Efficient Automatic Correction of Misspelled Arabic Words Based on Contextual Information

Zribi, Chiraz Ben Othmane; Ahmed, Md. Kawser

doi:10.1007/978-3-540-45224-9_104

Cited by 15 publications

(4 citation statements)

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“…For example, for the first erroneous word and corresponding to the database, we led that the erroneous insertion of the character " " is due to the ❒ ISSN: 2088-8708 proximity on the left and on the right between the characters " " and " ". According to these statistics and the different interpretations deduced, we can confirm that the majority of the editing errors made (insertion, deletion, permutation) are caused by the proximity and similarity of the character keys on the Arabic keyboard [17], [18]. In the rest of this paper, we will modelize these interpretations as matrices probabilistic weights.…”

Section: − Analysis Of Deletion and Insertion Errormentioning

confidence: 89%

Arabic spellchecking: a depth-filtered composition metric to achieve fully automatic correction

Gueddah,

Lachibi

2023

IJECE

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<p>Digital environments for human learning have evolved a lot in recent years thanks to incredible advances in information technologies. Computer assistance for text creation and editing tools represent a future market in which natural language processing (NLP) concepts will be used. This is particularly the case of the automatic correction of spelling mistakes used daily by data operators. Unfortunately, these spellcheckers are considered writing aids tools, they are unable to perform this task automatically without user’s assistance. In this paper, we suggest a filtered composition metric based on the weighting of two lexical similarity distances in order to reach the auto-correction. The approach developed in this article requires the use of two phases: the first phase of correction involves combining two well-known distances: the edit distance weighted by relative weights of the proximity of the Arabic keyboard and the calligraphical similarity between Arabic alphabet, and combine this measure with the JaroWinkler distance to better weight, filter solutions having the same metric. The second phase is considered as a booster of the first phase, this use the probabilistic bigram language model after the recognition of the solutions of error, which may have the same lexical similarity measure in the first correction phase. The evaluation of the experimental results obtained from the test performed by our filtered composition measure on a dataset of errors allowed us to achieve a 96% of auto-correction rate.</p>

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Section: − Analysis Of Deletion and Insertion Errormentioning

confidence: 89%

Arabic spellchecking: a depth-filtered composition metric to achieve fully automatic correction

Gueddah,

Lachibi

2023

IJECE

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“…For Arabic which is a Semitic language, its words are lexically very close to each other with an editing error. Indeed, the average number of neighbouring forms is 26.5% for Arabic, 3.5% for French and 3% for English (Zribi and Ahmed, 2003).…”

Section: The Merge Operationmentioning

confidence: 99%

The Automatic Generation of Arabic Sentences Based on a Minimalist Approach

Moukrim¹,

Tragha²,

Benlahmer³

2020

Int. J. Soft Comput.

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The automatic generation of sentences is a domain of Natural Language Processing (NLP); it is placed in the middle of computer science and linguistics. This is a very complex discipline; the aim of this is to create automatically correct sentences from a list of words which can serve as the basis for such various applications such as automatic translation, question-answering systems, correcting syntactic errors and so on. In this study, we present the use of the linguistic approach of Chomsky's minimalist grammar. We begin with the elaboration of the lexicon that constitutes the essential link of the generation. Then, based on this lexicon, we treat the merge and move operations to build a syntactically correct sentence.

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“…Arabic is challenging for language modeling due to the high graphemic similarity of Arabic words. This is shown by Zribi and Ben Ahmed (2003) who conducted an experiment automatically using four edit operations (addition, substitution, deletion, and transposition) to change words, calculating the number of correct forms among the number of automatically built forms (or lexically neighboring words) resulting from these edit operations. They found that the average number of neighboring forms for Arabic is 26.5, which is significantly higher than that for French, 3.5 and English, 3.0.…”

Section: Error Detectionmentioning

confidence: 99%

Arabic spelling error detection and correction

et al. 2015

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A spelling error detection and correction application is typically based on three main components: a dictionary (or reference word list), an error model and a language model. While most of the attention in the literature has been directed to the language model, we show how improvements in any of the three components can lead to significant cumulative improvements in the overall performance of the system. We develop our dictionary of 9.2 million fully-inflected Arabic words (types) from a morphological transducer and a large corpus, validated and manually revised. We improve the error model by analyzing error types and creating an edit distance re-ranker. We also improve the language model by analyzing the level of noise in different data sources and selecting an optimal subset to train the system on. Testing and evaluation experiments show that our system significantly outperforms Microsoft Word 2013, OpenOffice Ayaspell 3.4 and Google Docs.

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Efficient Automatic Correction of Misspelled Arabic Words Based on Contextual Information

Cited by 15 publications

References 4 publications

Arabic spellchecking: a depth-filtered composition metric to achieve fully automatic correction

Arabic spellchecking: a depth-filtered composition metric to achieve fully automatic correction

The Automatic Generation of Arabic Sentences Based on a Minimalist Approach

Arabic spelling error detection and correction

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