Human-generated knowledge bases like Wikipedia have excellent precision but poor recall. Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text and describe what it learns in human-readable text.
Amy Heineike is the vice president of product engineering at Primer, where she leads teams to build machines that read and write text leveraging natural language processing (NLP), natural language generation (NLG_, and a host of other algorithms to augment human analysts. Previously, she built out technology for visualizing large document sets as network maps at Quid. A Cambridge mathematician who previously worked in London modeling cities, Amy is fascinated by complex human systems and the algorithms and data that help us understand them.
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