Kfir Bar, N. Dershowitz, Lior Wolf, Yackov Lubarsky, Y. Choueka
Judeo-Arabic is a set of dialects spoken and written by Jewish communities living in Arab countries. Judeo-Arabic is typically written in Hebrew letters, enriched with diacritic marks that relate to the underlying Arabic. However, some inconsistencies in rendering words in Hebrew letters increase the level of ambiguity of a given word. Furthermore, Judeo-Arabic texts usually contain non-Arabic words and phrases, such as quotations or borrowed words from Hebrew and Aramaic. We focus on two main tasks: (1) automatic transliteration of Judeo-Arabic Hebrew letters into Arabic letters, and (2) automatic identification of language switching points between Judeo-Arabic and Hebrew. For transliteration, we employ a statistical translation system trained on the character level, resulting in 96.9% precision, a significant improvement over the baseline. For the language switching task, we use a word-level supervised classifier, also showing some significant improvements over the baseline.
{"title":"Processing Judeo-Arabic Texts","authors":"Kfir Bar, N. Dershowitz, Lior Wolf, Yackov Lubarsky, Y. Choueka","doi":"10.1109/ACLING.2015.27","DOIUrl":"https://doi.org/10.1109/ACLING.2015.27","url":null,"abstract":"Judeo-Arabic is a set of dialects spoken and written by Jewish communities living in Arab countries. Judeo-Arabic is typically written in Hebrew letters, enriched with diacritic marks that relate to the underlying Arabic. However, some inconsistencies in rendering words in Hebrew letters increase the level of ambiguity of a given word. Furthermore, Judeo-Arabic texts usually contain non-Arabic words and phrases, such as quotations or borrowed words from Hebrew and Aramaic. We focus on two main tasks: (1) automatic transliteration of Judeo-Arabic Hebrew letters into Arabic letters, and (2) automatic identification of language switching points between Judeo-Arabic and Hebrew. For transliteration, we employ a statistical translation system trained on the character level, resulting in 96.9% precision, a significant improvement over the baseline. For the language switching task, we use a word-level supervised classifier, also showing some significant improvements over the baseline.","PeriodicalId":404268,"journal":{"name":"2015 First International Conference on Arabic Computational Linguistics (ACLing)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124771780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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