Clustering-based Bangla spell checker

Mandal, Prianka; Hossain, Belal

doi:10.1109/icivpr.2017.7890878

Cited by 20 publications

(12 citation statements)

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“…In 2016, Ahmed et al [16] used the Edit Distance and n-gram models to post-process their Optical Character Recognition (OCR) output by omitting the misspelt words. Mandal and Hossain [17] proposed a clustering-based Bangla spell checker in 2017 that can handle only typographic and phonetic errors. They have used 2,450 misspelt words as their dataset.…”

Section: B Spell Checkermentioning

confidence: 99%

Development of Bangla Spell and Grammar Checkers: Resource Creation and Evaluation

2021

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A spell and grammar checker is profoundly essential for diverse publications especially for Bangla language in particular as it is spoken by millions of native speakers around the world. Considering the lack of research efforts, we demonstrate the development of a comprehensive Bangla spell and grammar checker with necessary resources. At first, a full-fledged and generalised Bangla monolingual corpus comprising over 100 million words has been built by scraping reputed, diversified online sources and then an extensive Bangla lexicon consisting of over 1 million unique words has been extracted from that corpus. Based on these corpus and lexicon, we have developed a combined spell and grammar checker application that simultaneously detects distinct spelling and grammatical mistakes and provides appropriate suggestions for both as well. The spell checker uses the Double Metaphone algorithm and Edit distance based on the distributed lexicons and numerical suffix dataset to detect all types of Bangla spelling mistakes with an accuracy rate of 97.21% individually. The grammar checker detects errors based on language model probability i.e. combination of bigram and trigram, and generates suggestions based on the Cosine similarity measure with the accuracy rate of 94.29% individually. The datasets and codes used in this work are freely available at https://git.io/JzJ4w.

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Section: B Spell Checkermentioning

confidence: 99%

Development of Bangla Spell and Grammar Checkers: Resource Creation and Evaluation

2021

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“…Bangla is the seventh (7 th ) most spoken language in the world [1]. Some efforts on automated spell checking in Bangla have been reported in [5], [6], [7] and [8]. Likewise, some efforts on automated spell checking in Chinese [9] and Arabic [10] have been reported.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Automated spell checking in Bangla has been experienced by a small number of papers [5], [6], [7], [8], [11], [12], [13], [14], and [15]. All of them concentrated automated contextfree spell checking and correction, whereas none has performed Context-Dependent spell checking and correction in Bangla.…”

Section: Literature Reviewmentioning

confidence: 99%

“…All of them concentrated automated contextfree spell checking and correction, whereas none has performed Context-Dependent spell checking and correction in Bangla. Although different techniques are deployed in [5], [6], [7], [8], [11], [12], [13], [14], and [15], one thing is common for them. It is the absence of a balanced, big and reputable corpus.…”

Section: Literature Reviewmentioning

confidence: 99%

“…P. Mondal and B.M.M. Hossain [5] have used clustering based on edit distance in order to solve the problem of automated Bangla spell checking. Although they have claimed to chance an accuracy of 99.8%, their findings are not performed done to the size of test data use, i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A Context-Sensitive Approach to Find Optimum Language Model for Automatic Bangla Spelling Correction

Islam¹,

Tarek²,

Sadekur³

et al. 2018

ijacsa

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Automated spelling correction is an important phenomenon in typing that has intense effect on aiding both literate and semi-literate people while using keyboard or other similar devices. Such automated spelling correction technique also helps students significantly in learning process through applying proper words during word processing. A lot of work has been conducted for English language, but for Bangla, it is still not adequate. All work done so far in Bangla is context-free. Bangla is one of the mostly spoken languages (3.05% of world population) and considered seventh language of all languages in the world. In this paper, we propose a context-sensitive approach for automated spelling correction in Bangla. We make combined use of edit distance and stochastic, i.e. N-gram language model. We use six N-gram models in total. A novel approach is deployed in order to find the optimum language model in terms of performance. In addition, for finding out better performance, a large Bangla corpus of different word types is used. We have achieved a satisfactory and promising accuracy of 87.58%.

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