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This paper is concerned with nearest neighbor search in distributionalsemantic models. A normal nearest neighbor search only returns a ranked list ofneighbors, with no information about the structure or topology of the localneighborhood. This is a potentially serious shortcoming of the mode of queryinga distributional semantic model, since a ranked list of neighbors may conflateseveral different senses. We argue that the topology of neighborhoods insemantic space provides important information about the different senses ofterms, and that such topological structures can be used for word-senseinduction. We also argue that the topology of the neighborhoods in semanticspace can be used to determine the semantic horizon of a point, which we defineas the set of neighbors that have a direct connection to the point. Weintroduce relative neighborhood graphs as method to uncover the topologicalproperties of neighborhoods in semantic models. We also provide examples ofrelative neighborhood graphs for three well-known semantic models; the PMImodel, the GloVe model, and the skipgram model.

Navigating the Semantic Horizon using Relative Neighborhood Graphs