  We present a novel framework to deal with relation extraction tasks in cases
where there is complete lack of supervision, either in the form of gold
annotations, or relations from a knowledge base. Our approach leverages
syntactic parsing and pre-trained word embeddings to extract few but precise
relations,which are then used to annotate a larger cor-pus, in a manner
identical to distant supervision. The resulting data set is employed to fine
tune a pre-trained BERT model in order to perform relation extraction.
Empirical evaluation on four data sets from the biomedical domain shows that
our method significantly outperforms two simple baselines for unsupervised
relation extraction and, even if not using any supervision at all, achieves
slightly worse results than the state-of-the-art in three out of four data
sets. Importantly, we show that it is possible to successfully fine tune a
large pre-trained language model with noisy data, as op-posed to previous works
that rely on gold data for fine tuning.
