Automatic Meter Classification of Kurdish Poems

TL;DR

This paper introduces a rule-based system for automatically classifying Kurdish poems' meters, achieving high accuracy despite linguistic uncertainties, to aid in literary analysis and understanding.

Contribution

It presents the first automatic meter classification method for Kurdish poetry, handling linguistic ambiguities and utilizing pattern matching for accurate identification.

Findings

97.3% precision in meter type classification

96.2% precision in pattern identification

Effective handling of vowel length uncertainties

Abstract

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.