  The advent of neural-networks in NLP brought with it substantial improvements
in supervised relation extraction. However, obtaining a sufficient quantity of
training data remains a key challenge. In this work we propose a process for
bootstrapping training datasets which can be performed quickly by
non-NLP-experts. We take advantage of search engines over syntactic-graphs
(Such as Shlain et al. (2020)) which expose a friendly by-example syntax. We
use these to obtain positive examples by searching for sentences that are
syntactically similar to user input examples. We apply this technique to
relations from TACRED and DocRED and show that the resulting models are
competitive with models trained on manually annotated data and on data obtained
from distant supervision. The models also outperform models trained using NLG
data augmentation techniques. Extending the search-based approach with the NLG
method further improves the results.
