TY - JOUR
KW - Arud Meters
KW - Natural Language Processing
KW - Pattern Matching
KW - Poetry
KW - Urdu Ghazal
AU - Asia Zaman
AU - Zia-Ud-Din
AU - Sajid Iqbal
AU - Asma Al Shuhail
AB - Urdu poetry retains a prominent position in the cultural heritage of Urdu language. Rhyme schemes and meters are frequently employed in poetry, which follow specific patterns and structures. Natural Language Processing has the capacity to recognize and analyze these patterns, which is beneficial in the investigation of poetic forms. This research presents the UPMVM (Urdu Poetry Metrics Verification Model), a novel rulebased architecture, designed for detecting meter of any given Urdu ghazal verse. In this work, we propose an algorithm that consists of sixteen steps that identifies the Arud meter in the Urdu verses using a custom developed system. This application will not only assist professional poets but also enable students to examine poetry within the framework of prosody principles. The accurate analysis of the prosody of any poetry relies on the act of uttering words rather than on a written record. UPMVM consists of two phases: 1) The primary objective of the initial phase is to consolidate all available literature of the Arud system into a unified digital platform, then develop individual and combined DFA of each identified meter for pattern recognition; 2) the second phase is about the algorithmic implementation. All these rhythmical patterns are matched with 290 Arud meters and their sub-meters developed during this study. The implementation strategy of phase 2 comprises of five essential sub-phases including tokenization, orthography, syllable identification, weight assignment, and meter detection. For evaluation of the proposed method, three different datasets are used for feature extraction, token identification and performance measurement for identification of rhythmic patterns in Urdu poetry. The UPMVM model reached to promising outcome with an average accuracy of 94%.
IS - In press
M1 - In press
N2 - Urdu poetry retains a prominent position in the cultural heritage of Urdu language. Rhyme schemes and meters are frequently employed in poetry, which follow specific patterns and structures. Natural Language Processing has the capacity to recognize and analyze these patterns, which is beneficial in the investigation of poetic forms. This research presents the UPMVM (Urdu Poetry Metrics Verification Model), a novel rulebased architecture, designed for detecting meter of any given Urdu ghazal verse. In this work, we propose an algorithm that consists of sixteen steps that identifies the Arud meter in the Urdu verses using a custom developed system. This application will not only assist professional poets but also enable students to examine poetry within the framework of prosody principles. The accurate analysis of the prosody of any poetry relies on the act of uttering words rather than on a written record. UPMVM consists of two phases: 1) The primary objective of the initial phase is to consolidate all available literature of the Arud system into a unified digital platform, then develop individual and combined DFA of each identified meter for pattern recognition; 2) the second phase is about the algorithmic implementation. All these rhythmical patterns are matched with 290 Arud meters and their sub-meters developed during this study. The implementation strategy of phase 2 comprises of five essential sub-phases including tokenization, orthography, syllable identification, weight assignment, and meter detection. For evaluation of the proposed method, three different datasets are used for feature extraction, token identification and performance measurement for identification of rhythmic patterns in Urdu poetry. The UPMVM model reached to promising outcome with an average accuracy of 94%.
PY - 9998
SE - 1
SP - 1 
EP -  15
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - UPMVM: A Metrics Verification Model for Urdu Poetry
UR - https://www.ijimai.org/journal/bibcite/reference/3589
VL - In press
SN - 1989-1660
ER -