Cross-lingual geo-parsing for non-structured data

Abstract

A geo-parser automatically identifies location words in a text. We have generated a geo-parser specifically to find locations in unstructured Spanish text. Our novel geo-parser architecture combines the results of four parsers: a lexico-semantic Named Location Parser, a rules-based building parser, a rules-based street parser, and a trained Named Entity Parser. Each parser has different strengths: the Named Location Parser is strong in recall, and the Named Entity Parser is strong in precision, and building and street parser finds buildings and streets that the others are not designed to do. To test our Spanish geo-parser performance, we compared the output of Spanish text through our Spanish geo-parser, with that same Spanish text translated into English and run through our English geo-parser. The results were that the Spanish geo-parser identified toponyms with an F1 of .796, and the English geo-parser identified toponyms with an F1 of .861 (and this is despite errors introduced by translation from Spanish to English), compared to an F1 of .114 from a commercial off-the-shelf Spanish geo-parser. Results suggest (1) geo-parsers should be built specifically for unstructured text, as have our Spanish and English geo-parsers, and (2) location entities in Spanish that have been machine translated to English are robust to geo-parsing in English.