Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Proceedings of the 18th international conference on World wide web
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Classification Based on Combination of Kernel Density Estimators
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Learning boundaries of vague places from noisy annotations
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
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Knowledge produced online often comes in the form of free-text labels, known as tags, with which users annotate the content they create, such as photos and videos. Increasingly, such content is also georeferenced, i.e., it is associated with geographic coordinates. The implicit relationships between tags and their locations can tell us much about how people conceptualize places and relations between them. However, extracting such knowledge from social annotations presents many challenges, since annotations are often ambiguous, noisy, uncertain and spatially inhomogeneous. We introduce a probabilistic framework for modeling georeferenced annotations and a method for learning model parameters from data. The framework is flexible and general, and can be used in a variety of applications that mine geospatial knowledge from user generated content. Specifically, we study two problems --- extracting place semantics and predicting locations of photos from tags --- and show that performance of our method is comparable to that of state-of-the-art approaches. Moreover, we show that combining the two problems leads to a better performance on the location prediction task than baseline.