  For over thirty years, researchers have developed and analyzed methods for
latent tree induction as an approach for unsupervised syntactic parsing.
Nonetheless, modern systems still do not perform well enough compared to their
supervised counterparts to have any practical use as structural annotation of
text. In this work, we present a technique that uses distant supervision in the
form of span constraints (i.e. phrase bracketing) to improve performance in
unsupervised constituency parsing. Using a relatively small number of span
constraints we can substantially improve the output from DIORA, an already
competitive unsupervised parsing system. Compared with full parse tree
annotation, span constraints can be acquired with minimal effort, such as with
a lexicon derived from Wikipedia, to find exact text matches. Our experiments
show span constraints based on entities improves constituency parsing on
English WSJ Penn Treebank by more than 5 F1. Furthermore, our method extends to
any domain where span constraints are easily attainable, and as a case study we
demonstrate its effectiveness by parsing biomedical text from the CRAFT
dataset.
