Abstract:
In this paper, the problem of missing diacritic marks in most of dialectal Arabic written resources is addressed. Our aim is to implement a scalable and extensible platfo...Show MoreMetadata
Abstract:
In this paper, the problem of missing diacritic marks in most of dialectal Arabic written resources is addressed. Our aim is to implement a scalable and extensible platform for automatically retrieving the diacritic marks for undiacritized dialectal Arabic texts. Different rule-based and statistical techniques are proposed. These include: morphological analyzer-based, maximum likelihood estimate, and statistical n-gram models. The proposed platform includes helper tools for text preprocessing and encoding conversion. Diacritization accuracy of each technique is evaluated in terms of Diacritic Error Rate (DER) and Word Error Rate (WER). The approach trains several n-gram models on different lexical units. A data pool of both Modern Standard Arabic (MSA) data along with Dialectal Arabic data was used to train the models.
Published in: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)
Date of Conference: 29 November 2016 - 02 December 2016
Date Added to IEEE Xplore: 12 June 2017
ISBN Information:
Electronic ISSN: 2161-5330