Smoothing Techniques for Arabic Diacritics Restoration

An algorithm to restore Arabic diacritics using dynamic programming approach was presented in [1]. The possible word sequences with diacritics are assigned scores using statistical n-gram language modeling approach. Using the assigned scores, it is possible to search the most likely sequence using a dynamic programming algorithm. The maximum likelihood (ML) estimation of the stochastic language model parameters leads to poor diacritization accuracy due to data sparse problem. Smoothing aims to handle this problem by taking some probability mass from the observed n-gram and distribute it to the unseen n-grams. In this paper, we show that applying smoothing techniques dramatically improve the diacritization accuracy. The interpolated version of the absolute discounting method leads to the best results for dierent

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