Finch for Text® Features Now Include Relationship Extraction and Relationship Tagging
Reston, VA – Finch Computing, developers of the innovative natural language processing solution Finch for Text® today announced two new product features: relationship extraction and relationship tagging.
“Relationship extraction has proven to be immensely valuable across a number of domains and use cases – from diligence to competitive intelligence to supply chain risk management and more,” Finch Computing Chief Technology Officer Scott Lightner said. “We look forward to bringing this expanded capability within Finch for Text® to customers.”
Relationship Extraction refers to the ability to discern how two entities are connected to one another. For companies, this can mean understanding supplier-customer, parent-subsidiary, or acquirer-acquired relationships. Understanding these connections can help organizations examine many types of risk or opportunity. Doing so in real-time and on huge volumes of text accelerates their ability to identify and assess these risks and opportunities, and then to make critical business decisions as a result.
Finch for Text®, leveraging its superior and highly accurate entity extraction and disambiguation capabilities, goes beyond keyword search and deploys proprietary deep transfer learning and artificial intelligence to extract relationships and then tag them for users. Additionally, with this approach Finch for Text® can discover the status of an extracted relationship – whether it is current, emerging or ending.
Users receive information about the relationship as well as supporting “evidence.” Finch for Text® provides the exact sentence from which the relationship was inferred and, via XML, the subject, predicate, and object.
For companies, Finch for Text can extract and identify current, potential, and past (ended) relationships including:
To learn more about Finch for Text®, including its relationship extraction feature, or to request a free, 14-day trial, please visit finchai.com