Span Classification

Span classification is a machine learning technique that identifies and categorizes specific segments of text, addressing tasks like named entity recognition and information extraction from unstructured data. Current research emphasizes improving accuracy and efficiency through ensemble methods, leveraging transformer-based architectures like BERT, and exploring alternative approaches such as question-answering formulations and sequence-to-sequence models. This technique finds applications in diverse fields, including clinical diagnosis extraction from medical reports, text sanitization for privacy protection, and enhancing the robustness of dialogue systems, demonstrating its broad utility in natural language processing and beyond.