  We introduce a hybrid human-automated system that provides scalable
entity-risk relation extractions across large data sets. Given an
expert-defined keyword taxonomy, entities, and data sources, the system returns
text extractions based on bidirectional token distances between entities and
keywords and expands taxonomy coverage with word vector encodings. Our system
represents a more simplified architecture compared to alerting focused systems
- motivated by high coverage use cases in the risk mining space such as due
diligence activities and intelligence gathering. We provide an overview of the
system and expert evaluations for a range of token distances. We demonstrate
that single and multi-sentence distance groups significantly outperform
baseline extractions with shorter, single sentences being preferred by
analysts. As the taxonomy expands, the amount of relevant information increases
and multi-sentence extractions become more preferred, but this is tempered
against entity-risk relations become more indirect. We discuss the implications
of these observations on users, management of ambiguity and taxonomy expansion,
and future system modifications.
