A Survey on Various Candidate Generator Methods for Efficient String Transformation


  • Malarvizhi P PG Scholar, Dept. of CSE, M.I.E.T Engineering College
  • Mohana S Associate Professor, Dept. of CSE, M.I.E.T Engineering College


candidate generation, string transformation, spelling error correction


String Transformation can be formalized such as given an input string; the system generates the k most likely output strings corresponding to the input string. The essential and important step for string transformation is to generate candidates to which the given string s is likely to be transformed. The different approaches and various candidate generator methods for efficient string transformation are discussed in this paper.


Ziqi Wang, Gu Xu, Hang Li, and Ming Zhang, "A Probabilistic Approach to String Transformation", IEEE transactions on knowledge and data engineering, VOL:PP NO:99, 2013.

Behm .A, S. Ji, C. Li, and J. Lu, ― Space-constrained grambased indexing for efficient approximate string search,‖ ICDE ’09. IEEE Computer Society, pp. 604–615, 2009.

Brill E. and R. C. Moore, ―An improved error model for noisy channel spelling correction,‖ ACL ’00. Morristown, NJ, USA: Association for Computational Linguistics, pp. 286–293, 2000.

Chen Q, M. Li, and M. Zhou, ―Improving query spelling correction using web search results,‖ EMNLP ’07, pp. 181–189, 2007.

Dreyer .M, J. R. Smith, and J. Eisner, ―Latent-variable modeling of string transductions with finite-state methods,‖EMNLP ’08, Association for Computational Linguistics, pp.1080–1089, 2008.

Duan.H and B.-J. P. Hsu, ―Online spelling correction for query completion,‖ WWW’11. ACM, pp. 117–126, 2011.

Guo .J, G. Xu, H. Li, and X. Cheng, ―A unified and discriminative model for query refinement,‖ SIGIR ’08. ACM, pp. 379–386, 2008.

Hadjieleftheriou.M and C. Li, ―Efficient approximate search on string collections,‖ Proc. VLDB Endow., vol. 2, pp.1660–1661, August 2009.

McCallum A., K. Bellare, and F. Pereira, ―A conditional random field for discriminatively-trained finite-state string edit distance,‖ in Proceedings of the 21st Conference on Uncertainty in Artifical Intelligence, ser. UAI ’05, pp. 388–395, 2005.

Okazaki .N, Y. Tsuruoka, S. Ananiadou, and J. Tsujii, ―A discriminative candidate generator for string transformations‖ EMNLP ’08, Association for Computational Linguistics, pp. 447–456, 2008.

Tejada .S, C. A. Knoblock, and S. Minton, ―Learning domain independent string transformation weights for high accuracy object identification,‖ KDD ’02. ACM, pp. 350–359, 2008.

Yang Y., J. Yu, and M. Kitsuregawa, ―Fast algorithms for top-k approximate string matching,‖ in Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, ser. AAAI ’10, pp.1467–1473.




How to Cite

Malarvizhi, P., & Mohana, S. (2024). A Survey on Various Candidate Generator Methods for Efficient String Transformation. COMPUSOFT: An International Journal of Advanced Computer Technology, 3(02), 524–528. Retrieved from https://ijact.in/index.php/j/article/view/95



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