2007
DOI: 10.1007/978-3-540-74999-8_127
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The University of Lisbon at GeoCLEF 2006

Abstract: This paper reports the participation of the XLDB team from the University of Lisbon at the 2008 GeoCLEF task. We focused on developing a better text annotation tool for geo-parsing the documents, handling both explicit geographic evidence (as given by placenames) and implicit geographic evidence (as given by monuments, for example). The query processing and geographic ranking approaches were redesigned to handle thematic and geographic criteria of each search in a non-segregation way. We detail the GIR system,… Show more

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Cited by 14 publications
(19 citation statements)
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“…Previous research in geographic information retrieval has addressed problems such as the recognition and disambiguation of place references given over text [10,15], the assignment of documents to encompassing geographic scopes [1], or the retrieval of documents considering geographic relevance [16,21,6]. The first two problems are normally seen as necessary pre-processing tasks, so that later one can use ranking formulas that leverage on the similarity between the geographic scopes of documents and of user queries.…”
Section: Related Workmentioning
confidence: 99%
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“…Previous research in geographic information retrieval has addressed problems such as the recognition and disambiguation of place references given over text [10,15], the assignment of documents to encompassing geographic scopes [1], or the retrieval of documents considering geographic relevance [16,21,6]. The first two problems are normally seen as necessary pre-processing tasks, so that later one can use ranking formulas that leverage on the similarity between the geographic scopes of documents and of user queries.…”
Section: Related Workmentioning
confidence: 99%
“…Many research and evaluation issues surrounding geographic mono-and bilingual search have been addressed in Geo-CLEF. The most common approaches are based on heuristic combinations of the standard IR metrics used in text retrieval (e.g., TF/IDF), with similarity metrics for geographic scopes based on distance and/or containment [16]. Frontiera et al compared different geographic similarity methods based on region overlaps [3].…”
Section: Related Workmentioning
confidence: 99%
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“…Several approaches were focused on solving the ranking problem during these years. Common employed strategies are: a) query expansion through feedback relevance [6], [9], [10]; b) re-ranking retrieved elements through adapted similarity measures [7]; and c) re-ranking through information fusion techniques [9], [10], [11].…”
mentioning
confidence: 99%
“…This leads to the fact that the amount of geographical information included in a general ontology is usually very small, which limits it as an effective geographical resource. Some other approaches that focus on the re-ranking problem propose algorithms that consider the existence of Geo-tags 4 ; therefore, the ranking function measures levels of topological space proximity, or geographical closeness among the geo-tags of retrieved documents and geo-queries [7]. In order to achieve this, geographical resources are needed.…”
mentioning
confidence: 99%