Nouf Alabbasi, Mohamed Al-Badrashiny, Maryam Aldahmani, Ahmed AlDhanhani, Abdullah Saleh Alhashmi, Fawaghy Ahmed Alhashmi, Khalid Al Hashemi, Rama Emad Alkhobbi, Shamma T Al Maazmi, Mohammed Ali Alyafeai, Mariam M Alzaabi, Mohamed Saqer Alzaabi, Fatma Khalid Badri, Kareem Darwish, Ehab Mansour Diab, Muhammad Morsy Elmallah, Amira Ayman Elnashar, Ashraf Elneima, MHD Tameem Kabbani, Nour Rabih, Ahmad Saad, Ammar Mamoun Sousou
{"title":"Gulf Arabic Diacritization: Guidelines, Initial Dataset, and Results","authors":"Nouf Alabbasi, Mohamed Al-Badrashiny, Maryam Aldahmani, Ahmed AlDhanhani, Abdullah Saleh Alhashmi, Fawaghy Ahmed Alhashmi, Khalid Al Hashemi, Rama Emad Alkhobbi, Shamma T Al Maazmi, Mohammed Ali Alyafeai, Mariam M Alzaabi, Mohamed Saqer Alzaabi, Fatma Khalid Badri, Kareem Darwish, Ehab Mansour Diab, Muhammad Morsy Elmallah, Amira Ayman Elnashar, Ashraf Elneima, MHD Tameem Kabbani, Nour Rabih, Ahmad Saad, Ammar Mamoun Sousou","doi":"10.18653/v1/2022.wanlp-1.33","DOIUrl":null,"url":null,"abstract":"Arabic diacritic recovery is important for a variety of downstream tasks such as text-to-speech. In this paper, we introduce a new Gulf Arabic diacritization dataset composed of 19,850 words based on a subset of the Gumar corpus. We provide comprehensive set of guidelines for diacritization to enable the diacritization of more data. We also report on diacritization results based on the new corpus using a Hidden Markov Model and character-based sequence to sequence models.","PeriodicalId":355149,"journal":{"name":"Workshop on Arabic Natural Language Processing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Arabic Natural Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2022.wanlp-1.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Arabic diacritic recovery is important for a variety of downstream tasks such as text-to-speech. In this paper, we introduce a new Gulf Arabic diacritization dataset composed of 19,850 words based on a subset of the Gumar corpus. We provide comprehensive set of guidelines for diacritization to enable the diacritization of more data. We also report on diacritization results based on the new corpus using a Hidden Markov Model and character-based sequence to sequence models.
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