👉 Span computing is a fundamental concept in natural language processing (NLP) that involves identifying and extracting contiguous sequences of words or tokens from a given text, often referred to as spans. These spans can represent various linguistic elements such as named entities (e.g., people, organizations, locations), phrases, or even specific parts of speech. The process typically involves training machine learning models on annotated datasets to recognize patterns and boundaries that indicate where one span ends and another begins. This technique is crucial for tasks like information extraction, question answering, and machine translation, where understanding the precise context and structure of text is essential for accurate processing and interpretation.
