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

Autor:	Yi Li, Nicola Stokes, Jiawen Rong, Alistair Moffat
Rok vydání:	2008
Předmět:	Ambiguity resolution Computer science business.industry Geography Planning and Development Library and Information Sciences Resolution (logic) computer.software_genre Weighting Geographic information retrieval Empirical research Synonym (database) Information system Artificial intelligence Data mining business computer Natural language processing Information Systems
Zdroj:	International Journal of Geographical Information Science. 22:247-264
ISSN:	1362-3087 1365-8816
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b0ccd8bbe3f9bf1019dbb4f9ff13edaa https://doi.org/10.1080/13658810701626210 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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