Text Annotation

Text annotation involves assigning labels or tags to text data, facilitating tasks like sentiment analysis, topic classification, and named entity recognition. Current research heavily utilizes large language models (LLMs), often employing techniques like few-shot prompting and prompt engineering to improve annotation accuracy and efficiency, while also emphasizing human-in-the-loop validation to address biases and inconsistencies. This automated annotation is crucial for scaling up machine learning applications across diverse fields, from social sciences and document processing to medical image analysis and software engineering, enabling more efficient and robust analysis of large textual datasets.