AEDA: Arabic edit distance algorithm Towards a new approach for Arabic name matching

Ghafour, Hesham H. Abdel; El-Bastawissy, Ali; Heggazy, Abdel Fattah A.

doi:10.1109/icces.2011.6141061

Cited by 7 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They applied edit distance algorithm and rule-based transformation approach. Rules are formulated according to their study on common spelling errors made by Arabic learners to handle the noisy data [31,32]. However, these approaches are partially different from our proposed approach, where, the most of them were concentrated on limited problems and used different mechanisms.…”

Section: Related Workmentioning

confidence: 99%

“…The Arabic edited distance algorithm proposed to measure the similarity between two Arabic strings. This type of algorithm is working based on a Levenshtein algorithm [32], likewise, a system developed for 'Arabic character' recognition using guessing the Fast Fourier Transform descriptors for counting the basic part of characters presented by [33]. The system used ten features to classify the character models.…”

Section: Related Workmentioning

confidence: 99%

“…The most common technique used for misspelling correction is the edit distance, which use the dynamic programming to find out the lowest cost of editing operations (insert, delete, and replace) in order to transform string Str 1 into new string Str 2 by giving a cost value of edit operations [32]. As mentioned in the example given by [41], to correct the word ‫'كثٍٍر'‬ to ' ‫كثٍر‬ kabeer' based on the Arabic dictionaries and unifying the text into standard word.…”

Section: Fig2 Arabic Misspelling Detection and Correction Modelmentioning

confidence: 99%

See 2 more Smart Citations

A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

Al-Hagery¹,

Alreshoodi²,

Almutairi³

et al. 2019

IJITCS

View full text Add to dashboard Cite

Real-World datasets accumulated over a number of years tend to be incomplete, inconsistent and contain noisy data, this, in turn, will cause an inconsistency of data warehouses. Data owners are having hundred-millions to billions of records written in different languages, hence continuously increases the need for comprehensive, efficient techniques to maintain data consistency and increase its quality. It is known that the data cleaning is a very complex and difficult task, especially for the data written in Arabic as a complex language, where various types of unclean data can occur to the contents. For example, missing values, dummy values, redundant, inconsistent values, misspelling, and noisy data. The ultimate goal of this paper is to improve the data quality by cleaning the contents of Arabic datasets from various types of errors, to produce data for better analysis and highly accurate results. This, in turn, leads to discover correct patterns of knowledge and get an accurate Decision-Making. This approach established based on the merging of different algorithms. It ensures that reliable methods are used for data cleansing. This approach cleans the Arabic datasets based on the multi-level cleaning using Arabic Misspelling Detection, Correction Model (AMDCM), and Decision Tree Induction (DTI). This approach can solve the problems of Arabic language misspelling, cryptic values, dummy values, and unification of naming styles. A sample of data before and after cleaning errors presented. . Currently, he is teaching the master degree students and a supervisor of four master thesis. He is a jury member of a number of PhD and master thesis, as an internal and external examiner in his field of his specialist.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Fig2 Arabic Misspelling Detection and Correction Modelmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

Al-Hagery¹,

Alreshoodi²,

Almutairi³

et al. 2019

IJITCS

View full text Add to dashboard Cite

show abstract

“…Ghafour et al [10] suggest to adapt the Levenshtein's distance [11] in the comparison of compare Arabic words. The cost of the operation captures three levels of similarity: phonetic, character form and keyboard wise similarities.…”

Section: Iiirelated Workmentioning

confidence: 99%

Unsupervised Morphological Relatedness

Khorsi¹,

Alsheddi²

2016

ijacsa

View full text Add to dashboard Cite

Abstract-Assessment of the similarities between texts has been studied for decades from different perspectives and for several purposes. One interesting perspective is the morphology. This article reports the results on a study on the assessment of the morphological relatedness between natural language words. The main idea is to adapt a formal string alignment algorithm namely Needleman-Wunsch's to accommodate the statistical characteristics of the words in order to approximate how similar are the linguistic morphologies of the two words. The approach is unsupervised from end to end and the experiments show an nDCG reaching 87% and an r-precision reaching 81%.

show abstract

Unsupervised Affix Identification Approach using Probabilistic Dependence

Alsheddi

Khorsi

2018

2018 1st International Conference on Computer Applications &Amp; Information Security (ICCAIS)

View full text Add to dashboard Cite

AEDA: Arabic edit distance algorithm Towards a new approach for Arabic name matching

Cited by 7 publications

References 12 publications

A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

Unsupervised Morphological Relatedness

Unsupervised Affix Identification Approach using Probabilistic Dependence

Contact Info

Product

Resources

About