  Syntactic dependencies can be predicted with high accuracy, and are useful
for both machine-learned and pattern-based information extraction tasks.
However, their utility can be improved. These syntactic dependencies are
designed to accurately reflect syntactic relations, and they do not make
semantic relations explicit. Therefore, these representations lack many
explicit connections between content words, that would be useful for downstream
applications. Proposals like English Enhanced UD improve the situation by
extending universal dependency trees with additional explicit arcs. However,
they are not available to Python users, and are also limited in coverage. We
introduce a broad-coverage, data-driven and linguistically sound set of
transformations, that makes event-structure and many lexical relations
explicit. We present pyBART, an easy-to-use open-source Python library for
converting English UD trees either to Enhanced UD graphs or to our
representation. The library can work as a standalone package or be integrated
within a spaCy NLP pipeline. When evaluated in a pattern-based relation
extraction scenario, our representation results in higher extraction scores
than Enhanced UD, while requiring fewer patterns.
