Taxonomy Learning

Taxonomy Learning is a task in the field of natural language processing that aims to automatically classify concepts hierarchically from a text corpus. This task primarily consists of two core steps: extracting hypernyms (superordinate terms) of concepts, and refining these relationships into a structured classification system. By constructing a hierarchical concept classification, Taxonomy Learning can enhance the efficiency and accuracy of information retrieval, knowledge management, and semantic understanding.