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We introduce a hybrid human-automated system that provides scalableentity-risk relation extractions across large data sets. Given anexpert-defined keyword taxonomy, entities, and data sources, the system returnstext extractions based on bidirectional token distances between entities andkeywords and expands taxonomy coverage with word vector encodings. Our systemrepresents a more simplified architecture compared to alerting focused systems- motivated by high coverage use cases in the risk mining space such as duediligence activities and intelligence gathering. We provide an overview of thesystem and expert evaluations for a range of token distances. We demonstratethat single and multi-sentence distance groups significantly outperformbaseline extractions with shorter, single sentences being preferred byanalysts. As the taxonomy expands, the amount of relevant information increasesand multi-sentence extractions become more preferred, but this is temperedagainst entity-risk relations become more indirect. We discuss the implicationsof these observations on users, management of ambiguity and taxonomy expansion,and future system modifications.

A Consolidated System for Robust Multi-Document Entity Risk Extraction and Taxonomy Augmentation