Abstract
Question Answering systems (QASs) is a system that provide answers to the question or query asked by the user in the natural language. It retrieves small portion of text from the collection of document which contains the answer of the user’s question. Therefore to retrieve such an accurate and precise answer from the collection of document, Information Retrieval (IR) Techniques are required and to process or understand the user’s question posed in the natural language (NLP) Natural Language Techniques are used.In this survey paper we will see what exactly a Question Answering System is, previous work done on such Question Answering system and we will also compare research against each other with respect to the different approaches that were followed and components that were used. At the end, the survey gives a clear comparison between the different QASs and idea of the our proposed QAS model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cimiano P, Unger C, McCrae J (2014) Ontology-based interpretation of natural language, Morgan & Claypool Publishers. Accessed 1 Mar 2014. ISBN 978-1-60845-990-2
Saini A, Yadav PK (2017) A survey on question–answering system. Int J Eng Comput Sci 6(3):20453–20457. https://doi.org/10.18535/ijecs/v6i3.09. ISSN:2319-7242
Tirpude S, Alvi AS (2015) Department of computer science & engineering closed domain question answering system: a survey. IJIFR/ V2/ E9/ 065
Bhardwaj D, Pakray P, Bentham J, Saha S (2016) Question answering system for frequently asked questions, Department of CSE NIT, Mizoram, India
Fu J (2009) Domain ontology based automatic question answering
Tahri A, Tibermacine O (2013) DBPedia based factoid question answering system. Int J Web Semant Technol (IJWesT) 4(3):23
Pragisha K, Reghuraj PC (2014) A natural language question answering system in malayalam using domain dependent document collection as repository. Int J Comput Linguist Nat Lang Process 3(3):0756–2279
Ryu P-M, Jang M-G, Kim H-K (2014) Open domain question answering using wikipedia- based knowledge model. Inf Process Manag 50:683–692
Zhang D, Lee WS (2003) A web-based question answering system
Sahu S, Vashnik N, Roy D (2012) Prashnottar: a Hindi question answering system. Int J Comput Sci Inf Technol (IJCSIT) 4(2):149–158
Kumar P, Kashyap S, Mittal A, Gupta S (2005) A Hindi question answering system for E-learning documents. In: Proceedings of IEEE international conference on intelligent sensing and information processing, Bangalore, India, pp 80–85
Bhoir VM, Potey A (2014) Question answering system: a heuristic approach. In: 2014, IEEE fifth international conference on applications of digital information and web technologies
Moussa AM, Abdel-Kader R (2011) QASYO: a question answering system for YAGO ontology. Int J Database Theory Appl 4(2):99
Li Y, Bontcheva K, Cunningham H (2004) SVM based learning system for information extraction, Department of Computer Science, the University of Sheffield, Sheffield, S1 4DP, UK
Cristianini C, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge
Bikel D, Schwartz R, Weischedel R (1999) An algorithm that learns what’s in a name. Mach Learn 34(1–3):211–231
Bizer C, Lehmann J, Kobilarov G, Auer S, Becker C, Cyganiak R, Hellmann S, (2009) DBPedia - a crystallization point for the web of data (PDF). Web Semant: Sci Serv Agents World Wide Web 7(3):154–165 https://doi.org/10.1016/j.websem.2009.07.002. ISSN 1570-8268, CiteSeerX 10.1.1.150.4898
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sultana, T., Badugu, S. (2020). A Review on Different Question Answering System Approaches. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_67
Download citation
DOI: https://doi.org/10.1007/978-3-030-24318-0_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24317-3
Online ISBN: 978-3-030-24318-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)