  Most of the classic texts in Kurdish literature are poems. Knowing the meter
of the poems is helpful for correct reading, a better understanding of the
meaning, and avoidance of ambiguity. This paper presents a rule-based method
for automatic classification of the poem meter for the Central Kurdish
language. The metrical system of Kurdish poetry is divided into three classes
of quantitative, syllabic, and free verses. As the vowel length is not phonemic
in the language, there are uncertainties in syllable weight and meter
identification. The proposed method generates all the possible situations and
then, by considering all lines of the input poem and the common meter patterns
of Kurdish poetry, identifies the most probable meter type and pattern of the
input poem. Evaluation of the method on a dataset from VejinBooks Kurdish
corpus resulted in 97.3% of precision in meter type and 96.2% of precision in
pattern identification.
