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Checking the Correctness of Bangla Words using N-Gram

by Nur Hossain Khan, Gonesh Chandra Saha, Bappa Sarker, Md. Habibur Rahman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 11
Year of Publication: 2014
Authors: Nur Hossain Khan, Gonesh Chandra Saha, Bappa Sarker, Md. Habibur Rahman
10.5120/15672-4416

Nur Hossain Khan, Gonesh Chandra Saha, Bappa Sarker, Md. Habibur Rahman . Checking the Correctness of Bangla Words using N-Gram. International Journal of Computer Applications. 89, 11 ( March 2014), 1-3. DOI=10.5120/15672-4416

@article{ 10.5120/15672-4416,
author = { Nur Hossain Khan, Gonesh Chandra Saha, Bappa Sarker, Md. Habibur Rahman },
title = { Checking the Correctness of Bangla Words using N-Gram },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 11 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number11/15672-4416/ },
doi = { 10.5120/15672-4416 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:55.915684+05:30
%A Nur Hossain Khan
%A Gonesh Chandra Saha
%A Bappa Sarker
%A Md. Habibur Rahman
%T Checking the Correctness of Bangla Words using N-Gram
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 11
%P 1-3
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

N-gram model is used in many domains like spelling and syntactic verification, speech recognition, machine translation, character recognition and like others. This paper describes a system for checking the correctness of a bangle word using N-gram model. An experimental corpus containing one million word tokens was used to train the system. The corpus was a part of the BdNC01 corpus, created in the SIPL lab. of Islamic university. Collecting several sample text from different newspapers, the system was tested by 50,000 correct and another 50,000 incorrect words. The system has successfully detected the correctness of the test words at a rate of 96. 17%. This paper also describes the limitations of the system with possible solutions.

References
  1. P Majumder, M Mitra, B. B. Chaudhuri, "N-gram: a language independent approach to IR and NLP", ICUKL November 2002, Goa, India.
  2. Wikipedia, "n-gram", http://en. wikipedia. org/wiki/N-gram, Access date: 17th Dec. 2013.
  3. Daniel Jurafsky, James H. Martin,"Speech and Language Processing An Introduction to Natural Language Processing: Computational Linguistics and Speech Recognition", Prentice Hall, Englewood Cliffs, New Jersey 07632 , September 28, 1999
  4. C. E. Shannon, "Prediction and entropy of printed English," Bell Sys. Tec. J. (30):50–64, 1951
  5. Farag Ahmed, Ernesto William De Luca, and Andreas Nürnberger, "Revised N-Gram based Automatic Spelling Correction Tool to Improve Retrieval Effectiveness", August 22, 2009
  6. Hasan Muaidi, Rasha Al-Tarawneh, "Towards Arabic Spell-Checker Based on N-Grams Scores", International Journal of Computer Applications (0975 -8887), Volume 53 - No. 3, September 2012.
Index Terms

Computer Science
Information Sciences

Keywords

N-gram Tokens Corpus Witten-Bell smoothing