Abstract
Literary compositions are very often analyzed using various constituent units like words, phrases, sentences, and paragraphs. Unlike the conventional research that focuses on the aforementioned constituent units, our task is a statistical effort carried out on the most fundamental unit of any literary composition calledvarna, or character, followed by automated classification using learning algorithms. This article is a case study on the Hindi adaptations of two significant literary pieces, namely,Jana-Gaṇa-ManaandVande-Mātaram, and acknowledging that the two songs being studied belong to different classes based on their bhava, i.e., the inherent emotion of the poem. The present task is the first of its kind that uses the concept ofkomalaandkaṭhora varnato establish diversity between the two. The two-proportion Z-test is successfully applied to statistical data pertaining to the candidate songs, thereby reestablishing the theoretical assertions by investigating real pieces of literature. Taking the statistical verification as ground, a learning-based classification system is designed to yield the best accuracy of 85%, which further compliments the theory reestablished statistically.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ACM Transactions on Asian and Low-Resource Language Information Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
