ICTACT Journal on Soft Computing

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A Hybrid Optimization Technique for Effective Document Clustering in Question Answering System


Affiliations
1 Department of Master of Computer Applications, Dr. Mahalingam College of Engineering and Technology, India
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, India
     

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Today, the information is growing enormously and it is difficult and tedious task to retrieve the necessary information from that pool. The main area for retrieving relevant answers is called intelligent information retrieval. To achieve this, question and answering system is used. This question and answering plays a major role in user query processing, information retrieval and extracting related information from the information pool. Recently, number of optimization algorithms is introduced to obtain the accurate and better results. Genetic Algorithm and Cuckoo Search are nature inspired meta-heuristic optimization algorithms. In this paper, combination of Genetic Algorithm with Cuckoo Search is applied to the question and answering system. The proposed algorithm is tested with the Amazon review, Trip Advisor and 20 news group data sets. The results are compared with Genetic Algorithm and Cuckoo Search algorithms.

Keywords

Document Clustering, Cuckoo Search, Genetic Algorithm, Information Retrieval, Question and Answering.
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  • John H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence”, MIT Press, 1975.

  • Xin-She Yang and Suash Deb, “Cuckoo Search via Levy Flights”, Proceedings of World Congress on Nature and Biologically Inspired Computing, pp. 210-214, 2009.

  • Abdessamad Echihabi, Ulf Hermjakob, Eduard Hovy, Daniel Marcu, Eric Melz and Deepak Ravichandran, “How to Select Answer String”, Available at: http://www.isi.edu/naturallanguage/people/hovy/papers/05QAbook-answer-stringselect.pdf.

  • J. Jeon, W. Croft and J. Lee, “Finding Semantically Similar Questions based on Their Answers”, Proceedings of 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 617-618, 2005.

  • P. Pathak, M. Gordon and W. Fan, “Effective Information Retrieval using Genetic Algorithms based Matching Functions Adaption”, Proceedings of 33rd Hawaii International Conference on System Sciences, pp. 1-8, 2000.

  • Xin-She Yang and Suash Deb, “Engineering Optimization by Cuckoo Search”, International Journal of Mathematical Modeling and Numerical Optimization, Vol. 1, No. 4, pp. 117, 2010.

  • Pinar Civicioglu and Erkan Besdok, “A Conceptual Comparison of the Cuckoo-Search, Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony Algorithms”, Artificial Intelligent Reviews, Vol. 39, No. 4, pp. 315-346, 2011.

  • Iztok Fister Jr., Xin-She Yang, Iztok Fister, Janez Brest and Dusan Fister, “A Brief Review of Nature-Inspired Algorithms for Optimization”, Elektrotehni Ski Vestnik, Vol.80, No. 3, pp. 1-7, 2013.

  • M. Bhuvaneswari, S. Hariraman, B. Anantharaj and N. Balaji, “Nature Inspired Algorithms: A Review”, International Journal of Emerging Technology in Computer Science and Electronics, Vol. 12, No. 1, pp. 21-28, 2014.

  • Nitisha Gupta and Sharad Sharma, “Nature-Inspired Techniques for Optimization: A Brief Review”, International Journal of Advance Research in Science and Engineering, Vol. 5, No. 5, pp. 36-44, 2016.

  • Mansaf Alam and Kishwar Sadaf, “Web Search Result Clustering based on Cuckoo Search and Consensus Clustering”, Indian Journal of Science and Technology, Vol. 9, No. 15, pp. 1-18, 2016.

  • J. Sethilnath, V. Das, S.N. Omkar and V.Maniv , “Clustering using Levy flight cuckoo search”, Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications, pp. 65-75, 2012.

  • R.G. Babu Kartik and P. Dhavachelvan, “Hybrid Algorithm by the advantage of ACO and Cuckoo Search for Job Scheduling”, International Journal of Information Technology Convergence and Services, Vol. 2, No. 4, pp. 25-34, 2012.

  • Satyendra Singh, Jitendra Kurmi and Sudanshu Prakash Tiwari, “A Hybrid Genetic and Cuckoo Search Algorithm for Job Scheduling”, International Journal of Scientific and Research Publications, Vol. 5, No. 6, pp. 1-4, 2015.

  • Oleksandr Kolomiyets and Marie-Francine Moens, “A Survey on Question Answering Technology from an information Retrieval Perspective”, Information Sciences, Vol. 181, No. 24, pp. 5412-5434, 2011.

  • Gunnar Schroder, Maik Thiele and Wolfgang Lehne, “Setting Goals and Choosing Metrics for Recommender System Evaluations”, Proceedings of 5th ACM Conference on Dresden University of Technology Recommender Systems, pp. 78-85, 2011

  • Iman Khodadi and Mohammad Saniee Abadeh, “Genetic Programming-based feature Learning for Question Answering”, Information Processing and Management, Vol. 52, No. 2, pp. 340-357, 2016.


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  • A Hybrid Optimization Technique for Effective Document Clustering in Question Answering System

Abstract Views: 504  |  PDF Views: 4

Authors

K. Karpagam
Department of Master of Computer Applications, Dr. Mahalingam College of Engineering and Technology, India
A. Saradha
Department of Computer Science and Engineering, Institute of Road and Transport Technology, India

Abstract


Today, the information is growing enormously and it is difficult and tedious task to retrieve the necessary information from that pool. The main area for retrieving relevant answers is called intelligent information retrieval. To achieve this, question and answering system is used. This question and answering plays a major role in user query processing, information retrieval and extracting related information from the information pool. Recently, number of optimization algorithms is introduced to obtain the accurate and better results. Genetic Algorithm and Cuckoo Search are nature inspired meta-heuristic optimization algorithms. In this paper, combination of Genetic Algorithm with Cuckoo Search is applied to the question and answering system. The proposed algorithm is tested with the Amazon review, Trip Advisor and 20 news group data sets. The results are compared with Genetic Algorithm and Cuckoo Search algorithms.

Keywords


Document Clustering, Cuckoo Search, Genetic Algorithm, Information Retrieval, Question and Answering.

References