  Relation extraction (RE) has achieved remarkable progress with the help of
pre-trained language models. However, existing RE models are usually incapable
of handling two situations: implicit expressions and long-tail relation
classes, caused by language complexity and data sparsity. Further, these
approaches and models are largely inaccessible to users who don't have direct
access to large language models (LLMs) and/or infrastructure for supervised
training or fine-tuning. Rule-based systems also struggle with implicit
expressions. Apart from this, Real world financial documents such as various
10-X reports (including 10-K, 10-Q, etc.) of publicly traded companies pose
another challenge to rule-based systems in terms of longer and complex
sentences. In this paper, we introduce a simple approach that consults training
relations at test time through a nearest-neighbor search over dense vectors of
lexico-syntactic patterns and provides a simple yet effective means to tackle
the above issues. We evaluate our approach on REFinD and show that our method
achieves state-of-the-art performance. We further show that it can provide a
good start for human in the loop setup when a small number of annotations are
available and it is also beneficial when domain experts can provide high
quality patterns.
