Abstract
Geographic information retrieval (GIR) is a new research area that aims at the retrieval of geographic-related documents based not only on keyword relevance but also on geographic relationships between the query and the geographic information in texts. It is natural for people to want information related to just their surroundings. Conventional GIR systems, however, have relatively poor granularity, such as city or province, because they use geographic information in restricted ways -- mostly just for filtering. To address this problem, we propose a geographic scoring method that considers extent implied by each geographic names appeared in texts to emphasize geographic names that focus specific areas, rather than broad geographic names. Furthermore, to improve robustness against errors in pre-processing such as geo-parsing and geo-coding, we also propose a noise elimination method based on clustering. Evaluation is conducted using standard TREC-style evaluation metrics including MAP, R-precision, and so on. The results show that our method outperforms two baseline approaches: full-text search and using the nearest point in the text.
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