Natural Language Processing Explained

Co-authored by a NLP dream team: Stella Liu (AI Product Manager), David Kearns (Product Manager in Data and AI), Nadine Handal (Data Scientist), Shubham Agarwal (AI Research Scientist), Michael Flores (AI Architect) and me (Machine Learning Developer)

Introduction

While AI has sometimes become synonymous with chatbots like Siri and Facebook Discovery bots, this is only scratching the surface of what the underlying technology — natural language processing (NLP) — can do for your product or your organization.

Google “natural language processing” though and you’ll get high level messaging on how ubiquitous it is or deep technical details on its implementation — co-reference resolution, neural network dependency parser and entity recognizer… anyone?

We (a data scientist, NLP consultant, NLP research scientist and AI product manager with a collective 50+ years of experience in this technology) decided that this is not helpful for people to grasp the potential of this technology and understand how to get started.

We collectively wrote this article for you — a tech-savvy but non-technical reader — to get the inside scoop on what this technology is, common success patterns that the team has seen that work in real-world situations, questions you can ask yourself to vet its…