Measuring Success in Natural Language Processing Evaluation and Metrics
Akshi Kumar · 2024
This chapter focuses on evaluating NLP systems, discussing intrinsic and extrinsic evaluation techniques and key metrics for tasks like text classification, machine translation, and question answering. It provides a comprehensive guide to assessing model performance, ensuring robustness and accuracy in NLP applications. Metrics such as precision, recall, F1 score, and BLEU are discussed in detail.