CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
An exploration of proximity measures in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
STEWARD: architecture of a spatio-textual search engine
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
International Journal of Geographical Information Science
Introduction to Information Retrieval
Introduction to Information Retrieval
Answering general time sensitive queries
Proceedings of the 17th ACM conference on Information and knowledge management
Term proximity scoring for keyword-based retrieval systems
ECIR'03 Proceedings of the 25th European conference on IR research
Use of temporal expressions in web search
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
The crucial role of semantic discovery and markup in geo-temporal search
ESAIR '10 Proceedings of the third workshop on Exploiting semantic annotations in information retrieval
A language modeling approach for temporal information needs
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Event-centric search and exploration in document collections
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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Temporal and geographic information needs are frequent and important but not well served by standard IR systems. Recent approaches address such needs by extracting and normalizing temporal and geographic expressions from documents. They calculate specific scores for the temporal and/ or geographic parts of a query. However, all approaches assume independence between the different query parts. In this paper, we present a new model to rank documents according to combined textual, temporal, and geographic queries. The independence assumption between the query parts is eliminated by calculating proximity scores. Thus, documents are regarded to be more relevant if terms and expressions satisfying the different query parts occur close to each other in a document. As our evaluations based on the NTCIR-GeoTime data show, our proposed model outperforms baseline models that do not use proximity information.