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Diacritization: A Challenge to Arabic Treebank Annotation and Parsing
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In: http://papers.ldc.upenn.edu/NLTSG/DiacritizationArabicTreebank.pdf (2006)
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| 43 |
Variability and Invariance of vocalic system in Modern Standard Arabic ; Variabilité et invariance du système vocalique de l'arabe standard
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In: https://hal.archives-ouvertes.fr/tel-03606009 ; Linguistique. Université de Franche-Comté (Besançon), 1991. Français (1991)
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| 44 |
Sana’ani Dialect to Modern Standard Arabic: Rule-based Direct Machine Translation
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In: http://world-comp.org/p2011/ICA8011.pdf
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Abstract:
Abstract- This paper presents an algorithm to normalize Sana’ani dialect to modern standard Arabic. The algorithm based on morphological rules related to Sana’ani dialect as well as Modern Standard Arabic. Such rules facilitate the dialect conversion to its corresponding MSA. The algorithm tokenizes the input dialect text and analyses each token into stem and its affixes; such affixes can be categorized into two categories: dialect affixes and/or MSA affixes. At the same time, the stem could be dialect stem or MSA stem. Therefore, our algorithm must pay attention to such situations. That is done by using of a simple MSA stemmer. Then our dialect stemmer is applied to strip the resulting token and extract dialect affixes. At this point, the rules take place to decide when to carry out the extraction of an affix. The experiment shows that Sana’ani dialect has three classes of distortions. They are prefixes, suffixes, and stems distortions. The algorithm normalizes such distortion based on the morphological rules. For each morphological rule the algorithm checks possibility of applying such rule. That means if rule conditions meet, then the dialect affix will be replaced by its MSA correspondence. If there is no restriction on applying the rule related to the distorted stem, then the rule can be considered as a parallel corpus of dialect and MSA. Finally, the experiment computes the distortion ratio of MSA in Sana’ani dialect. For Sana’ani dialect sample of 529 words, 6.98 % of them have distorted suffixes, 5.65 % have distorted prefixes and 11.72% contain distorted stems. These percentages are related to processed words only.
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Keyword:
Modern Standard Arabic; MSA; NLP; Rule-based Machine translation; Sana’a Dialect; Yemeni
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URL: http://world-comp.org/p2011/ICA8011.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.6513
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| 45 |
F0 ALIGNMENT PATTERNS IN ARABIC DIALECTS
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In: http://www.icphs2007.de/conference/Papers/1173/1173.pdf
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| 46 |
Non-coinciding Phrasal Heads
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In: http://www.cs.kun.nl/agfl/AP4IR/CCCT03.pdf/
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| 47 |
A NOVEL METHOD FOR ARABIC CONSONANT/VOWEL SEGMENTATION USING WAVELET TRANSFORM
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In: http://www.ijicis.net/Vol5_No1 No24.pdf
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| 48 |
Token Level Identification of Linguistic Code Switching
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In: http://aclweb.org/anthology/C/C12/C12-2029.pdf
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| 49 |
Building A Modern Standard Arabic Corpus
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In: http://hachita.nmsu.edu/ref/Building_MSA_Corpus.pdf
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| 50 |
TABLE OF CONTENTS
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In: http://sdsu-dspace.calstate.edu/bitstream/handle/10211.10/1804/Shah_Vaidehi.pdf?sequence=1
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