Comprehensive Survey: Automatic Query Expansion

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-7
Year of Publication : 2019
Authors : Sayani Ghosal, Dr. Devendra Kumar Tayal
DOI :  10.14445/22312803/IJCTT-V67I7P101

MLA

MLA Style:Sayani Ghosal, Dr. Devendra Kumar Tayal"Comprehensive Survey: Automatic Query Expansion" International Journal of Computer Trends and Technology 67.7 (2019): 1-7.

APA Style Sayani Ghosal, Dr. Devendra Kumar Tayal. Comprehensive Survey: Automatic Query Expansion International Journal of Computer Trends and Technology, 67(7),1-7.

Abstract
Intoday’s world massive amount of data in web is increasing exponentially. Internet users extract relevant information through few key words from loads of unstructured data; due to that reason query expansion comes into play. Automatic Query Expansion is a well-liked method and major research area which can be used to enhancethe performance of information retrieval. This paper aspires to unveil fundamentals of information retrieval, various techniques of Query Expansion, their methods and challenges. Its further explains Automated Query Expansion in detail with its inherent advantages of handling large unstructured data. This paper aims to study various researches conducted in the endeavour including comparative study of different techniques and their advantages.

Reference
[1] Singh, J., Sharan, A., & Siddiqi, S. (2013). A literature survey on automatic query expansion for effective retrieval task. International Journal of Advanced Computer Research, 3(12), 170.
[2] Greengrass, E. (2000). Information retrieval: A survey
[3] Carpineto, C., & Romano, G. (2012). A survey of automatic query expansion in information retrieval. ACM Computing Surveys (CSUR), 44(1), 1..
[4] Chum, O., Philbin, J., Sivic, J., Isard, M., & Zisserman, A. (2007, October). Total recall: Automatic query expansion with a generative feature model for object retrieval. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on (pp. 1-8). IEEE.
[5] Azad, H. K., & Deepak, A. (2019). Query expansion techniques for information retrieval: a survey. Information Processing & Management, 56(5), 1698-1735.
[6] Buey, M. G., Garrido, Á. L., & Ilarri, S. (2014, September). An approach for automatic query expansion based on NLP and semantics. In International Conference on Database and Expert Systems Applications (pp. 349-356). Springer, Cham
[7] Kathuria, N., Mittal, K., & Chhabra, A. (2017). A Comprehensive Survey on Query Expansion Techniques, their Issues and Challenges. International Journal of Computer Applications, 168(12)
[8] Carpineto, C., Romano, G., & De Mori, R. (1999). Informative term selection for automatic query expansion. NIST SPECIAL PUBLICATION SP, 363-370.
[9] Schäuble, P. (2012). Multimedia information retrieval: content-based information retrieval from large text and audio databases (Vol. 397). Springer Science & Business Media.
[10] Sharma, D. K., Pamula, R., & Chauhan, D. S. (2019). A hybrid evolutionary algorithm based automatic query expansion for enhancing document retrieval system. Journal of Ambient Intelligence and Humanized Computing, 1-20.
[11] Sharma, D. K., Pamula, R., & Chauhan, D. S. (2019, February). Soft Computing Techniques Based Automatic Query Expansion Approach for Improving Document Retrieval. In 2019 Amity International Conference on Artificial Intelligence (AICAI) (pp. 972-976). IEEE.
[12] Bouziri, A., Latiri, C., & Gaussier, E. (2017, April). Efficient Association Rules Selecting for Automatic Query Expansion. In International Conference on Computational Linguistics and Intelligent Text Processing (pp. 563-574). Springer, Cham.
[13] Gupta, Y., & Saini, A. (2017). A novel Fuzzy-PSO term weighting automatic query expansion approach using combined semantic filtering. Knowledge-Based Systems, 136, 97-120.
[14] Roy, D., Paul, D., Mitra, M., & Garain, U. (2016). Using word embeddings for automatic query expansion. arXiv preprint arXiv:1606.07608.
[15] Singh, J., & Sharan, A. (2015, February). Co-occurrence and semantic similarity based hybrid approach for improving automatic query expansion in information retrieval. In International Conference on Distributed Computing and Internet Technology (pp. 415-418). Springer, Cham.
[16] Chantzios, T., Zervakis, L., Skiadopoulos, S., & Tryfonopoulos, C. (2019, June). Ping-A customizable, open-source information filtering system for textual data. In Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems (pp. 228-231). ACM.
[17] Rocha, V., Kon, F., Cobe, R., & Wassermann, R. (2016). A hybrid cloud-P2P architecture for multimedia information retrieval on VoD services. Computing, 98(1-2), 73-92.
[18] Chen, Y., Dong, B., Shen, Y., Zhenglong, W. E. I., & Liu, X. (2015). U.S. Patent No. 9,213,771. Washington, DC: U.S. Patent and Trademark Office.
[19] Sharma, V. K., & Mittal, N. (2016). Cross lingual information retrieval (CLIR): review of tools, challenges and translation approaches. In Information systems design and intelligent applications (pp. 699-708). Springer, New Delhi.
[20] Trieu, L. Q., Tran, H. Q., & Tran, M. T. (2017, December). News classification from social media using twitter-based doc2vec model and automatic query expansion. In Proceedings of the Eighth International Symposium on Information and Communication Technology (pp. 460-467). ACM.
[21] Miller, G. A. (1995). WordNet: a lexical database for English. Communications of the ACM, 38(11), 39-41.
[22] Patil, D., & Potey, M. A. (2015). Survey of Content Based Lecture Video Retrieval. International Journal of Computer Trends and Technology (IJCTT), 19(1).
[23] Saritha, A., & NaveenKumar, N. Effective Classification of Text. International Journal of Computer Trends 0, 20, 40-60.
[24] Garg, P., & Bedi, E. C. S. (2013). Automatic question generation system from Punjabi text using hybrid approach. International Journal of Computer Trends and Technology (IJCTT), 21(3), 130-133.
[25] Chahal, M. (2016). Information retrieval using Jaccard similarity coefficient. Int. J. Comput. Trends Technol, 36, 140-143.
[26] Ababneh, J., Almomani, O., Hadi, W., El-Omari, N. K. T., & Al-Ibrahim, A. (2014). Vector space models to classify Arabic text. International Journal of Computer Trends and Technology (IJCTT), 7(4), 219-223.

Keywords
Information Retrieval (IR), Query Expansion, Automatic Query Expansion (AQE)