Spell corrector for Bangla language using Norvig’s algorithm and Jaro-Winkler distance

Ahamed, Istiak; Jahan, Maliha; Tasnim, Zarin; Karim, Tajbia; Reza, Shamim; Hossain, Dilshad Ara

doi:10.11591/eei.v10i4.2410

Cited by 12 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors did not mention any specific description for the spell checker. Recently in 2021, Ahamed et al [21] proposed a spell corrector for the Bangla language using Norvig's algorithm and Jaro-Winkler distance which can handle specifically typographic errors. In the same year, Noshin Jahan et al [22] proposed a hybrid model for Bangla word error detection correct that is based on bidirectional LSTM and bigram.…”

Section: B Spell Checkermentioning

confidence: 99%

“…• Limited error detection and suggestions by spell checker: The previous spell checkers including the recent ones ( [20] and [21]) can detect only typographic errors. On the contrary, [22] can detect only real-word errors.…”

Section: Limitations Of Previous Approachesmentioning

confidence: 99%

“…However, they tested their spell checker with datasets containing 1,607 and 2,450 words, respectively. Most of these approaches, including recent work [20] and [21] handle specifically typographic errors, [22] handles particularly real-word errors where the proposed approach handles all different types of errors that exist in Bangla language. Another significant difference between our proposed spell checker and [13] to [22] (excepts [20]) is that they tested their spell checker on test sets consisting of only single words instead of complete sentences, which might not reflect the reallife situations and complexities.…”

Section: Comparison With the Existing Solutionsmentioning

confidence: 99%

See 2 more Smart Citations

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

2021

View full text Add to dashboard Cite

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.

show abstract

Section: B Spell Checkermentioning

confidence: 99%

Section: Limitations Of Previous Approachesmentioning

confidence: 99%

Section: Comparison With the Existing Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

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

2021

View full text Add to dashboard Cite

show abstract

“…The use of n-grams in this research is due to previous research obtaining reasonably good accuracy and having the ability to detect more than one word in a sentence. In addition, the Jaro Winkler can also run faster processes than the Levenshtein distance algorithm [21,26]. Therefore, this research aims to hybrid Jaro Winker and N-Gram methods to detect and correct writing errors automatically.…”

Section: Introductionmentioning

confidence: 99%

Essay auto-scoring using N-Gram and Jaro Winkler based Indonesian Typos

Jayadianti,

Santosa,

Cahyaning

et al. 2023

matrik

View full text Add to dashboard Cite

Writing errors on e-essay exams reduce scores. Thus, detecting and correcting errors automatically in writing answers is necessary. The implementation of Levenshtein Distance and N-Gram can detect writing errors. However, this process needed a long time because of the distance method used. Therefore, this research aims to hybrid Jaro Winker and N-Gram methods to detect and correct writing errors automatically. This process required preprocessing and finding the best word recommendations by the Jaro Winkler method, which refers to Kamus Besar Bahasa Indonesia (KBBI). The N-Gram method refers to the corpus. The final scoring used the Vector Space Model (VSM) method based on the similarity of words between the answer keys and the respondent’s answers. Datasets used 115 answers from 23 respondents with some writing errors. The results of Jaro Winkler and N-Gram methods are good in detecting and correcting Indonesian words with the accuracy of detection averages of 83.64% (minimum of 57.14% and maximum of 100.00%). In contrast, the error correction accuracy averages 78.44% (minimum of 40.00% and maximum of 100.00%). However, Natural Language Processing (NLP) needs to improve these results for word recommendations.

show abstract

“…Since people often spell the same word in different ways, we have also introduced a word suggestion module. We have obtained the list of suggestions using double metaphone encoding [1]- [3] since the encoding can produce multiple encodings for the same word with different pronunciations, Jaro-Winkler similarity algorithm [4], [5] since the algorithm gives higher accuracy in multi-byte Bangla characters than other similar algorithms and (K-nearest neighbors) K-NN since it gives higher accuracy in suggesting closest Bangla words [6]. The double metaphone encoding method has been used to encode the input into corresponding English letters, the Jaro-Winkler similarity algorithm compares the encodings to determine similarities, and K-NN generates suggestions based on the nearest matches.…”

Section: Introductionmentioning

confidence: 99%

Inclusive bidirectional conversion system between Chittagonian and standard Bangla

Hossain

Milon²,

Sabbir

et al. 2022

Bulletin EEI

View full text Add to dashboard Cite

In recent years, the Bangla language has come out as a very prominent figure in the world of natural language processing. Many researchers have produced exemplary works on the language, but there has not been any notable work on its highly enriched dialects due to the lack of resources and the diversity and complexity in its grammatical structure. This paper has suggested a bidirectional conversion system for one of the most widely used dialects of Bangla; the Chittagonian dialect. Our method employs a bidirectional lexicon that uses binary search, word-to-word mapping, and morphological transformations. The system has achieved an accuracy rate of 95.86% for Chittagonian to standard Bangla and 93.89% in the case of standard Bangla to Chittagonian. We have also provided an efficient word suggestion module, and it has yielded satisfactory results.

show abstract

Spell corrector for Bangla language using Norvig’s algorithm and Jaro-Winkler distance

Cited by 12 publications

References 10 publications

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

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

Essay auto-scoring using N-Gram and Jaro Winkler based Indonesian Typos

Inclusive bidirectional conversion system between Chittagonian and standard Bangla

Contact Info

Product

Resources

About