Text Document

Text document analysis focuses on extracting meaningful information and insights from textual data, encompassing diverse applications from business process modeling to historical document interpretation. Current research emphasizes improving the efficiency and accuracy of information extraction through human-machine collaboration, leveraging advanced models like transformers (e.g., RoBERTa, BART) and algorithms such as TF-IDF and spectral clustering, often enhanced by active learning techniques to reduce annotation costs. These advancements are crucial for various fields, enabling more efficient data processing, improved understanding of complex textual data, and facilitating the development of more robust and explainable AI systems.