Structured toponym resolution using combined hierarchical place categories

Abstract

Determining geographic interpretations for place names, or toponyms, involves resolving multiple types of ambiguity. Place names commonly occur within lists and data tables, whose authors frequently omit qualifications (such as city or state containers) for place names because they expect the meaning of individual place names to be obvious from context. We present a novel technique for place name disambiguation (also known as toponym resolution) that uses Bayesian inference to assign categories to lists or tables containing place names, and then interprets individual toponyms based on the most likely category assignments. The categories are defined as nodes in hierarchies along three orthogonal dimensions: place types (e.g., cities, capitals, rivers, etc.), geographic containers, and prominence (e.g., based on population).