Incorporating a semantic analysis into a document retrieval strategy
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
GIPSY: automated geographic indexing of text documents
Journal of the American Society for Information Science - Special issue: spatial information
Natural language information retrieval
TREC-2 Proceedings of the second conference on Text retrieval conference
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Geospatial mapping and navigation of the web
Proceedings of the 10th international conference on World Wide Web
Hybrid transformation for indexing and searching web documents in the cartographic paradigm
Information Systems - Special issue on the 1st web information systems engineering conference (WISE '00)
Information Retrieval
Focused Crawling Using Context Graphs
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
When and Why Are Visual Landmarks Used in Giving Directions?
COSIT 2001 Proceedings of the International Conference on Spatial Information Theory: Foundations of Geographic Information Science
The Nature of Landmarks for Real and Electronic Spaces
COSIT '99 Proceedings of the International Conference on Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science
Enriching Wayfinding Instructions with Local Landmarks
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
Toward tighter integration of web search with a geographic information system
Proceedings of the 15th international conference on World Wide Web
Automatic generation of tourist maps
ACM SIGGRAPH 2008 papers
An Influence Model for Reference Object Selection in Spatially Locative Phrases
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
Location approximation for local search services using natural language hints
International Journal of Geographical Information Science
Mapping geographic coverage of the web
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Mining a Multilingual Geographical Gazetteer from the Web
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Landmark classification for route directions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Crowd-based urban characterization: extracting crowd behavioral patterns in urban areas from Twitter
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Improving vertical geo/geo disambiguation by increasing geographical feature weights of places
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Model for landmark highlighting in mobile web services
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Geographic Information Retrieval and Text Mining on Chinese Tourism Web Pages
International Journal of Information Technology and Web Engineering
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Landmarks play crucial roles in human geographic knowledge. There has been much work focusing on the extraction of landmarks from geographic information systems (GIS) or 3D city models. The extraction of landmarks from digital documents, however, has not been fully explored. The World Wide Web provides a rich source of region related information based on our understanding of geographic space. Web mining enables a new mean of extracting landmarks, differently from conventional vision oriented methods. Our approach is based on how geographic objects are expressed by humans, instead of how they are observed. We extend existing methods of text mining so that spatial context is considered. The results of the experiments showed that adopting spatial context into text mining improves the precision of extracting landmarks from web documents.