Annotation Schema

Annotation schemas are structured frameworks for labeling data, crucial for training and evaluating natural language processing (NLP) models across diverse tasks like information extraction, sentiment analysis, and coreference resolution. Current research emphasizes creating more comprehensive and nuanced schemas that capture complex linguistic phenomena, such as nested entities and multi-argument relations, often utilizing transformer-based models like BERT for improved performance. These improved schemas and associated datasets are vital for advancing NLP capabilities in various domains, including biomedical literature analysis, legal text interpretation, and the understanding of political discourse.