Unstructured Text

Unstructured text, encompassing free-flowing text and diverse data formats like tables and images, presents significant challenges for information extraction and analysis. Current research focuses on leveraging large language models (LLMs) and deep learning architectures, such as convolutional and generative adversarial networks, to improve tasks like question answering, summarization, and relation extraction from unstructured data. These advancements are driving progress in diverse fields, including medical informatics, legal technology, and scientific knowledge discovery, by enabling efficient processing and analysis of large, complex textual datasets. The development of robust and reliable methods for handling unstructured text is crucial for unlocking the vast potential of information embedded within these data sources.