Natural languages (NL) can be classified as prepositional or postpositional based on the order of the noun phrase and the adposition. Categorizing a language by its adposition typology helps in addressing several challenges in linguistics and natural language processing (NLP). Understanding the adposition typologies for less-studied languages by manual analysis of large text corpora can be quite expensive, yet automatic discovery of the same has received very little attention till date. This research presents a simple unsupervised technique to automatically predict the adposition typology for a language. Most of the function words of a language are adpositions, and we show that function words can be effectively separated from content words by leveraging differences in their distributional properties in a corpus. Using this principle, we show that languages can be classified as prepositional or postpositional based on the rank correlations derived from entropies of word co-occurrence distributions. Our claims are substantiated through experiments on 23 languages from ten diverse families, 19 of which are correctly classified by our technique.
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