Usually unsupervised dependency parsers try to optimize the probability of a corpus by revising the dependency model that is assumed to have generated the corpus. In this paper we explore a different view in which a dependency structure is, among other things, a partial order on the nodes in terms of centrality or saliency. Under this assumption we directly model centrality and derive dependency trees from the ordering of words. The result is an approach to unsupervised dependency parsing that is very different from standard ones in that it requires no training data. The input words are ordered by centrality, and a parse is derived from the ranking using a simple deterministic parsing algorithm, relying on the universal dependency rules defined by Naseem et al. (Naseem, T., Chen, H., Barzilay, R., Johnson, M. 2010. Using universal linguistic knowledge to guide grammar induction. In Proceedings of Empirical Methods in Natural Language Processing, Boston, MA, USA, pp. 1234–44.). Our approach is evaluated on data from twelve different languages and is remarkably competitive.
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