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In this paper, we introduce several vector space manipulation methods that are applied to trained vector space models in a post-hoc fashion, and present an application of these techniques in semantic role labeling for Finnish and English. Specifically, we show that the vectors can be circularly shifted to encode syntactic information and subsequently averaged to produce representations of predicate senses and arguments. Further, we show that it is possible to effectively learn a linear transformation between the vector representations of predicates and their arguments, within the same vector space.

Post-hoc Manipulations of Vector Space Models with Application to Semantic Role Labeling