Abstract
Question and Answering (QA) Systems provide a platform where users can ask questions in natural language to a system and get answers retrieved from a knowledge base. The work proposed in PythonQA create a Question and Answer System for the Python Programming Language. The knowledge is built from the Python Frequent Answered Questions (PyFAQ). In this paper, we extend the PythonQA system by enhancing the Knowledge Base with Question-Answer pairs from the StackExchange Python Question Answering Community Site. Some tests were performed to analyze the impact of a richer Knowledge Base on the PythonQA system, increasing the number of answer candidates.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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 (2016)
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)
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)
Cao, Y.G., Liu, F., Simpson, P., Antieau, L., Bennett, A., Cimino, J.J., Ely, J., Yu, H.: AskHERMES: an online question answering system for complex clinical questions. J. Biomed. Inform. 44(2), 277–288 (2011)
Clark, A., Fox, C., Lappin, S.: The Handbook of Computational Linguistics and Natural Language Processing. Wiley-Blackwell (2010)
Hoque, M.M., Quaresma, P.: A content-aware hybrid architecture for answering questions from open-domain texts. In: 2016 19th International Conference on Computer and Information Technology (ICCIT), pp. 293–298 (2016)
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, pp. 314–317 (2015)
Lende, S.P., Raghuwanshi, M.M.: Question answering system on education acts using NLP techniques. In: IEEE WCTFTR - Proceedings of 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (2016)
Ramos, M., Pereira, M.J.V., Henriques, P.R.: A QA system for learning python. In: Communication Papers of the 2017 FedCSIS, Prague, Czech Republic (2017)
Rossum, G.: Python reference manual. Technical report, Amsterdam, The Netherlands (1995)
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
de Azevedo, R.P., Henriques, P.R., Pereira, M.J.V. (2018). 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'18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-77703-0_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-77703-0_56
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77702-3
Online ISBN: 978-3-319-77703-0
eBook Packages: EngineeringEngineering (R0)