Research Article
A Context Free Spell Correction Method using Supervised Machine Learning Algorithms
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 176 - Issue 27 |
| Published: Jun 2020 |
| Authors: Ahmed Yunus, Md Masum |
10.5120/ijca2020920288
|
Ahmed Yunus, Md Masum . A Context Free Spell Correction Method using Supervised Machine Learning Algorithms. International Journal of Computer Applications. 176, 27 (Jun 2020), 36-41. DOI=10.5120/ijca2020920288
@article{ 10.5120/ijca2020920288,
author = { Ahmed Yunus,Md Masum },
title = { A Context Free Spell Correction Method using Supervised Machine Learning Algorithms },
journal = { International Journal of Computer Applications },
year = { 2020 },
volume = { 176 },
number = { 27 },
pages = { 36-41 },
doi = { 10.5120/ijca2020920288 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2020
%A Ahmed Yunus
%A Md Masum
%T A Context Free Spell Correction Method using Supervised Machine Learning Algorithms%T
%J International Journal of Computer Applications
%V 176
%N 27
%P 36-41
%R 10.5120/ijca2020920288
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Spell correction is a modern day necessity for a system that lets a user extract the proper result while searching different things. Misspelled words are highly likely to occur while typing in queries to these systems and when users misspell query, the users may get inconclusive or false information returned by the system. Spell correction can be context-free or context-sensitive based on the usage. This paper traverses a spell correction method using supervised machine learning algorithms in which the wrong word does not rely on any context. Also this paper includes the comparison between different supervised machine learning algorithms for this case and additionally provides the best case and limitation of this spell correction method.
References
- K. Kukich, “Techniques for automatically correcting words in text,” ACM Computing Surveys, 24(4), 377–439, 1992.
- R. A. Wagner and M. J. Fisher, “The string to string correction problem,” Journal of Assoc. Comp. Mach., 21(1):168-173, 1974
- E. J. Yannakoudakis and D. Fawthrop, “An intelligent spelling error corrector,” Information Processing and Management, 19:1, 101-108,1983.
- Jin-ming Zhan, Xiaolong Mou, Shuqing Li, Ditang Fang, “A Language Model in a Large-Vocabulary Speech Recognition System,” in Proc. Of Int. Conf. ICSLP98, Sydney, Australia, 1998.
- K. Church and W. A. Gale, “Probability scoring for spelling correction,”Statistics and Computing, Vol. 1, No. 1, pp. 93–103, 1991.
- Golding, Andrew R.; Roth, Dan (1999). "Journal Article". Machine Learning. SpringerLink. 34: 107–130. doi:10.1023/A:1007545901558
- Revised N-Gram based Automatic Spelling Correction Tool to Improve Retrieval Effectiveness,December 2009, DOI: 10.17562/PB-40-6
- Personalized Spell Checking using Neural Networks by Tyler Garaas, Mei Xiao, and Marc Pomplun
- Arabic Spelling Correction using Supervised Learning, September 2014, DOI: 10.3115/v1/W14-3615
- https://medium.com/@BhashkarKunal/spelling-correction-using-deep-learning-how-bi-directional-lstm-with-attention-flow-works-in-366fabcc7a2f
- https://englishlive.ef.com/blog/language-lab/many-words-english-language/
- https://www.ef.com/wwen/english-resources/english-vocabulary/top-1000-words/
- https://ahrefs.com/blog/top-amazon-searches/
- https://machinelearningmastery.com/prepare-text-data-machine-learning-scikit-learn/
- https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
Index Terms
Keywords