  We present an approach for recursively splitting and rephrasing complex
English sentences into a novel semantic hierarchy of simplified sentences, with
each of them presenting a more regular structure that may facilitate a wide
variety of artificial intelligence tasks, such as machine translation (MT) or
information extraction (IE). Using a set of hand-crafted transformation rules,
input sentences are recursively transformed into a two-layered hierarchical
representation in the form of core sentences and accompanying contexts that are
linked via rhetorical relations. In this way, the semantic relationship of the
decomposed constituents is preserved in the output, maintaining its
interpretability for downstream applications. Both a thorough manual analysis
and automatic evaluation across three datasets from two different domains
demonstrate that the proposed syntactic simplification approach outperforms the
state of the art in structural text simplification. Moreover, an extrinsic
evaluation shows that when applying our framework as a preprocessing step the
performance of state-of-the-art Open IE systems can be improved by up to 346%
in precision and 52% in recall. To enable reproducible research, all code is
provided online.
