Geo-tagging for imprecise regions of different sizes
Proceedings of the 4th ACM workshop on Geographical information retrieval
Robust location search from text queries
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
STEWARD: architecture of a spatio-textual search engine
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Determining geographic representations for arbitrary concepts at query time
Proceedings of the first international workshop on Location and the web
Analysis of geographic queries in a search engine log
Proceedings of the first international workshop on Location and the web
Using co-occurrence models for placename disambiguation
International Journal of Geographical Information Science
A Toponym Resolution Service Following the OGC WPS Standard
W2GIS '08 Proceedings of the 8th International Symposium on Web and Wireless Geographical Information Systems
Extracting geographic features from the Internet to automatically build detailed regional gazetteers
International Journal of Geographical Information Science
UNN-WePS: web person search using co-present names and lexical Chains
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Towards heterogeneous resources-based ambiguity reduction of sub-typed geographic named entities
GeoS'11 Proceedings of the 4th international conference on GeoSpatial semantics
Semantic extraction of geographic data from web tables for big data integration
Proceedings of the 7th Workshop on Geographic Information Retrieval
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Named entity tagging comprises the sub-tasks of identifying a text span and classifying it, but this view ignores the relationship between the entities and the world. Spatial and temporal entities ground events in space-time, and this relationship is vital for applications such as question answering and event tracking. There is much recent work regarding the temporal dimension (Setzer and Gaizauskas 2002, Mani and Wilson 2000), but no detailed study of the spatial dimension.I propose to investigate how spatial named entities (which are often referentially ambiguous) can be automatically resolved with respect to an extensional coordinate model (toponym resolution). To this end, various information sources including linguistic cue patterns, co-occurrence information, discourse/positional information, world knowledge (such as size and population) as well as minimality heuristics (Leidner et al. 2003) will be combined in a supervised machine learning regime.The major contributions of this research project will be a corpus of text manually annotated for spatial named entities with their model correlates as a training and evaluation resource, a novel method to spatially ground toponyms in text and a component-based evaluation based on this new reference corpus.