  Relation extraction (RE) is a sub-discipline of information extraction (IE)
which focuses on the prediction of a relational predicate from a
natural-language input unit (such as a sentence, a clause, or even a short
paragraph consisting of multiple sentences and/or clauses). Together with
named-entity recognition (NER) and disambiguation (NED), RE forms the basis for
many advanced IE tasks such as knowledge-base (KB) population and verification.
In this work, we explore how recent approaches for open information extraction
(OpenIE) may help to improve the task of RE by encoding structured information
about the sentences' principal units, such as subjects, objects, verbal
phrases, and adverbials, into various forms of vectorized (and hence
unstructured) representations of the sentences. Our main conjecture is that the
decomposition of long and possibly convoluted sentences into multiple smaller
clauses via OpenIE even helps to fine-tune context-sensitive language models
such as BERT (and its plethora of variants) for RE. Our experiments over two
annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy
of our enriched models compared to existing RE approaches. Our best results
reach 92% and 71% of F1 score for KnowledgeNet and FewRel, respectively,
proving the effectiveness of our approach on competitive benchmarks.
