Natural language processing (NLP) is a common catch-all bucket for the world of natural language algorithms. As adoption of NLP technology continues to grow, it’s critical that builders of NLP applications understand the range of algorithms required to turn text into value.
Amy Heineike explains how she orchestrates natural language processing, understanding, and generation algorithms to build text-based AI applications for Fortune 500 companies. You’ll explore a variety of cutting-edge natural language processing, understanding, and generation algorithms and research, from text parsing, entity extraction, and organization extraction to sentiment analysis, event detection, and summarization, and learn how these new and old techniques are being used for AI applications. You’ll leave with a holistic understanding of different algorithms that power NLP applications and a better sense of what algorithms are best for the right job.
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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