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For the purposes of computational dialectology or other geographically boundtext analysis tasks, texts must be annotated with their or their authors'location. Many texts are locatable through explicit labels but most have noexplicit annotation of place. This paper describes a series of experiments todetermine how positionally annotated microblog posts can be used to learnlocation-indicating words which then can be used to locate blog texts and theirauthors. A Gaussian distribution is used to model the locational qualities ofwords. We introduce the notion of placeness to describe how locational wordsare.  We find that modelling word distributions to account for several locationsand thus several Gaussian distributions per word, defining a filter which picksout words with high placeness based on their local distributional context, andaggregating locational information in a centroid for each text gives the mostuseful results. The results are applied to data in the Swedish language.

Inferring the location of authors from words in their texts