  Unsupervised relation extraction (URE) extracts relations between named
entities from raw text without manually-labelled data and existing knowledge
bases (KBs). URE methods can be categorised into generative and discriminative
approaches, which rely either on hand-crafted features or surface form.
However, we demonstrate that by using only named entities to induce relation
types, we can outperform existing methods on two popular datasets. We conduct a
comparison and evaluation of our findings with other URE techniques, to
ascertain the important features in URE. We conclude that entity types provide
a strong inductive bias for URE.
