Telugu Text Files

Project Overview

Objective

Our mission was to create a comprehensive and meticulously annotated dataset of Telugu text files. This dataset was aimed to significantly enhance the capabilities of natural language processing (NLP) models, particularly in understanding and processing the Telugu language, which is pivotal for various AI-driven applications.

Scope

This project focused on gathering and annotating a vast collection of Telugu text files. These files spanned a wide range of genres, including literature, technical documents, and everyday communication, providing a diverse linguistic landscape for our NLP models.

Sources

Literary Works: Collected over 50,000 pages from classic and contemporary Telugu literature.
Technical and Academic Resources: Amassed 30,000 pages of technical documents and academic papers.
Public and Online Forums: Integrated 20,000 pages of content from public domains and online platforms, ensuring a blend of formal and colloquial language.

Data Collection Metrics

Total Images Collected: 200,000
Direct Field Collection: 120,000
Transportation Authority Partnerships: 50,000
Archives (Public and Private): 30,000

Annotation Process

Stages

Linguistic Categorization: Each text was meticulously categorized based on language style, genre, and complexity.
Semantic Tagging: Vital for understanding context, texts were tagged for semantics, including idioms, colloquialisms, and technical jargon.
Syntax and Grammar Annotations: Detailed annotations were added for syntax and grammatical structures, crucial for NLP training.

Annotation Metrics

Pages Annotated: 100,000
Semantic Tags Applied: 100,000
Syntax and Grammar Annotations: 100,000

Quality Assurance

Model Evaluation: Regular assessments were conducted to ensure the dataset’s effectiveness in training models.
Privacy and Ethical Compliance: Ensured that all texts were ethically sourced and complied with copyright and privacy laws.
Feedback Integration: Continual feedback from linguists and language model developers was incorporated to refine the dataset.

QA Metrics:

Model Accuracy on Test Data: 98.8%
Recognition Speed: 30 ms per image
False Positive Rate: 0.4%

Conclusion

The Telugu Text Files project has set a new standard in the field of NLP. It’s not just a dataset; it’s a bridge connecting the rich linguistic heritage of Telugu with the future of AI-driven language understanding. Our dataset has enabled AI models to process and understand Telugu with unprecedented accuracy and efficiency, opening new avenues in technological advancements for the Telugu-speaking world.