Transforming Standard Arabic to Colloquial Arabic
Domain:
natural language processingRecord type:
paper
Creator:
EmaBehKem
Host:
We present a method for generating Colloquial
Egyptian Arabic (CEA) from morphologically disambiguated
Modern Standard Arabic (MSA).
When used in POS tagging, this process improves
the accuracy from 73.24% to 86.84% on unseen
CEA text, and reduces the percentage of out-of vocabulary
words from 28.98% to 16.66%. The
process holds promise for any NLP task targeting
the dialectal varieties of Arabic; e.g., this approach
may provide a cheap way to leverage MSA data
and morphological resources to create resources
for colloquial Arabic to English machine translation.
It can also considerably speed up the annotation
of Arabic dialects.
Similar
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