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IJSTR >> Volume 6 - Issue 2, February 2017 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Arabic Question Answering System Based On Data Mining

[Full Text]

 

AUTHOR(S)

Waheeb Ahmed, Babu Anto P

 

KEYWORDS

Information Retrieval, Information Extraction, Natural Language Processing, Question Answering System, Text Mining

 

ABSTRACT

In this study, we describe An Arabic Question Answering(QA) system based on data mining approach. The system employs text mining techniques to determine the likely answers to factoid questions. It depends mainly on the use of lexical information and does not apply any complex language processing tools such as named entity recognizers, parsers and ontologies. The system achieved an accuracy of 61.5%.

 

REFERENCES

[1] G. Nanda, M. Dua and K. Singla, “A Hindi Question Answering System using Machine Learning Approach”, In Proceedings of the International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), 2016.

[2] Y. Liu, X. Yi, R. Chen, and Y. Song, “A Survey on Frameworks and Methods of Question Answering”, In Proceedings of the 3rd International Conference on Information Science and Control Engineering, IEEE, pp. 115-119, 2016.

[3] H. Hu, "A study on Question Answering System Using Integrated retrieval method", Phd Thesis Submitted to Graduate school of engineering at the University of Tokushima, February, 2006.

[4] S. Tellex, "Pauchok: A Modular Framework for question Answering", Master Thesis Submitted to the Department of Electrical Engineering and computer science, Maccachusetts institute of Technology, June 2003.

[5] H. Sundblad, "Question Classification in Question Answering systems", Phd Thesis Submitted to Department of Computer and information Science at Linkoping University, 2007

[6] Y. Niu, "Analysis of Semantic classes: Toward Non-Factoid question answering", Phd Thesis submitted to Department of Computer science, University of Toronto, 2007.