Frequency based spell checking and rule based grammar checking

Singh, Shashi Pal; Kumar, Ajai; Singh, Lenali; Bhargava, Mahesh; Goyal, Kritika; Sharma, Bhanu

doi:10.1109/iceeot.2016.7755557

Cited by 11 publications

(2 citation statements)

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“…This method can detect the position of the misspelled words with or without a dictionary. The word-frequency method identifies and corrects errors by using the probability of common mistakes repetition and the distance-editing method 25,26 . Singh et al 26 proposed a spelling checker based on frequency and a grammar checker based on rules.…”

Section: Probabilistic and Rule-based Methodsmentioning

confidence: 99%

“…The word-frequency method identifies and corrects errors by using the probability of common mistakes repetition and the distance-editing method 25,26 . Singh et al 26 proposed a spelling checker based on frequency and a grammar checker based on rules. They used a dictionary and bigram to find spelling errors and chose the best word for spelling correction.…”

Section: Probabilistic and Rule-based Methodsmentioning

confidence: 99%

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Correcting spelling mistakes in Persian texts with rules and deep learning methods

Kasmaiee,

Homayounpour

2023

Sci Rep

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This study aims to develop a system for automatically correcting spelling errors in Persian texts using two approaches: one that relies on rules and a common spelling mistake list and another that uses a deep neural network. The list of 700 common misspellings was compiled, and a database of 55,000 common Persian words was used to identify spelling errors in the rule-based approach. 112 rules were implemented for spelling correction, each providing suggested words for misspelled words. 2500 sentences were used for evaluation, with the word with the shortest Levenshtein distance selected for evaluation. In the deep learning approach, a deep encoder-decoder network that utilized long short-term memory (LSTM) with a word embedding layer was used as the base network, with FastText chosen as the word embedding layer. The base network was enhanced by adding convolutional and capsule layers. A database of 1.2 million sentences was created, with 800,000 for training, 200,000 for testing, and 200,000 for evaluation. The results showed that the network's performance with capsule and convolutional layers was similar to that of the base network. The network performed well in evaluation, achieving accuracy, precision, recall, F-measure, and bilingual evaluation understudy (Bleu) scores of 87%, 70%, 89%, 78%, and 84%, respectively.

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Section: Probabilistic and Rule-based Methodsmentioning

confidence: 99%