Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Geo-tagging for imprecise regions of different sizes
Proceedings of the 4th ACM workshop on Geographical information retrieval
Modelling vague places with knowledge from the Web
International Journal of Geographical Information Science - Digital Gazetteer Research
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Proceedings of the 18th international conference on World wide web
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Uncovering locally characterizing regions within geotagged data
Proceedings of the 22nd international conference on World Wide Web
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What ordinary people mean by places may differ dramatically from what experts consider them to be. This is especially evident in how people talk about places in social media, where 'Los Angeles', for instance, could include areas well outside of the city or even in another county. In order to make best use of the information in social media, we need to understand what people mean when they refer to a place. Social annotations provide valuable evidence for harvesting knowledge about places, e.g., learning their boundaries and relations to other places. However, social annotations are noisy, and this can dramatically distort the learned boundaries. In this paper we propose a method that exploits the distinctive property of social annotations --- that it is created by many people --- to filter out noise. Using a large data set extracted from Flickr we show that our crowd-based noise filtering method can learn accurate boundaries of places, including vague places.