are enriched with side information such as part of speech and named entity markup here we disclose a class of statistical methods called here u201c corpus clustering u201d which assign initial confidence values c n p that represent our degree of belief that text element name n refers to point or region p a technical advance is achieved in the art by exploiting knowledge of a hitherto unobserved statistical property of documents namely geo textual correlation by inspecting large corpora we have found that there is a high degree of spatial correlation in geographic references that are in textual proximity this applies not only to points that are nearby such as madison and milwaukee but also to geographic entities that enclose or are enclosed by regions madison and wisconsin for example more specifically if the textual distance between names n and m is small and if n has a reading p i e n is associated with p or n means p and m has a reading q then the physical distance between p and q is likely to be lower than would be expected randomly conversely if p and q are close geographically then their names n and m are more likely to appear together in texts than would be expected randomly this correlation between geographic and textual distance is considered in.