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Annals of Computer Science and Information Systems, Volume 13

Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems

A QA System for learning Python

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DOI: http://dx.doi.org/10.15439/2017F157

Citation: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 13, pages 157164 ()

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Abstract. This article proposes a Question Answering System that can automatically answer to questions presented in a natural language about the Python programming language. A system of this kind aims at the interaction with a human. Since it is natural for a human to communicate in a natural language, such as Portuguese or English, there is a need for systems that can respond to the user in the same language. When restricted to a closed or specific knowledge domain, these systems can offer satisfiable answers to the posed questions. So, it is expected that the proposed QA System can present reasonable answers to questions about Python. After surveying this emergent working area, that is growing every day, we will present the design and implementation of a Python QA system.


  1. Giuseppe Attardi, Antonio Cisternino, Francesco Formica, Maria Simi, Alessandro Tommasi, and Cesare Zavattari. Piqasso: Pisa question answering system. In TREC, 2001.
  2. Susan Dumais, Michele Banko, Eric Brill, Jimmy Lin, and Andrew Ng. Web question answering: Is more always better? In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 291–298. ACM, 2002.
  3. Oscar Ferrández, Rubén Izquierdo, Sergio Ferrández, and José Luis Vicedo. Addressing ontology-based question answering with collections of user queries. Information Processing & Management, 45(2):175–188, 2009.
  4. Bert F Green Jr, Alice K Wolf, Carol Chomsky, and Kenneth Laughery. Baseball: an automatic question-answerer. In Papers presented at the May 9-11, 1961, western joint IRE-AIEE-ACM computer conference, pages 219–224. ACM, 1961.
  5. Michael Kaisser. The qualim question answering demo: Supplementing answers with paragraphs drawn from wikipedia. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session, pages 32–35. Association for Computational Linguistics, 2008.
  6. Michael Kaisser and Tilman Becker. Question answering by searching large corpora with linguistic methods. In TREC, 2004.
  7. Cody Kwok, Oren Etzioni, and Daniel S Weld. Scaling question answering to the web. ACM Transactions on Information Systems (TOIS), 19(3):242–262, 2001.
  8. Jimmy Lin, Dennis Quan, Vineet Sinha, Karun Bakshi, David Huynh, Boris Katz, and David R Karger. What makes a good answer? the role of context in question answering. In Proceedings of the Ninth IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2003), pages 25–32, 2003.
  9. Diego Mollá and José Luis Vicedo. Question answering in restricted domains: An overview. Computational Linguistics, 33(1):41–61, 2007.
  10. Deepak Ravichandran and Eduard Hovy. Learning surface text patterns for a question answering system. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pages 41–47. Association for Computational Linguistics, 2002.
  11. Maria Vargas-Vera and Miltiadis D Lytras. Aqua: A closed-domain question answering system. Information Systems Management, 27(3):217–225, 2010.
  12. Martin Volk and Rico Sennrich. Disambiguation of english contractions for machine translation of tv subtitles. 2011.