Span Detection

Span detection, a subfield of natural language processing and related areas, focuses on identifying specific segments of text (spans) that possess particular characteristics, such as named entities, causal relationships, or instances of hate speech. Current research emphasizes improving the accuracy and efficiency of span detection through techniques like two-stage question-answering models, transformer-based architectures with deep span representations, and the incorporation of knowledge from external sources like large language models. These advancements are crucial for various applications, including improved named entity recognition, hate speech mitigation, and more nuanced understanding of complex textual relationships.