Using NLP, machine learning & deep learning algorithms to extract meaning from text

This talk covers the three main tasks required to build a system that can extract semantically meaningful facts from free text documents – like clinical notes, patent applications, research papers on customer service emails. Current systems can go beyond the traditional search and keyword matching capabilities, to enable much deeper inference at scale, and this talk focuses on algorithms and open source libraries that may that possible.

The talk walks through building a natural language annotations pipeline with domain-specific annotators, training machine learning models and applying those as annotators, and using deep learning to automatically expand and update taxonomies. Source code will be available online after the talk to enable you to learn and experiment further.

David Talby is a chief technology officer at John Snow Labs, helping healthcare & life science companies put AI to good use. David is the creator of Spark NLP – the world’s most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft’s Bing in the US and Europe, and to scale Amazon’s financial systems in Seattle and the UK. David holds a PhD in computer science and master’s degrees in both computer science and business administration.