Spelling Correction Using Recurrent Neural Networks and Character Level N-gram

Kinaci, Ahmet Cumhur

doi:10.1109/idap.2018.8620899

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Combined together with other methods they have many various applications, like: spell checking (e.g. in search engines) [12], [13], word correction [14], [15], text categorization [16] or word based sentiment classification [17]. One advantage of the n-gram method is that it is language independent.…”

Section: Related Workmentioning

confidence: 99%

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Polish Word Recognition Based on n-Gram Methods

Wojcicki,

Zientarski

2024

IEEE Access

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Word recognition of Slavic languages is not an easy task due to the complicated declension of words and a variety of diacritical signs. Polish is a representative of West Slavic languages, which are written in Latin characters. Automatic handwritten word recognition in Slavic languages is not easy, due to the poor recognition rate of letters with diacritical signs and lack of good handwritten text corpora for languages with declension. The main aim of the research is to investigate the possibility of correcting typos made in the final phase of recognizing Polish. The method developed is based on letter recognition by means of convolutional neural networks (CNNs) and text matching algorithms for resulting words. At the first stage, we use a designed convolutional neural network for character recognition. At the second stage, after combining letters into words we apply a post-processing error correction method, which improves the efficiency of recognition of the misspelled words. We checked the efficiency of word matching for a few measures of similarity of words, i.e: edit distance (Damerau-Levenshtein), string matching (Sorensen-Dice) and list of candidates. In addition, we examine how word length and the number of misplaced letters affect the behaviour of the algorithms used. The analysis is carried out for bigram and trigram methods. By combining different methods to assess the similarity of words, better selection of lists of proposed words has been achieved. The article proposes an innovative method for correcting post-processing errors in recognizing Polish words with the efficiency of correct word matching ranging from 76% to 99%, depending on the measure and word length used.

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Section: Related Workmentioning

confidence: 99%

“…The algorithm proposed in [21] is a combination of n-gram characters with a neural network. Ngrams and recurrent neural network (LSTM) are used for spell checking of the Punjabi language [22] and for the spelling correction process in Turkish [14].…”

Section: Related Workmentioning

confidence: 99%

Polish Word Recognition Based on n-Gram Methods

Wojcicki,

Zientarski

2024

IEEE Access

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“…The Long Short-Term Memory model (LSTM) proposed in [14] encodes the input word at the character level, which also uses word and POS tag contexts as features for Indonesian text. Similarly, [15] trained a recurrent neural network with dictionary words. For a given misspelled word, they retrieve a candidate list from that dictionary word, then the list gets expanded using a characterlevel bi-gram model and the trained model.…”

Section: Literature Reviewmentioning

confidence: 99%

AraSpell: A Deep Learning Approach for Arabic Spelling Correction

Salhab

Abu-Khzam

2023

Preprint

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Spelling correction is the task of identifying spelling mistakes, typos, and grammatical mistakes in a given text and correcting them according to their context and grammatical structure. This work introduces "AraSpell," a framework for Arabic spelling correction using different seq2seq model architectures such as Recurrent Neural Network (RNN) and Transformer with artificial data generation for error injection, trained on more than 6.9 Million Arabic sentences. Thorough experimental studies provide empirical evidence of the effectiveness of the proposed approach, which achieved 4.8% and 1.11% word error rate (WER) and character error rate (CER), respectively, in comparison with labeled data of 29.72% WER and 5.03% CER. Our approach achieved 2.9% CER and 10.65% WER in comparison with labeled data of 10.02% CER and 50.94% WER. Both of these results are obtained on a test set of 100K sentences.

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“…Zhang and Zhang [21] stated that the task of similarity joining is to find all pairs of strings for which similarities are above a predetermined threshold, where the similarity of two strings is measured by a specific distance function. Kernighan et al [22] proposed a simplification to restrict the candidate list to words that differ with just one edit operation of the Damerau-Levenshtein edit distance-substitution, insertion, deletion, or replacement of succeeding letters [23].…”

Section: Candidate Generationmentioning

confidence: 99%

Survey of Automatic Spelling Correction

2020

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Automatic spelling correction has been receiving sustained research attention. Although each article contains a brief introduction to the topic, there is a lack of work that would summarize the theoretical framework and provide an overview of the approaches developed so far. Our survey selected papers about spelling correction indexed in Scopus and Web of Science from 1991 to 2019. The first group uses a set of rules designed in advance. The second group uses an additional model of context. The third group of automatic spelling correction systems in the survey can adapt its model to the given problem. The summary tables show the application area, language, string metrics, and context model for each system. The survey describes selected approaches in a common theoretical framework based on Shannon’s noisy channel. A separate section describes evaluation methods and benchmarks.

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Spelling Correction Using Recurrent Neural Networks and Character Level N-gram

Cited by 7 publications

References 14 publications

Polish Word Recognition Based on n-Gram Methods

Polish Word Recognition Based on n-Gram Methods

AraSpell: A Deep Learning Approach for Arabic Spelling Correction

Survey of Automatic Spelling Correction

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