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Spatially Aware Term Selection for Geotagging
Jan. 2014 (vol. 26 no. 1)
pp. 221-234
Olivier Van Laere, Ghent University, Belgium
Jonathan Quinn, Cardiff University, UK
Steven Schockaert, Cardiff University, UK
Bart Dhoedt, Ghent University, Belgium
The task of assigning geographic coordinates to textual resources plays an increasingly central role in geographic information retrieval. The ability to select those terms from a given collection that are most indicative of geographic location is of key importance in successfully addressing this task. However, this process of selecting spatially relevant terms is at present not well understood, and the majority of current systems are based on standard term selection techniques, such as $(\chi^2)$ or information gain, and thus fail to exploit the spatial nature of the domain. In this paper, we propose two classes of term selection techniques based on standard geostatistical methods. First, to implement the idea of spatial smoothing of term occurrences, we investigate the use of kernel density estimation (KDE) to model each term as a two-dimensional probability distribution over the surface of the Earth. The second class of term selection methods we consider is based on Ripley's K statistic, which measures the deviation of a point set from spatial homogeneity. We provide experimental results which compare these classes of methods against existing baseline techniques on the tasks of assigning coordinates to Flickr photos and to Wikipedia articles, revealing marked improvements in cases where only a relatively small number of terms can be selected.
Index Terms:
Standards,Encyclopedias,Electronic publishing,Internet,Estimation,Context,feature extraction,Information search and retrieval,knowledge management,artificial intelligence,text mining,metadata,geographic information retrieval,classification,semi-structured data
Citation:
Olivier Van Laere, Jonathan Quinn, Steven Schockaert, Bart Dhoedt, "Spatially Aware Term Selection for Geotagging," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 221-234, Jan. 2014, doi:10.1109/TKDE.2013.42
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