To turn easier this process, we are proposing a way to automatically create Q&A Systems (AcQA) based on DSLs, thus allowing the setup and the validation of the Q&A System independent of the implementation techniques. With our proposal (AcQA language), we want the developers to focus on the data and contents, instead of implementation details. We conducted an experiment to assess the feasibility of using AcQA. The study was carried out with people from the computer science field and shows that our language simplifies the development of a Q&A System.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Source code generation
  • Software and its engineering → Domain specific languages
  • Software and its engineering → Design languages
Keywords
  • Question & Answering
  • DSL
  • Natural Language Processing

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Sorin Adam and Ulrik Pagh Schultz. Towards tool support for spreadsheet-based domain-specific languages. In ACM SIGPLAN Notices, volume 51, pages 95-98. ACM, 2015. Google Scholar
  2. Ahlam Ansari, Moonish Maknojia, and Altamash Shaikh. Intelligent question answering system based on Artificial Neural Network. In 2016 IEEE International Conference on Engineering and Technology (ICETECH), pages 758-763. IEEE, March 2016. URL: https://doi.org/10.1109/ICETECH.2016.7569350.
  3. Renato Azevedo, Pedro Rangel Henriques, and Maria João Varanda Pereira. Extending PythonQA with Knowledge from StackOverflow. In Álvaro Rocha, Hojjat Adeli, Luís Paulo Reis, and Sandra Costanzo, editors, Trends and Advances in Information Systems and Technologies, WorldCist2018, volume 745 of Advances in Intelligent Systems and Computing, pages 568-575. Springer International Publishing, 1 edition, 2018. URL: https://doi.org/10.1007/978-3-319-77703-0_56.
  4. Mithun Balakrishna, Steven Werner, Marta Tatu, Tatiana Erekhinskaya, and Dan Moldovan. K-Extractor: Automatic Knowledge Extraction for Hybrid Question Answering. In Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016, 2016. URL: https://doi.org/10.1109/ICSC.2016.30.
  5. Victoria Beltran. Characterization of web single sign-on protocols. IEEE Communications Magazine, 54(7):24-30, 2016. Google Scholar
  6. Asma Ben Abacha and Pierre Zweigenbaum. MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies. Information Processing and Management, 51(5):570-594, 2015. URL: https://doi.org/10.1016/j.ipm.2015.04.006.
  7. Ghada Besbes, Hajer Baazaoui-Zghal, and Henda Ben Ghezela. An ontology-driven visual question-answering framework. Proceedings of the International Conference on Information Visualisation, 2015-Septe:127-132, 2015. URL: https://doi.org/10.1109/iV.2015.32.
  8. Steven Bird, Ewan Klein, and Edward Loper. Natural Language Processing with Python. O'Reilly Media, Inc., 1st edition, 2009. Google Scholar
  9. Daniel G Bobrow. A question-answering system for high school algebra word problems. In Proceedings of the October 27-29, 1964, fall joint computer conference, part I, pages 591-614. ACM, 1964. Google Scholar
  10. Yong Gang Cao, Feifan Liu, Pippa Simpson, Lamont Antieau, Andrew Bennett, James J. Cimino, John Ely, and Hong Yu. AskHERMES: An online question answering system for complex clinical questions. Journal of Biomedical Informatics, 44(2):277-288, 2011. URL: https://doi.org/10.1016/j.jbi.2011.01.004.
  11. Alexander Clark, Chris Fox, and Shalom Lappin. The Handbook of Computational Linguistics and Natural Language Processing. Wiley-Blackwell, 2010. Google Scholar
  12. Pierre Cointe. Towards generative programming. In Unconventional Programming Paradigms, pages 315 - -325. Springer, 2005. Google Scholar
  13. Krzysztof Czarnecki. Overview of generative software development. In Unconventional Programming Paradigms, pages 326-341. Springer, 2005. Google Scholar
  14. Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh K. Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C. Platt, C. Lawrence Zitnick, and Geoffrey Zweig. From captions to visual concepts and back. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June:1473-1482, 2015. URL: https://doi.org/10.1109/CVPR.2015.7298754.
  15. D Ferrucci. Build watson: An overview of DeepQA for the Jeopardy! Challenge. In 2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT), page 1, 2010. Google Scholar
  16. Lance Fortnow and Steve Homer. A short history of computational complexity. Technical report, Boston University Computer Science Department, 2003. Google Scholar
  17. Martin Fowler. Domain-specific languages. Pearson Education, 2010. Google Scholar
  18. Debasish Ghosh. DSLs in action. Manning Publications Co., 2010. Google Scholar
  19. Fernand Gobet, Jean Retschitzki, and Alex de Voogt. Moves in mind: The psychology of board games. Psychology Press, 2004. Google Scholar
  20. David C Gondek, Adam Lally, Aditya Kalyanpur, J William Murdock, Pablo Ariel Duboué, Lei Zhang, Yue Pan, Zhao Ming Qiu, and Chris Welty. A framework for merging and ranking of answers in deepqa. IBM Journal of Research and Development, 56(3.4):14-1, 2012. Google Scholar
  21. Md Moinul Hoque and Paulo Quaresma. A Content-Aware Hybrid Architecture for Answering Questions from Open-domain Texts. In 19th International Conference on Computer and Information Technology, 2016. Google Scholar
  22. Xiangzhou Huang, Baogang Wei, and Yin Zhang. Automatic Question-Answering Based on Wikipedia Data Extraction. In 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015, Taipei, Taiwan, November 24-27, 2015, pages 314-317, 2015. URL: https://doi.org/10.1109/ISKE.2015.78.
  23. Inc., Wolfram Research. Wolfram Alpha, 2018. Google Scholar
  24. Aditya Jain, Gandhar Kulkarni, and Vraj Shah. Natural language processing. International Journal of Computer Sciences and Engineering, 2018. Google Scholar
  25. Michael Kaisser and Tilman Becker. Question Answering by Searching Large Corpora With Linguistic Methods. In TREC, 2004. Google Scholar
  26. S. Kalaivani and K. Duraiswamy. Comparison of question answering systems based on ontology and semantic web in different environment. Journal of Computer Science, 8(8):1407-1413, 2012. URL: https://doi.org/10.3844/jcssp.2012.1407.1413.
  27. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. Biobert: pre-trained biomedical language representation model for biomedical text mining. arXiv preprint, 2019. URL: http://arxiv.org/abs/1901.08746.
  28. Sweta P. Lende and M. M. Raghuwanshi. 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. URL: https://doi.org/10.1109/STARTUP.2016.7583963.
  29. Marjan Mernik, Jan Heering, and Anthony M Sloane. When and how to develop domain-specific languages. ACM computing surveys (CSUR), 37(4):316-344, 2005. Google Scholar
  30. V. A. Mochalova, Kuznetsov V. A., Mochalov V., and A. Ontological-semantic text analysis and the question answering system using data from ontology. ICACT Transactions on Advanced Communications Technology (TACT) Vol., 4(4):651-658, 2015. Google Scholar
  31. FJ Och. Minimum error rate training in statistical machine translation. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1, volume 1, pages 160-167, 2003. URL: https://doi.org/10.3115/1075096.1075117.
  32. Terence Parr. The Definitive ANTLR 4 Reference. Pragmatic Bookshelf, 2nd edition, 2013. Google Scholar
  33. Warren J. Plath. Request: A natural language question-answering system. IBM Journal of Research and Development, 20(4):326-335, 1976. Google Scholar
  34. P Selvi Rajendran and Rufina Sharon. Dynamic question answering system based on ontology. In 2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp), pages 1-6. IEEE, December 2017. URL: https://doi.org/10.1109/ICSOFTCOMP.2017.8280094.
  35. Marcos Ramos, Maria João Varanda Pereira, and Pedro Rangel Henriques. 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, September 3-6, 2017., pages 157-164, 2017. URL: https://doi.org/10.15439/2017F157.
  36. Unmesh Sasikumar and L Sindhu. A Survey of Natural Language Question Answering System. International Journal of Computer Applications, 108(15), 2014. Google Scholar
  37. William Stallings. Cryptography and network security: principles and practice. Pearson Upper Saddle River, 2017. Google Scholar
  38. Maria Vargas-Vera and Miltiadis D Lytras. Aqua: A closed-domain question answering system. Information Systems Management, 27(3):217-225, 2010. Google Scholar
  39. David L Waltz. An english language question answering system for a large relational database. Communications of the ACM, 21(7):526-539, 1978. Google Scholar
  40. Dirk Weissenborn, Georg Wiese, and Laura Seiffe. FastQA: A simple and efficient neural architecture for question answering. arXiv preprint, 2017. URL: http://arxiv.org/abs/1703.04816.
  41. Tatu Ylonen. Ssh-secure login connections over the internet. In Proceedings of the 6th USENIX Security Symposium, volume 37, 1996. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact