Perhaps a more interesting question, and one that the current paper aims to address, is to what extent disambiguation can also provide benefits for tensor-based approaches, which in general constitute more powerful models for natural language (see discussion in Section 2).
this paper aims to: (a) propose disambiguation algorithms for a number of tensorbased distributional models; (b) examine the effect of disambiguation on tensors for relational words; and (c) meaningfully compare the effectiveness of tensor-based against vector mixture models in a number of tasks.