Natural Language Annotation

Natural language annotation focuses on enriching textual data with structured information, enabling computers to better understand and process human language. Current research emphasizes improving the accuracy and efficiency of annotation, particularly for under-resourced languages and complex linguistic phenomena like formality and implicit discourse relations, often leveraging techniques like crowdsourcing and large language models for data generation and refinement. This work is crucial for advancing numerous applications, including machine translation, speech emotion recognition, and intelligent fault diagnosis in industrial settings, by providing high-quality training data for machine learning models.