A Consolidated System for Robust Multi-Document Entity Risk Extraction and Taxonomy Augmentation
This work addresses scalable entity-risk extraction for due diligence and intelligence gathering, representing an incremental improvement with a simplified architecture.
The paper tackles the problem of extracting entity-risk relations from large datasets by introducing a hybrid human-automated system that uses bidirectional token distances and word vector encodings, demonstrating that single and multi-sentence distance groups significantly outperform baseline extractions with analysts preferring shorter, single sentences.
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.