A Hybrid Algorithm for Matching Arabic Names
This work addresses name matching challenges in Arabic, which is incremental as it builds on existing methods like Levenshtein distance.
The paper tackles the problem of matching Arabic names by proposing a hybrid algorithm that combines token-based and character-based approaches, achieving higher success levels and upper limits compared to classical algorithms on a large Arabic dataset.
In this paper, a new hybrid algorithm which combines both of token-based and character-based approaches is presented. The basic Levenshtein approach has been extended to token-based distance metric. The distance metric is enhanced to set the proper granularity level behavior of the algorithm. It smoothly maps a threshold of misspellings differences at the character level, and the importance of token level errors in terms of token's position and frequency. Using a large Arabic dataset, the experimental results show that the proposed algorithm overcomes successfully many types of errors such as: typographical errors, omission or insertion of middle name components, omission of non-significant popular name components, and different writing styles character variations. When compared the results with other classical algorithms, using the same dataset, the proposed algorithm was found to increase the minimum success level of best tested algorithms, while achieving higher upper limits .