Tagging Scheme

Tagging schemes are systems for annotating data, crucial for training machine learning models in various natural language processing (NLP) tasks. Current research focuses on optimizing tagging schemes for efficiency and accuracy, exploring minimalist approaches and contrastive learning methods to improve model performance, particularly in resource-constrained settings. This involves investigating the effectiveness of different model architectures, such as transformers, and leveraging knowledge graphs to enhance the translation of rare words. Improved tagging schemes directly impact the performance of NLP applications, leading to more accurate and efficient systems for tasks like sentiment analysis and machine translation.