  We propose a lightly-supervised approach for information extraction, in
particular named entity classification, which combines the benefits of
traditional bootstrapping, i.e., use of limited annotations and
interpretability of extraction patterns, with the robust learning approaches
proposed in representation learning. Our algorithm iteratively learns custom
embeddings for both the multi-word entities to be extracted and the patterns
that match them from a few example entities per category. We demonstrate that
this representation-based approach outperforms three other state-of-the-art
bootstrapping approaches on two datasets: CoNLL-2003 and OntoNotes.
Additionally, using these embeddings, our approach outputs a
globally-interpretable model consisting of a decision list, by ranking patterns
based on their proximity to the average entity embedding in a given class. We
show that this interpretable model performs close to our complete bootstrapping
model, proving that representation learning can be used to produce
interpretable models with small loss in performance.
