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Arabic Text Diacritization Methodology by temp2172 is a document available to read on EtoBox.

This paper presents a multi-layered approach for Arabic text diacritization, which improves the performance of various NLP tasks by diacritizing input Arabic sequences both morphologically and syntactically. The system operates in three layers: the first uses HMM for known words, the second employs a morphological analyzer for out-of-vocabulary words, and the third applies CRF for syntactic diacritization. The proposed method achieved a morphological word error rate of 4.3% and a syntactic word error rate o

Author
temp2172
Language
EN