## In-depth Analysis of the Bangla Language: Diversity and NLP Applications A new study has deeply analyzed the lexical diversity and structural complexity of Bangla language texts, both literary and journalistic. The research is based on two large corpora: Vacaspati (literature) and IndicCorp (newspapers). The analysis examined several linguistic properties, including the type-token ratio (TTR), the hapax legomena ratio (HLR), and bigram diversity. The results indicate that the literary corpus, despite its smaller size, exhibits significantly higher lexical richness and structural variation compared to the newspaper corpus. ## Impact on Natural Language Processing Models The study also assessed how the inclusion of literary data influences the performance of NLP models. Integrating literary texts with newspaper texts appears to improve performance in various tasks. Furthermore, it has been demonstrated that the literary corpus adheres more closely to Zipf's law on word distribution compared to the newspaper corpus or a mixed corpus. The research also evaluated the readability of the texts using the Flesch and Coleman-Liau indices, confirming that literary texts are generally more complex.