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.

本研究提出了一种使用远程监督形式的跨度约束技术来提高无监督语法分析性能的方法。通过少量跨度约束，可以大大提高无监督分析系统 DIORA 的性能。我们的实验表明，基于实体的跨度约束可以使英语 WSJ Penn Treebank 的组成分析提高超过 5 F1，并将其扩展到任何易于实现跨度约束的领域，同时在生物医学文本方面，该方法的有效性得到了证明。