Phil Gooch – Scholarcy

CEO

Phil Gooch – Scholarcy

Phil Gooch starting building intelligent text processing solutions when he accidentally created one of the first fully automated book typesetting systems in Microsoft Word for Routledge. He’s been developing content enrichment and automated knowledge extraction solutions for the publishing, education, legal and healthcare sectors ever since. Phil has a PhD in Health Informatics from City University, London, and prior to founding Scholarcy, Phil led the development of NLP solutions for Babylon Health.

AI is often used to suggest content based on various similarity measures. But this content still needs to be read and understood. We discuss how AI can be used to summarise and validate content for reviewers, and create explanations for new readers

Session Full Description
Improving content discovery in the scholarly publishing sector has been a hot topic over the past few years, and as a result there have been an explosion of new services that aim to ‘solve’ discovery. But the fact that much content is never read or cited is not just a discovery problem – it’s an information understanding problem. If your chosen discovery service recommends 20-30 hot new papers each week, you still need to make time to read them. Skim reading has become the norm now, but when you skim read you don’t really retain the information. And if you are new to a field, you don’t know what’s important and what you can skip. Fortunately, the latest developments in machine learning and artificial intelligence can be brought to bear to transform linear, textual content into a non-linear, interactive and visual experience that aids knowledge retention and learning. In this session, we’ll provide an overview of the techniques used, and how they can be combined and deployed to take content enrichment and transformation to the next level. If you want to see how AI can go beyond simple keyword extraction and topic categorisation, then this session is for you.

Actionable Takeaway #1
Understand how machine-learning techniques can be used to extract key facts and findings from content across most domains

Actionable Takeaway #2
Appreciate how AI can be deployed to validate knowledge statements and assertions

Actionable Takeaway #3
Gain insights into how these techniques can be used to drive improved discovery, and to create new products and services