  The processing of the Arabic language is a complex field of research. This is
due to many factors, including the complex and rich morphology of Arabic, its
high degree of ambiguity, and the presence of several regional varieties that
need to be processed while taking into account their unique characteristics.
When its dialects are taken into account, this language pushes the limits of
NLP to find solutions to problems posed by its inherent nature. It is a
diglossic language; the standard language is used in formal settings and in
education and is quite different from the vernacular languages spoken in the
different regions and influenced by older languages that were historically
spoken in those regions. This should encourage NLP specialists to create
dialect-specific corpora such as the Palestinian morphologically annotated
Curras corpus of Birzeit University. In this work, we present the Lebanese
Corpus Baladi that consists of around 9.6K morphologically annotated tokens.
Since Lebanese and Palestinian dialects are part of the same Levantine
dialectal continuum, and thus highly mutually intelligible, our proposed corpus
was constructed to be used to (1) enrich Curras and transform it into a more
general Levantine corpus and (2) improve Curras by solving detected errors.
