Robust Toponym Resolution Based on Surface Statistics

Tomohisa SANO
Shiho Hoshi NOBESAWA
Hiroyuki OKAMOTO
Hiroaki SAITO

IEICE TRANSACTIONS on Information and Systems   Vol.E92-D    No.12    pp.2313-2320
Publication Date: 2009/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.2313
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Natural Language Processing and its Applications)
Category: Unknown Word Processing
natural language processing,  toponym resolution,  area identification,  statistical information,  

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Toponyms and other named entities are main issues in unknown word processing problem. Our purpose is to salvage unknown toponyms, not only for avoiding noises but also providing them information of area candidates to where they may belong. Most of previous toponym resolution methods were targeting disambiguation among area candidates, which is caused by the multiple existence of a toponym. These approaches were mostly based on gazetteers and contexts. When it comes to the documents which may contain toponyms worldwide, like newspaper articles, toponym resolution is not just an ambiguity resolution, but an area candidate selection from all the areas on Earth. Thus we propose an automatic toponym resolution method which enables to identify its area candidates based only on their surface statistics, in place of dictionary-lookup approaches. Our method combines two modules, area candidate reduction and area candidate examination which uses block-unit data, to obtain high accuracy without reducing recall rate. Our empirical result showed 85.54% precision rate, 91.92% recall rate and .89 F-measure value on average. This method is a flexible and robust approach for toponym resolution targeting unrestricted number of areas.