Automatic extraction of similar poetry for study of literary texts: An experiment on Hindi poetry

Abstract

The study of literary texts is one of the earliest disciplines practiced around the globe. Poetry is artistic writing in which words are carefully chosen and arranged for their meaning, sound, and rhythm. Poetry usually has a broad and profound sense that makes it difficult to be interpreted even by humans. The essence of poetry is Rasa, which signifies mood or emotion. In this paper, we propose a poetry classification‐based approach to automatically extract similar poems from a repository. Specifically, we perform a novel Rasa‐based classification of Hindi poetry. For the task, we primarily used lexical features in a bag‐of‐words model trained using the support vector machine classifier. In the model, we employed Hindi WordNet, Latent Semantic Indexing, and Word2Vec‐based neural word embedding. To extract the rich feature vectors, we prepared a repository containing 37 717 poems collected from various sources. We evaluated the performance of the system on a manually constructed dataset containing 945 Hindi poems. Experimental results demonstrated that the proposed model attained satisfactory performance.

Framework for evaluating code generation ability of large language models

“…Recently, large language models (LLMs) have emerged as powerful tools for natural language processing [1–3], revolutionizing various applications such as text generation, translation, and question answering. Although initially intended for natural language understanding and generation, LLMs have also displayed remarkable proficiency in comprehending and generating programming code.…”