Bootstrapping toponym classifiers
David A. Smith, Gideon S. Mann · 2003
We present minimally supervised methods for training and testing geographic name disambiguation (GND) systems.We train data-driven place name classifiers using toponyms already disambiguated in the training text -by such existing cues as "Nashville, Tenn." or "Springfield, MA" -and test the system on texts where these cues have been stripped out and on hand-tagged historical texts.We experiment on three English-language corpora of varying provenance and complexity: newsfeed from the 1990s, personal narratives from the 19th century American west, and memoirs and records of the U.S. Civil War.Disambiguation accuracy ranges from 87% for news to 69% for some historical collections.