Automatic Correction of Arabic Dyslexic Text

This paper proposes an automatic correction system that detects and corrects dyslexic errors in Arabic text. The system uses a language model based on the Prediction by Partial Matching (PPM) text compression scheme that generates possible alternatives for each misspelled word. Furthermore, the generated candidate list is based on edit operations (insertion, deletion, substitution and transposition), and the correct alternative for each misspelled word is chosen on the basis of the compression codelength of the trigram. The system is compared with widely-used Arabic word processing software and the Farasa tool. The system provided good results compared with the other tools, with a recall of 43%, precision 89%, F1 58% and accuracy 81%

An improved Levenshtein algorithm for spelling correction word candidate list generation

Candidates’ list generation in spelling correction is a process of finding words from a lexicon that should be close to the incorrect word. The most widely used algorithm for generating candidates’ list for incorrect words is based on Levenshtein distance. However, this algorithm takes too much time when there is a large number of spelling errors. The reason is that calculating Levenshtein algorithm includes operations that create an array and fill the cells of this array by comparing the characters of an incorrect word with the characters of a word from a lexicon. Since most lexicons contain millions of words, then these operations will be repeated millions of times for each incorrect word to generate its candidates list. This dissertation improved Levenshtein algorithm by designing an operational technique that has been included in this algorithm. The proposed operational technique enhances Levenshtein algorithm in terms of the processing time of its executing without affecting its accuracy. It reduces the operations required to measure cells’ values in the first row, first column, second row, second column, third row, and third column in Levenshtein array. The improved Levenshtein algorithm was evaluated against the original algorithm. Experimental results show that the proposed algorithm outperforms Levenshtein algorithm in terms of the processing time by 36.45% while the accuracy of both algorithms is still the same

Detection of semantic errors in Arabic texts

AbstractDetecting semantic errors in a text is still a challenging area of investigation. A lot of research has been done on lexical and syntactic errors while fewer studies have tackled semantic errors, as they are more difficult to treat. Compared to other languages, Arabic appears to be a special challenge for this problem. Because words are graphically very similar to each other, the risk of getting semantic errors in Arabic texts is bigger. Moreover, there are special cases and unique complexities for this language. This paper deals with the detection of semantic errors in Arabic texts but the approach we have adopted can also be applied for texts in other languages. It combines four contextual methods (using statistics and linguistic information) in order to decide about the semantic validity of a word in a sentence. We chose to implement our approach on a distributed architecture, namely, a Multi Agent System (MAS). The implemented system achieved a precision rate of about 90% and a recall rate of about 83%

An enhanced automatic speech recognition system for Arabic

International audienceAutomatic speech recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition , it is essential to take into consideration the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) speech recognition. We introduce the challenges related to Arabic language, namely the complex morphology nature of the language and the absence of the short vowels in written text, which leads to several potential vowelization for each graphemes, which is often conflicting. We develop an ASR system for MSA by using Kaldi toolkit. Several acoustic and language models are trained. We obtain a Word Error Rate (WER) of 14.42 for the baseline system and 12.2 relative improvement by rescoring the lattice and by rewriting the output with the right hamoza above or below Alif

From Arabic user-generated content to machine translation: integrating automatic error correction

With the wide spread of the social media and online forums, individual users have been able to actively participate in the generation of online content in different languages and dialects. Arabic is one of the fastest growing languages used on Internet, but dialects (like Egyptian and Saudi Arabian) have a big share of the Arabic online content. There are many differences between Dialectal Arabic and Modern Standard Arabic which cause many challenges for Machine Translation of informal Arabic language. In this paper, we investigate the use of Automatic Error Correction method to improve the quality of Arabic User-Generated texts and its automatic translation. Our experiments show that the new system with automatic correction module outperforms the baseline system by nearly 22.59% of relative improvement

Survey of Arabic Checker Techniques

It is known that the importance of spell checking, which increases with the expanding of technologies, using the Internet and the local dialects, in addition to non-awareness of linguistic language. So, this importance increases with the Arabic language, which has many complexities and specificities that differ from other languages. This paper explains these specificities and presents the existing works based on techniques categories that are used, as well as explores these techniques. Besides, it gives directions for future work

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

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