Hybrid QA™ : Question Answering

Lymba’s Hybrid QA™ uses automatically generated ontologies to process complex text (such as compliance policy documents) integrated with an internal or external chatbot to process answers to complex and specific questions.

Real Lymba Example

A Major Global Banks’s compliance managers were flooded with emails regarding basic employee investment compliance questions. They needed a question answering system that would answer employee investment questions using existing company policies.

Employee questions were not always explicitly addressed by company policies, as some nuance exists. Exact matches connecting to FAQs, while useful, were not comprehensive.

The Lymba Solution

We converted documents via semantics to RDF, stored them to an RDF Triple Store, and integrated Lymba’s Hybrid QA™ into a Chatbot to retrieve answers. Users were able to ask questions such as “Does TPC approval expire? If I obtained TPC approval yesterday, can I execute the trade today?” Hybrid QA™ outperformed Lucene keyword matching and Deep Learning approaches in both accuracy and response time. Lymba increased chatbot performance from 74% to 91%.