An empirical study of the effects of NLP components on Geographic IR performance

Natural Language Processing (NLP) techniques, such as toponym detection and resolution, are an integral part of most Geographic Information Retrieval (GIR) architectures. Without these components, synonym detection, ambiguity resolution and accurate toponym expansion would not be possible. However, there are many important factors affecting the success of an NLP approach to GIR, including toponym detection errors, toponym resolution errors, and query overloading. The aim of this paper is to determine how severe these errors are in state-of-the-art systems, and to what extent they affect GIR performance. We show that a careful choice of weighting schemes in the IR engine can minimize the negative impact of these errors on GIR accuracy. We provide empirical evidence from the GeoCLEF 2005 and 2006 datasets to support our observations.